Tesi sul tema "Energy consumption model"

This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.

This paper examines the long-term relationship between primary energy consumption and other key macroeconomic variables, including real GDP, labour force, capital stock and technology, using a panel dataset for 64 countries over the period 1965-2009. Deploying panel error correction models, we find that there is a positive relationship running from physical capital, GDP, and population to primary energy consumption. We observe however a negative relationship between total factor productivity and primary energy usage. Significant differences arise in the magnitude of the cointegration coefficients, when we allow for differences in geopolitics and wealth levels. We also argue that inference on the basis of a single model without taking model uncertainty into account can lead to biased conclusions. Consequently, we address this problem by applying simple model averaging techniques to the estimated panel cointegration models. We find that tackling the uncertainty associated with selecting a single model with model averaging techniques leads to a more accurate representation of the link between energy consumption and the other macroeconomic variables, and to a significantly increased out-of-sample forecast performance. (authors' abstract)
Series: Department of Economics Working Paper Series

The importance of energy efficiency has grown alongside awareness of climate change due to the rapid increase of greenhouse gases. With the increasing trend regarding mobile subscribers, it is necessary to prevent an expansion of energy consumption via mobile networks. In this thesis, the energy optimization of the new radio access technology called 5G NR utilizing different sleep states to put base stations to sleep when they are not transmitting data is discussed. Energy savings and file latency with heterogeneous and super dense urban scenarios was evaluated through simulations with different network deployments. An updated power model has been proposed and the sensitivity of the new power model was analyzed by adjusting wake-up time and sleep factors. This showed that careful implementation is necessary when adjusting these parameter settings, although in most cases it did not change the end results by much. Since 5G NR has more potential in energy optimization compared to the previous generation mobile network 4G LTE, up to 4 sleep states was implemented on the NR base stations and one idle mode on LTE base stations. To mitigate unnecessary sleep, deactivation timers are used which decides when to put base stations to sleep. Without deactivation timers, the delay could increase significantly, while with deactivation timers the delay increase would only be a few percent. Up to 42.5% energy could be saved with LTE-NR non-standalone deployment and 72.7% energy with NR standalone deployment compared to LTE standalone deployment, while minimally impacting the delay on file by 1%.

Thesis (M.S.)--West Virginia University, 2005.
Title from document title page. Document formatted into pages; contains ix, 71 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 67-71).

In this paper we use the long-term empirical relationship among primary energy consumption, real income, physical capital, population and technology, obtained by averaged panel error correction models, to project the long-term primary energy consumption of 56 countries up to 2100. In forecasting long-term primary energy consumption, we work with four different Shared Socioeconomic Pathway Scenarios (SSPs) developed for the Intergovernmental Panel on Climate Change (IPCC) framework, assuming different challenges to adaptation and mitigation. We find that in all scenarios, China, the United States and India will be the largest energy consumers, while highly growing countries will also significantly contribute to energy use. We observe for most scenarios a sharp increase in global energy consumption, followed by a levelling-out and a decrease towards the second half of the century. The reasons behind this pattern are not only slower population growth, but also infrastructure saturation and increased total factor productivity. This means, as countries move towards more knowledge based societies, and higher energy efficiency, their primary energy usage is likely to decrease as a result. Global primary energy consumption is expected however to increase significantly in the coming decades, thus increasing the pressure on policy makers to cope with the questions of energy security and greenhouse gas mitigation at the same time. (authors' abstract)
Series: Department of Economics Working Paper Series

The automotive industry is facing some of the most difficult design challenges in industry history. Developing innovative methods to reduce fossil fuel dependence is imperative for maintaining compliance with government regulations and consumer demand. In addition to powertrain design, route selection contributes to vehicle environmental impact. The objective of this thesis is to develop a methodology for evaluating the energy consumption of each route option for a specific vehicle. A 'backwards' energy tracking method determines tractive demand at the wheels from route requirements and vehicle characteristics. Next, this method tracks energy quantities at each powertrain component. Each component model is scalable such that different vehicle powertrains may be approximated. Using an 'ecorouting' process, the most ideal route is selected by weighting relative total energy consumption and travel time. Only limited powertrain characteristics are publicly available. As the future goal of this project is to apply the model to many vehicle powertrain types, the powertrain model must be reasonably accurate with minimal vehicle powertrain characteristics. Future work expands this model to constantly re-evaluate energy consumption with real-time traffic and terrain information. While ecorouting has been applied to conventional vehicles in many publications, electrified vehicles are less studied. Hybrid vehicles are particularly complicated to model due to additional components, systems, and operation modes. This methodology has been validated to represent conventional, battery electric, and parallel hybrid electric vehicles. A sensitivity study demonstrates that the model is capable of differentiating powertrains with different parameters and routes with different characteristics.
Master of Science

Idag uppmärksammas energiförbrukningen allt mer och mer, både inom privata och industriella sektorer. Emellertid ägnas inom industrin mycket lite uppmärksamhet åt skärvätskesystem. Alltsom oftast är studier gällande vätskesystem mer intresserade av den mängd vätska som används, inte mängden energi.I denna avhandling var syftet att skapa en interaktiv modell som gör det enkelt för användaren att kartlägga energikonsumtionen i sina skärvätskesystem och spåra trender av komponenter av hög energiförbrukning. Modellfunktionerna är huvudsakligen baserade på resultaten av företagsbesök och online litteraturforskning. Modellen har skapats i Microsoft Excel. När modellen byggts användes den för att analysera de observerade systemen. Modellen beräknade energiförbrukningarna och identifierade även pumpar som de högst energikrävande komponenterna för de flesta systemen.Emedan modellen är ganska enkel som den är, beror detta på brist av befintlig information och oförmågan att få information gällande systemdesign och layout på ett en rimligtvis enkelt sätt. Framtida expansioner av modellen diskuteras, och är främst i fråga om utbyte av programvara.
In the current environment, energy consumption is gaining more and more attention, both within private and industrial settings. However, within the industrial world, very little attention is being paid to cutting fluid systems. More often than not, fluid system studies are more concerned with the amount of fluid used, not the amount of energy.In this thesis, the purpose was to create an interactive model that makes it easy for the user to map down the energy consumption of their cutting fluid systems and track down trends of components of high energy consumption. The model features are mainly based on the results of company visits and online literature research. The model itself is created in Microsoft Excel.Once the model was built, it was utilized to analyze the observed systems. The model calculated the energy consumptions, and also identified pumps as the major energy consuming components for the majority of the systems.While the model is fairly simple as it stands, this is due to lack of existing information and the inability to gain data rearing system design and layout in a reasonably simplistic form. Future expansion of the model is discussed, and are mainly in regard of software data exchange.

A production industry with many robots working 24 hours a day, 7 days a week consumes a lot of energy. Industries aim to reduce the energy consumed per machine so as to support their financial budgets and also to be a more sustainable, energy efficient entity. Energy models can be used to predict the energy consumed by robot(s) for optimising the input parameters which determine robot motion and task execution. This work presents an ener-gy model to predict the energy consumption of 2D belt robots used for press line tending. Based on the components' specifications and the trajectory, an estimation of the energy consumption is computed. As part of this work, the proposed energy model is formulated, implemented in MATLAB and experimentally validated. The energy model is further used to investigate the effect of tool weight on energy consumption which includes predicting potential energy reductions achieved by reducing the weight of the gripper tools. Further, investigation of potential energy savings which can be achieved when mechanical brakes are used when the robot is idle is also presented. This illustrates the purpose and usefulness of the proposed energy model.

Thesis (Ph. D.)--West Virginia University, 2007.
Title from document title page. Document formatted into pages; contains xvi, 222 p. : ill. (some col.). Includes abstract. Includes bibliographical references (p. 203-207).

Energy use in houses causes the indirect emission of Carbon Dioxide (CO2) and there is an increasing need to develop effective models to predict such emissions to establish benchmarks against which reductions can be targeted. The Prometheus model described in this thesis is such a tool which was developed under a holistic approach integrating the physical conditions of the house, climatic variations and also social variables all of which affect consumption. The model is sufficiently robust that with minimal modification in the extension of underlying databases it could be used in other areas within the UK.

The main purpose of this study is to examine the causal relations between energy use, CO2 emissions and economic growth for Sweden. Vector Error Correction model with annual data from 1970 to 2016 has been used in order to determine potential causality between the variables. The empirical findings indicate that in the long-run, causality relationship between energy consumption, CO2 emissions and economic growth cannot be rejected and it is bidirectional. This means that energy is a determining factor for economic growth in Sweden and that applying policies in order to reduce the CO2 emissions has slowed down economic growth in Sweden. This finding is consistent with the Feedback Hypothesis. But in the short-run no causality was found between energy and economic growth. According to Granger causality test results, bidirectional causality between CO2 emissions and energy consumption cannot be rejected in the short-run. Variables’ trends show that in the period under study, energy consumption and economic growth have moved in the same direction; meaning that higher energy consumption has led to higher economic growth. At the same time, lower CO2 emissions have been accompanied by higher economic growth. There is also short-run causality running from capital to economic growth according to VECM results. It can be suggested to the policy makers that in order to maintain economic growth and reduce environmental degradation, energy consumption should be shifted gradually from nonrenewable sources to renewable ones so to avoid decrease in economic growth and ensure lower levels of CO2 emissions in the long-run.

FDI investment is a vital factor for the developing countries economic growth. Apart from working as a catalyst of increasing total output level, FDI is a source of clean energy, technology transfer and energy efficiency. There have been very limited studies on the impact of FDI on renewable energy consumption in the context of Bangladesh. In fact, to my best knowledge there hasn’t been any studies on Bangladesh regarding this relationship with recent data available. Therefore, the aim of this paper is to reveal the relationship between FDI and renewable energy consumption in Bangladesh with annual Data spanning from 1980 to 2016. Johansen’s cointegration test showed that variables are cointegrated in the long run. Through Vector Error Correction Model (VECM), the paper shows there is short run and long run causality between FDI and Renewable Energy Consumption and the causality is negative. Granger causality test reveals that the direction of causality is running from FDI to Renewable Energy Consumption. Policies regarding attracting more sectoral FDI should be considered to improve investment scenario in Renewable energy sector.

Building portfolio management on campus and metropolitan scale involves decisions about energy retrofits, energy resource pooling, and investments in shared energy systems, such as district cooling, community PV and wind power, CHP systems, geothermal systems etc. There are currently no tools that help a portfolio/campus manager make these decisions by rapid comparison of variants. The research has developed an energy supply network management tool at the campus scale. The underlying network energy performance (NEP) model uses (1) an existing energy performance toolkit to quantify the energy performance of building energy consumers on hourly basis, and (2) added modules to calculate hourly average energy generation from a wide variety of energy supply systems. The NEP model supports macro decisions at the generation side (decisions about adding or retrofitting campus wide systems) and consumption side (planning of new building design and retrofit measures). It allows testing different supply topologies by inspecting which consumer nodes should connect to which local suppliers and to which global suppliers, i.e. the electricity and gas utility grids. A prototype software implementation allows a portfolio or campus manager to define the demand and supply nodes on campus scale and manipulate the connections between them through a graphical interface. The NEP model maintains the network topology which is represented by a directed graph with the supply and demand nodes as vertices and their connections as arcs. Every change in the graph automatically triggers an update of the energy generation and consumption pattern, the results of which are shown on campus wide energy performance dashboards. The dissertation shows how the NEP model supports decision making with respect to large-scale building energy system design with a case study of the Georgia Tech campus evaluating the following three assertions: 1. The normative calculations at the individual building scale are accurate enough to support the network energy performance analysis 2. The NEP model supports the study of the tradeoffs between local building retrofits and campus wide energy interventions in renewable systems, under different circumstances 3. The NEP approach is a viable basis for routine campus asset management policies.

Whilst accounting for about 7% of the total built area within the EU non-residential sector, hospitals present the highest energy consumption per unit floor area. They contribute 10% of the total energy use and are estimated to be responsible for roughly 5% of the EU carbon dioxide emissions. High hospital energy consumption is mostly due to extremely high demand for space heating and cooling throughout the year, caused by the need of high air change rates and the strict requirements for microclimatic control. In addition, the fast-moving advances of the diagnostic techniques require the continuous remodelling of the spaces in existing facilities. This process, besides architectural-distributive aspects, deeply impacts hospital energy consumption, underlining the need of forecasting these new dynamics in relation to the different medical functions. However, a review of the studies available in literature uncovers that energy analyses are still rarely based on departmental level, thus hospital energy consumption by space type remains largely unexplained. Furthermore, very few works are primarily based on measurement data as well as examine large samples of whole existing structures. This critical knowledge gap, along with a lack of studies exploring hospital energy performance by carefully considering the huge domain of the affecting factors, hinder the construction of more robust and reliable energy benchmarking. Against this background, the aim of the work consists in the development of a simplified data-driven numerical model able to identify and quantify the dominant end uses in energy consumption costs in relation to the type of the spaces. Six hospitals located in Northern Italy were taken as case studies and data regarding their medical functions, morphological features of the buildings and levels of insulation, air change rates, operating hours, etc. were analysed in order to build the numerical model, which was then validated with measured energy consumption data of the six facilities. The main aspect of the work is represented by the methodological framework, which has been based on the identification of the energy needs and consumption from micro (single space) to macro scale (macro-area). This method of analysis allows to have energy consumption per unit conditioned floor area and volume by type of medical functions. Thus, the model developed enables to assess annual thermal energy consumption of hospital buildings – including the energy consumed for summer reheating, DHW and steam used for humidification and sterilisation – in relation to the different type of spaces and to forecast changes in energy consumption related to the refurbishment or modification of these spaces. The objective of the work, omitting complex dynamic modeling, is also to develop a method as simple as possible, which allows fast obtaining of fairly reliable results, being primarily intended for engineers, architects, technical staff responsible for the maintenance of healthcare facilities and energy managers. The simplified numerical model does not only allow to assess energy consumption, define effective energy saving measures, cut costs and invest saved resources to improve healthcare. By reducing hospital energy consumption, it represents a robust and reliable instrument to cut healthcare-related carbon emissions.
Pur rappresentando circa il 7% della superficie degli edifici appartenenti al settore non residenziale in Europa, gli ospedali sono caratterizzati dal più alto consumo di energia. Essi contribuiscono per il 10% al consumo energetico totale e sono responsabili di circa il 5% delle emissioni di biossido di carbonio a livello europeo. L'elevato consumo di energia negli edifici ospedalieri è dovuto principalmente ai fabbisogni per riscaldamento e raffrescamento, che sono estremamente alti durante tutto l'anno a causa della necessità di garantire elevati tassi di ventilazione e rispettare i requisiti relativi al controllo microclimatico degli ambienti. Inoltre, i rapidi progressi delle tecniche diagnostiche richiedono la continua riorganizzazione degli spazi nelle strutture sanitarie. Tale processo, oltre a coinvolgere aspetti architettonici e distributivi, incide profondamente sui consumi energetici, sottolineando la necessità di prevedere queste nuove dinamiche in relazione alle diverse funzioni mediche. Tuttavia, l’esame degli studi disponibili in letteratura rivela che le analisi energetiche sono raramente svolte a livello di reparti ospedalieri. Inoltre, pochissime ricerche sono basate principalmente su dati di misurazione, così come pochissimi lavori esaminano campioni significativi di tipologie di spazi in strutture esistenti. Tale lacuna nell’ambito dell’analisi e previsione dei consumi, insieme alla mancanza di studi che esaminano le prestazioni energetiche degli ospedali considerando attentamente l'enorme dominio dei fattori che su di esse incidono, ostacola lo sviluppo di benchmarking energetici solidi e affidabili. In questo contesto, l'obiettivo del lavoro è lo sviluppo di un modello numerico semplificato, costruito a partire da dati reali, in grado di identificare e quantificare le voci di costo dell’energia negli edifici ospedalieri in relazione alle tipologie di spazi. Sei ospedali situati nel nord Italia sono stati selezionati come casi di studio e ne sono stati analizzati i dati relativi ai diversi reparti, le caratteristiche morfologiche degli edifici e i livelli di isolamento, i tassi di ventilazione, le ore di funzionamento degli impianti, ecc. per la costruzione del modello numerico, il quale è stato validato con i dati reali dei consumi energetici degli ospedali considerati. L'aspetto principale del lavoro è rappresentato dall’approccio metodologico, il quale è basato sull'identificazione dei fabbisogni energetici e dei consumi da micro (singolo spazio) a macro-scala (macro-area). Questo metodo di analisi consente di ottenere i consumi energetici per unità di superficie e per unità di volume in funzione della destinazione d'uso degli spazi. Pertanto, il modello consente di valutare il consumo annuale di energia termica degli edifici ospedalieri – compresi i consumi per il post-riscaldamento estivo, l'acqua calda sanitaria e la produzione di vapore per l’umidificazione e la sterilizzazione – in relazione ai diversi spazi, e di prevedere le variazioni dei consumi legate alla ristrutturazione o modifica degli spazi stessi. Scopo del lavoro, omettendo complesse simulazioni energetiche dinamiche, è anche quello di sviluppare un metodo estremamente semplice che consenta di ottenere rapidamente risultati affidabili, essendo principalmente destinato a ingegneri, architetti, al personale tecnico responsabile della gestione delle strutture sanitarie e agli energy managers. Tale modello non permette solo di valutare i consumi, definire interventi di risparmio energetico efficaci, diminuire i costi relativi e investire le risorse risparmiate per migliorare l'assistenza sanitaria. Riducendo il consumo di energia degli ospedali, il modello sviluppato rappresenta uno strumento robusto per ridurre le emissioni di carbonio che derivano dalle strutture sanitarie.

This research proposes to simplify the energy consumption assessment for residential homes while building the foundation towards the development of prediction tools that can achieve a credible level of accuracy for confident decision making. The energy consumption assessment is based on simplified energy consumption models. The energy consumption analysis uses a reduced number of energy model equations utilizing a critical, limited set of parameters. The results of the analysis are used to develop the minimum set of consumption influence parameters with predicted effects for each energy consumption domain. During this research study, multiple modeling approaches and occupancy scenarios were utilized according to climate conditions in Blacksburg, Virginia. As a part of the analysis process, a parameter study was conducted to: develop a comprehensive set of energy consumption influence parameters, identify the inter-relationships among parameters, determine the impact of energy consumption influence parameters in energy consumption models, and classify energy consumption influence parameters under identified energy consumption domains. Based on the results of the parameter study, a minimum set of parameters and energy consumption influence matrices were developed. This research suggests the minimum set of parameters with predicted effects to be used during the development of the simplified baseline energy consumption model.
Ph. D.

1. Confronto fra i componenti termici ed elettrici sulla base delle conoscenze dei circuiti elettrici. Svilluppo di modelli termici tradizionali per le finestre e le pareti, realizzando la quantizzazione della perdita di calore di un edificio. Per quanto riguarda le teorie, i dati edi risultati di ricerche esistenti relativi al tempo, materiali di costruzione, termotecnica, astronomia e meteorologia, viene proposta la metodologia sulla stima oraria dell'energia solare e della radiazione atmosferica laddove l'approssimazione matematica risulta la più adatta al problema . 2. Definizione del concetto di "unit wall " basato sul miglioramento innovativo del modello di edificio tradizionale grazie all'ausilio della termocamera ad infrarossi e del quadricottero che consente di ottenere direttamente le informazioni sulla distribuzione della temperatura anziché dover ricorrere al calcolocome nel modello tradizionale, con il rischio di errori aggiuntivi. 3. Costruzione sistematica di un modello matematico per una termocamera ad infrarossi, basata sulla teoria dell'infrarosso termico e della radiazione, realizzando la conversione di file RAW originali a 14 bit. 4. Esecuzione della calibrazione dell'immagine, distorsione dell’obiettivo e la rettifica prospettica comprese, tramite la conoscenza della visione artificiale e dell'elaborazione delle immagini. Le finestre possono essere selezionate e rimosse con precisione dalle pareti usando il cursore in un'interfaccia utente grafica. La misurazione manuale delle dimensioni degli edifici può essereevitatagrazie all’utilizzo dei parametri dell'obiettivo e della distanza dell'oggetto. Infine, per convalidare l'applicabilità e l'accuratezza del modello, viene presentato il framework applicativo costituito dalla termocamera ad infrarossi Flir VUE Pro 640 trasportata da un quadricottero SAGA D600 alimentato dal Pixhawk firmware open source, seguito da esperimenti e test di moduli singoli.

One of the greatest challenges that will face humanity in the 21st century is the issue of climate change brought about by emissions of greenhouse gases. Energy use is one of the primary sources of greenhouse gas emissions. However, it is also one of the most important contributors to improved human welfare over the past two centuries and will continue to be so for years to come. This quandary has led a number of researchers to suggest that geoengineering may be required in order to allow for continued use of fossil fuels while at the same time mitigating the effects of the associated greenhouse gas emissions on the global climate. The goal of this research was to develop a model that would allow decision-makers and policy analysts to assess the optimal mix of energy and geoengineering resources needed to meet global or regional energy demand at the lowest cost while accounting for appropriate emissions, greenhouse gas concentration, or temperature rise constraints. The resulting software model is called the Combined Energy and Geoengineering Optimization Model (CEAGOM). CEAGOM was then used to analyze the recently announced U.S.-China emissions agreement and to assess what the optimal global energy resource mix might be over the course of the 21st century, including the associated potential need for geoengineering. These analyses yielded optimal mixes of energy and geoengineering resources that could be used to inform regional and global energy and climate management strategies.

The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 traditional internal combustion engine Chevrolet Blazer and to transform the vehicle into a P4 hybrid. Due to the P4 Hybrid architecture, the HEVT vehicle has an internal combustion engine on the front axle and an electric motor on the rear axle. The goal of this competition is to create a vehicle that achieves better fuel economy and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced vehicle technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of a full range longitudinal Adaptive Cruise Control (ACC) algorithm. Initially, research is conducted on various linear and nonlinear control strategies that provide the necessary functionality. Based on the ability to predict future time instances in an optimal method, the Model Predictive Control (MPC) algorithm is chosen and combined with other standard control strategies to create an ACC system. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy savings to the rider while maintaining safety as the priority. Rider comfort is achieved by placing constraints on acceleration and jerk. Lastly, a proper energy analysis is conducted to showcase the potential energy savings with the implementation of the Adaptive Cruise Control system. This implementation includes tuning the algorithm so that the best energy consumption at the wheel is achieved without compromising vehicle safety. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified nonlinear vehicle system model in MATLAB to simulate different conditions. For each condition, comfort and energy consumption are analyzed. The city 505 simulation of a traditional ACC system show a 14% or 42 Wh/mi reduction in energy at the wheel. The city 505 simulation of the environmentally friendly ACC system show a 29% or 88 Wh/mi reduction in energy at the wheel. Furthermore, these simulations confirm that maximum acceleration and jerk are bounded. Specifically, peak jerk is reduced by 90% or 8 m/s3 during a jerky US06 drive cycle. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces tractive energy consumption while improving rider comfort for any vehicle.
Master of Science
The Hybrid Electric Vehicle Team (HEVT) of Virginia Tech is partaking in the 4-Year EcoCar Mobility Challenge organized by Argonne National Labs. The objective of this competition is to modify a stock 2019 Chevrolet Blazer into a hybrid. This modification is accomplished by creating a vehicle that burns less gasoline and increases customer appeal. The general target market of hybrids is smaller vehicles. As a midsize sport utility vehicle (SUV), the Blazer offers a larger vehicle with the perk of better fuel economy. In the competition, the vehicle is assessed on the ability to integrate advanced technology, improve consumer appeal, and provide comfort for the passenger. The research of this paper is centered around the design of Adaptive Cruise Control (ACC). Initially, research is conducted on various control strategies that provide the necessary functionality. A controller that predicts future events is selected for the Adaptive Cruise Control. The main objective of this research is the implementation of Adaptive Cruise Control features that provide comfort and energy consumption savings to the rider while maintaining safety as the priority. Rider comfort is achieved by creating a smoother ride. Lastly, a proper energy analysis showcases the potential energy savings with the implementation of the Adaptive Cruise Control system. The scope of this paper expands on current knowledge of Adaptive Cruise Control by using a simplified vehicle model to simulate different conditions. The city simulations of a traditional ACC system show a 14% reduction in energy at the wheel. City simulations of the environmentally friendly Adaptive Cruise Controller show a 29% reduction in energy. Both of these simulations allow for comfortable ride. Specifically, maximum car jerk is reduced by 90%. The main objective of this analysis is to demonstrate that with proper implementation, this ACC system effectively reduces energy consumption at the wheel while improving rider comfort.

Le secteur du bâtiment est considéré comme un gros consommateur d'énergie et une source de pollution majeure parmi tous les secteurs économiques. Il représente entre 16 et 50 pour cent des consommations nationales d'énergie. La réduction de ces consommations et des émissions est donc une étape importante vers un développement durable. Récemment, la transition vers la construction des bâtiments à faible consommation d’énergie a conduit à de nouvelles exigences en matière de performance et de durabilité, et ainsi encore complexifié le processus de conception des bâtiments. Le comportement des occupants est maintenant considéré comme un facteur déterminant de la performance énergétique d’un bâtiment, particulièrement dans le cas des bâtiments basse consommation (BBC). Pourtant, les outils de simulation utilisés dans l'industrie des bâtiments ne sont pas aujourd'hui en mesure de fournir des estimations fiables de la demande d'énergie des occupants. Par conséquent, les experts en énergie et bâtiments portent une grande attention à développer des méthodes plus précises pour la modélisation et la prévision de l’influence des occupants sur la performance du bâtiment. Ces modèles doivent pouvoir fournir des estimations plus précises des consommations d’énergie et évaluer la variabilité de ces consommations. En conséquence, l’objectif visé est de permettre aux experts en construction d’améliorer leurs solutions techniques, améliorer la performance de leurs services, et promouvoir des incitations mieux ciblées vers les usagers afin de réduire leurs consommations énergétiques. L'objectif de cette thèse est de proposer un modèle pour estimer la consommation d'énergie liée aux comportements des occupants de bâtiments résidentiels, en prenant en compte la variabilité des modes de consommation au travers de la diversité des profils socio-démographiques et économiques des occupants. Une approche stochastique basée sur la notion d’activité est donc adoptée. Avec ce modèle, la consommation d'énergie d'un ménage est estimée en additionnant la consommation d'énergie des différentes activités domestiques (comme faire la cuisine, le lavage du linge, etc.). La nature stochastique du modèle est due aux relations probabilistes établies entre les attributs des ménages d'une part (type de ménage, nombre d'occupants, etc.) et la possession des équipements domestiques, les caractéristiques des appareils, leur puissance, et les quantités d'activité d’autre part. Afin d'établir ces relations stochastiques, un nombre suffisant d'attributs est pris en compte pour caractériser un ménage. Le modèle proposé a été appliqué pour deux activités domestiques, à savoir regarder la télévision et laver le linge. Des simulations de Monte Carlo sont effectuées pour fournir des estimations de consommation d'énergie pour ces deux activités dans trois cas de figure : pour un ménage spécifique, pour des ménages générés aléatoirement avec des contraintes sur leurs attributs, et pour des ménages totalement aléatoires représentatifs de la population française. Une comparaison entre les résultats de la simulation de modèle d’une part et des données de consommation d'énergie réelle d’autre part, a permis de valider le modèle pour les deux activités considérées. Un cadre de généralisation du modèle pour d'autres activités domestiques a été introduit, et sa possible intégration dans le processus de conception des bâtiments a été discutée et illustrée au travers d’un certain nombre d’exemples
Résumé en Anglais : The building sector is considered as a major energy consumer and pollution source among all economic sectors. It accounts for important shares, ranging between 16 and 50 percent, of national energy consumption worldwide. Reducing these consumptions and emissions is thus an important step towards sustainable development. Recently, the shift towards constructing low-consuming and nearly zero-energy buildings lead to further requirements with regard to performance and sustainability, and thus caused the design process of buildings to be more complex. Occupants’ behavior is now considered as a key determinant of building’s energy performance especially in the case of green buildings. Yet, energy simulation tools used in buildings industry nowadays are not capable of providing accurate estimations of occupant-related energy demands. Therefore, buildings and energy experts are devoting considerable efforts on developing more precise methods for modeling and forecasting occupants influence on whole building performance. Such models can provide accurate energy estimates and can assess future consumption variability. Consequently, building experts may improve their technical solutions, ameliorate their service performances, and promote targeted incentives. The objective of this dissertation is to propose a model for forecasting occupant-related energy consumption in residential buildings, while accounting for variability in consumption patterns due to diversity in occupants’ socio-demographic and economic profiles. A stochastic activity-based approach is thus adopted. By activity-based, it means that energy consumption of a household is estimated by summing up the energy use of different activities performed (such as cooking, washing clothes, etc.). The stochastic nature of the model is due to the probabilistic mapping established between household attributes from one side (household type, number of occupants, etc.) and the corresponding appliance ownership, appliance characteristics and power rating, and activity quantities from the other side. In order to establish these stochastic relations, a fairly sufficient number of households’ characterizing attributes is taken into account. The proposed model is applied for two domestic activities, namely watching TV and washing laundry. Three types of Monte Carlo simulations are performed to provide energy estimates for these two activities: for a given specified household, for randomly generated households with constraints, and for totally random population-wise households. A comparison between model’s simulation results and real measured energy consumption data enables validating the model for the two considered activities. A generalization framework of the modeling approach for other domestic activities is sketched, and its possible integration into buildings design process is discussed and illustrated through a number of examples

This thesis deals with a real application of econometric methods to the analysis of electric energy consumption in a significant Czech brewery. The main objective is to construct a model predicting the electric energy consumption in the production process in the next week based on various data measured in the last 2 years. Results will be used in the costs management of the company.

This research investigated airborne particle characteristics and their dynamics inside and around the envelope of mechanically ventilated office buildings, together with building thermal conditions and energy consumption. Based on these, a comprehensive model was developed to facilitate the optimisation of building heating, ventilation and air conditioning systems, in order to protect the health of their occupants and minimise the energy requirements of these buildings.

Current mobile network architecture is facing a big challenge as the traffic demands have been increasing dramatically these years. Explosive mobile data demands are driving a significant growth in energy consumption in mobile networks, as well as the cost and carbon footprints [1]. In 2010, China Mobile Research Institute proposed Cloud Radio Access Network (C-RAN) [2], which has been regarded as one of the most promising architecture to solve the challenge of operators. In C-RAN, the baseband units (BBU) are decoupled from the remote radio units (RRH) and centralized in one or more locations. The feasibility of combination of implementing the very tight radio coordination schemes and sharing baseband processing and cooling system resources proves to be the two main advantages of C-RAN compared to traditional RAN. More importantly, mobile operators can quickly deploy RRHs to expand and make upgrades to their networks. Therefore, the C-RAN has been advocated by both operators and equipment vendors as a means to achieve the significant performance gains required for 5G [3]. However, one of the biggest barriers has shown up in the deployment of C-RAN as the novel architecture imposes very high capacity requirement on the transport network between the RRHs and BBUs, which is been called fronthaul network. With the implementation of 5G wireless system using advanced multi-antenna transmission (MIMO), the capacity requirement would go further up, as well as the power consumption. One solution has been proposed to solve the problem is to have the baseband functions divided, partially staying with RRHs and other functions would be centralized in BBU pool. Different splitting solutions has been proposed in [4] [5] and [6]. In this thesis work, we choose four different splitting solutions to build four CRAN architecture models. Under one specific case scenario with the fixed number of LTE base stations, we calculate the transport capacity requirement for fronthaul and adopt three different fronthaul technology. The power consumption is calculated by adding up the power utilized by RRHs, fronthaul network and baseband processing. By comparing the numerical results, split 1 and 2 shows the best results while split 2 is more practical for dense cell area, since split 1 requires large fronthaul capacity. The fronthaul transport technology can be decided according to different density of base stations. TWDM-PON shows better energy performance as fronthaul network when the capacity requirement is high, compared to EPON. However, for larger number of BSs, mm-Wave fronthaul is a better solution in terms of energy efficiency, fiber saving and flexibility.

The objective of the proposed research is to develop an analytical model that predicts performance and power for many-core architecture and further propose a mechanism, which leverages the analytical model, to enable energy-efficient execution of an application. The key insight of the model is to investigate and quantify a complex relationship that exists between the thread-level parallelism and memory-level parallelism for an application on a given many-core architecture. Two metrics are proposed: memory warp parallelism (MWP), which refers to the number of overlapping memory accesses per core, and computation warp parallelism (CWP), which characterizes an application type. By using these metrics in addition to the architectural and application parameters, the overall application performance is produced. The model uses statically-available parameters such as instruction-mixture information and input-data size, and the prediction accuracy is 13.3% for the GPU-computing benchmarks. Another important aspect of using many-core architecture is reducing peak power and achieving energy savings. By using the proposed integrated power and performance (IPP) framework, the results showed that different optimization points exist for GPU architecture depending on the application type. The work shows that by activating fewer cores, 10.99% of run-time energy consumption can be saved for the bandwidth-limited benchmarks, and a projection of 25.8% energy savings is predicted when power-gating at core level is employed. Finally, the model is shifted to throughput using OpenCL for targeting more variety of processors. First, multiple outputs relating to performance are predicted, including upper-bound and lower-bound values. Second, by using the model parameters, an application can be categorized into a different category, each with its own suggestions for improving performance and energy efficiency. Third, the bandwidth saturation point accuracy is significantly improved by considering independent memory accesses and updating the performance model. Furthermore, a trade-off analysis using architectural and application parameters is straightforward, which provides more insights to improve energy efficiency. In the future, a computer system will contain hundreds of heterogeneous cores. Hence, it is mandatory that a workload gets scheduled to an efficient core or distributed on both types of cores. A preliminary work by using the analytical model to do scheduling between CPU and GPU is demonstrated in the appendix. Since profiling phase is not required, the kernel code can be transformed to run more efficiently on the specific architecture. Another extension of the work regarding the relationship between the speed-up and energy efficiency is mathematically derived. Finally, future research ideas are presented regarding the usage of the model for programmer, compiler, and runtime for future heterogeneous systems.

Energy used in the operation of the United Kingdom’s non-domestic buildings contributes 18% of national carbon dioxide emissions and reducing these is government policy. The use of electrical equipment in buildings is a major contributor to overall consumption, due to both its intrinsic energy consumption and the effects of incidental internal gains resulting from its operation. Knowledge of how and where consumption and internal gains occur in buildings is important in understanding the consumption characteristics of the building stock. The overall aim of this research was to improve the prediction of energy consumption in the non-domestic stock through the inference of appliance electricity consumption and resultant heat gains, for internal space uses of premises, as identified in UK property taxation data. To achieve this, the objectives were to: 1. Develop a method for inferring space usage in premises. 2. Infer values for the electricity consumption of appliances, and hence internal gains, for space uses within premises. 3. Apply the method to a dataset at the urban scale and use a suitable model to deduce the energy consumption. 4. Compare the results with measured data. Objectives 1 and 2 were achieved through analyses of detailed energy surveys of more than 300 non-domestic premises. By excluding equipment used for heating and cooling, both intrinsic electricity consumption and internal gains from appliances have been characterised for combinations of internal space use and premises activity type. For each combination, the characteristics include the energy intensity (kWh/m2/year) for: • overall appliance use • 14 end uses of appliances (e.g. lighting, catering, computers) • 18 groups of appliance activity descriptions (e.g. sales, office work, process) These characteristics were mapped onto subdivisions of space use, within premises, listed in property taxation data for a test urban area (City of Leicester). Using only 115 descriptions of space use, appliance consumption characteristics have been inferred for 91.5% of the measured internal floor area of the test dataset; this achieved the third objective. More than 80% of the floor area was identified using standard space use descriptions utilised in real estate taxation datasets. The total estimated consumption accounted for 75% of the recorded annual electricity consumption of the test area (the fourth objective). This result is acceptable, given the known limitations of the datasets and suggests that the method constitutes an improvement to stock energy modelling, thus meeting the overall aim. By inferring appliance electricity consumption and internal gains at a finer spatial resolution than previous methods, the diversity of energy consumption characteristics of the non-domestic stock may be represented more faithfully than by values applied to entire homogenised premises or premises types. The method may be used by policy makers as part of an urban energy model and as a means of evaluating potential energy interventions in the non-domestic stock, or parts thereof.

Thesis: S.M. in Transportation, Massachusetts Institute of Technology, Department of Civil and Environmental Engineering, 2013.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 101-109).
Household in-home activities and out-of-home transportation are two major sources of urban energy consumption. In light of China's rapid urbanization and income growth, changing lifestyles and consumer patterns - evident in increased ownership of appliances and motor vehicles - will have a large impact on residential energy use in the future. The pattern of growth of Chinese cities may also play an intertwined role in influencing and being influenced by consumption patterns and, thus energy use. Nonetheless, models for evaluating energy demand often neglect the evolution of appliance & vehicle ownership and directly correlate consumption with static characteristics without explicit behavioral links. In this thesis I aim to provide a comprehensive method for understanding household energy behavior over time. Using household survey data and neighborhood form characteristics from Jinan, a mid-sized Chinese city, I explore the relationship between neighborhood design and household-level behaviors and their impact on final energy consumption. My ultimate goal is to provide the modeling engine for the "Energy Proforma©" a tool intended to help developers, designers, and policy-makers implement more energy-efficient neighborhoods. To predict in-home and transportation energy use, and their trade-offs, I develop an integrated household-level micro-simulation framework. The simulation tool is based on a total of eight inter-related behavioral models which estimate out-of-home energy use by predicting trip generation, mode choice and trip length for each household and in-home energy use according to different energy sources. In the various sub-models, relevant dimensions of neighborhood form and design are included as explanatory variables. These models are then combined with modules that update household demographics, appliance & vehicle ownership information, and activity trade-off patterns. These inter-linked models can then be used to estimate the long-term effects of neighborhood design on household energy consumption and greenhouse gas emissions. Unlike separate in-home or out-of-home energy demand models, I develop an integrated simulation framework for forecasting. It captures estimated trade-off effects between in-home and transportation energy-consuming behaviors. The approach produces indicators of detailed behavioral outcomes such as trip mode and trip length choice, making it easier to relate policies, such as mode-oriented strategies, to ultimate outcomes of interest. I ultimately aim to provide urban designers, developers, and policy makers a decision support tool to explore and compare long-term energy performance across proposed neighborhood development projects.
by Feifei Yu.
S.M. in Transportation

Power is one of the today’s major constraints for both hardware and software design. Thus the need to understand the statistics and distribution of power consumption from a hardware and software perspective is high. Power models satisfy this requirement to a certain extent, by estimating the power consumption for a subset of applications, or by providing a detailed power consumption distribution of a system. Till date, many power models have been proposed for the desktop and mobile platforms. However, most of these models were created based on power measurements performed on the entire system when different microbenchmarks stressing different blocks of the system were run. Then the measured power and the profiled information of the subsystem stressing benchmarks were used to create a regression analysis based model. Here, the power/energy prediction accuracy of the models created in this way, depend on both the method and accuracy of the power measurements and the type of regression used in generating the model. This work tries to eliminate the dependency of the accuracy of the power models on the type of regression analysis used, by performing power measurements at a subsystem granularity. When the power measurement of a single subsystem is obtained while stressing it, one can know the exact power it is consuming, instead of obtaining the power consumption of the entire system - without knowing the power consumption of the subsystem of interest - and depending on the regression analysis to provide the answer. Here we propose a generic method that can be used to create power models of individual subsystems of mobile platforms, and validate the method by presenting an empirical power model of the OMAP4460 based Pandaboard-ES, created using the proposed method. The created model has an average percentage of energy prediction error of just around -2.7% for the entire Pandaboard-ES system.

Meanwhile environmental concerns and global energy consumption continue to increase, the current ageing power distribution grid is becoming increasingly inefficient and unreliable. The vision of Smart Grid is to create a widely distributed energy supply infrastructure by means of information and communications technology (ICT). By incorporating ICT in all aspects of electricity delivery, generation and consumption the intention is to ensure a better match between supply and demand, while also easing the transition to increased use of renewable energy sources. This study explores the dynamics of electricity consumption in households and the potentials of photovoltaic energy generation in residential Smart Grid cells (microgrids). That is, by analysing actual consumption patterns and solar generation data, the aim is to investigate the potential benefits of distributed energy generation and storage in futuristic microgrids. Furthermore, by using the acquired dynamics of energy generation and consumption it is attempted to model autonomy in microgrids with purpose of shaving usage peaks and avoiding power outages by use of local generation and storage.

There are many factors which are understood to a ect domestic energy consumption, including: occupant demographics such as age, income and family type, occupant attitudes, peer networks and occupant-building interactions such as window opening, heating and lighting patterns. Both top-down and bottom-up modelling approaches have been used previously to represent these behavioural factors and other domestic energy usage variables such as dwelling construction. Top-down models were found to lack the granularity and exibility to accurately portray the UK domestic energy sector from the perspective of individual households. Conversely, bottom-up models were found to be more applicable to behavioural factors due to their ability to model individual entities and interactions. However, it was also identi ed that most current models only consider building construction or occupant behaviour, with few combining the two. This project aims to combine occupant behaviour and dwelling construction variables by suggesting an agent-based model implementation schema to provide insight into the domestic energy consumption system, with special interest in the e ects of life-stage changes on the household and the e ect of peer networks on the adoption of energy e ciency measures. Five `shock' groups are considered, namely, households a ected by a recent: retirement, unemployment, new child, house move or reduction in household size. A pilot survey was conducted in order to obtain results to inform model design decisions and the results are discussed.

The purpose of this master thesis is to study the energy efficiency of a vehicle when it is cornering. To achieve this, a Simulink model was built from a simple basic bicycle model and theoretically validated. This model was then analysed and successively improved by adding velocity and yaw moment control. A study of the vehicle model behaviour by changing parameters such as cornering stiffness and centre of gravity position was the nconducted. The traction force needed for a constant radius was calculated and methods such as torque vectoring have been tested using the model to obtain the lowest traction force. The model was compared with different vehicle types and further validated by comparing the simulation results with experimental data acquired from a field test. The rolling resistance and aerodynamic resistance were taken into account when the model was validated with the experimental data and the result suggest that by distributing the required traction force (using torque vectoring between inner and outer driven wheels) the energy efficiency could be improved by 10%. This report ends with recommendations for future work.

Energy and environmental impacts are two factors that will influence urban region composition in the near future. One emerging issue is the effect on water usage resulting from changes in regional or urban transportation trends. With many regions experiencing stresses on water availability, transportation planners and users need to combine information on transportation-related water consumption for any region and assess potential impacts on local water resources from the expansion of alternative transportation modes. This thesis will focus on use-phase water consumption factors for multiple vehicle modes, energy and fuel pathways, roads, and vehicle infrastructure for a given transportation network. While there are studies examining life cycle impacts for energy generation and vehicle usage, few repeatable models exist for assessing overall water consumption across several transportation modes within urban regions. As such, the question is: is it possible to develop a traceable decision support model that combines and assesses water consumption from transportation modes and related mobility infrastructure for a given mobility network? Based on this, an object-oriented system model of transportation elements was developed using the Systems Modeling Language (SysML) and Model-Based Systems Engineering principles to compare water consumption across vehicle modes for assessing the resiliency of existing infrastructure and water resources. To demonstrate the intent of this model, daily network usage water consumption will be analyzed for current and alternative network scenarios projected by policies regarding the expansion of alternative energy. The model is expected to show variations in water consumption due to fluctuations in energy pathways, market shares, and driving conditions, from which the model should help determine the feasibility of expanding alterative vehicles and fuels in these networks. While spatially explicit data is limited compared to the national averages that are used as model inputs, the analytical framework within this model closely follows that of existing assessments and the reusable nature of SysML model elements allows for the future expansion of additional transportation modes and infrastructure as well as other environmental analyses.

Wireless access networks today consume 0.5 percent of the global energy. Rapidly growing demand for new services and ubiqutious connectivity, will further increase the energy consumption. This situation imposes a big challenge for mobile operators not only due to soaring cost of energy, but also increasing concern for global warming and sustainable development. This thesis focuses on the energy efficiency issue at the system level and studies how to incorporate energy-awareness into the design of future wireless access networks. The main contributions have been given in the areas of energy efficiency assessment, architectural and operational solutions, and total cost of investment analysis. The precise evaluation of energy efficiency is the first essential step to determine optimized solutions where metrics and models constitute the two key elements.We show that maximizing energy efficiency is not always equivalent to minimizing energy consumption which is one of the main reasons behind the presented contradictory and disputable conclusions in the literature. Further we indicate that in order to avoid the debatable directions, energy efficient network design problems should be formulated with well defined coverage and capacity requirements. Moreover, we propose novel backhaul power consumption models considering various technology and architectural options relevant for urban and rural environments and show that backhaul will potentially become a bottleneck in future ultra-high capacity wireless access networks. Second, we focus on clean-slate network deployment solutions satisfying different quality of service requirements in a more energy efficient manner. We identify that the ratio between idle- and transmit power dependent power consumption of a base station as well as the network capacity requirement are the two key parameters that affect the energy-optimum design.While results show that macro cellular systems are the most energy efficient solution for moderate average traffic density, Hetnet solutions prevail homogeneous deployment due to their ability to increase the capacity with a relatively lower energy consumption and thus enable significant energy savings in medium and high capacity demand regions. Moreover, we investigate the energy saving potential of short-term energy aware management approach, i.e., cell DTX, taking advantage of low resource utilization in the current networks arising from strict QoS requirements. With the help of developed novel quantitative method, we show that Cell DTX brings striking reduction in energy consumption and further savings are achievable if the networks are designed taking into account the fact that network deployment and operation are closely related. Finally, we develop a general framework for investigating the main cost elements and for evaluating the viability of energy efficient solutions.We first reveal the strong positive impact of spectrum on both energy and infrastructure cost and further indicate that applying sustainable solutions might also bring total cost reduction, but the viability highly depends on unit cost values as well as the indirect cost benefits of energy efficiency. Results obtained in this dissertation might provide guidelines for the network designers to achieve future high-capacity and sustainable wireless access networks.

QC 20140505

Orientador: Varese Salvador Timóteo
Dissertação (mestrado) - Universidade Estadual de Campinas, Faculdade de Tecnologia
Made available in DSpace on 2018-08-19T10:25:36Z (GMT). No. of bitstreams: 1 Oliveira_TitoRicardoBianchin_M.pdf: 4698545 bytes, checksum: a02de15000b11124637f63ed02fe50ee (MD5) Previous issue date: 2011
Resumo: O crescimento das redes de terceira geração, aliado a sua alta velocidade para transmissão de dados e banda disponível fazem com que novos terminais lançados no mercado utilizem o comportamento Always On, no qual o dispositivo fica 100% do tempo conectado a rede para transmissão e recepção de dados. Esse comportamento, no entanto, faz com que o consumo de bateria do dispositivo seja maior devido ao uso de aplicativos que recebem informações periodicamente, e principalmente pelo recebimento de pacotes não solicitados provenientes de ataques a rede. Este trabalho tem como objetivo analisar os elementos de rede responsáveis pela transmissão de pacotes de dados, identificando os fatores responsáveis pelo aumento de consumo. Ao final, e proposto um método para melhor aproveitamento dos recursos de radio para transmissão de dados e consequentemente, a diminuição do consumo de energia, utilizando um modelo de previsão
Abstract: The expansion of third generation network and its high speed of data transmission and available bandwidth, made that new designed mobile devices use the "Always On" concept, in which the device is 100% connected in packet switch network, and able for data transmission. This behavior makes the device's energy consumption to be higher due to usage of applications that receives periodically information, and mainly due to the unsolicited packages from hacker attack. This work has as main purpose analyze the network elements responsible for data package transmission, identifying the main factors related to the energy consumption increasing. Finally, it is proposed a method to enhance the radio resources for data transmission and energy consumption decreasing, using a prevision model
Mestrado
Tecnologia e Inovação
Mestre em Tecnologia

Automotive manufacturers are facing increasing pressure from legislative bodies and consumers to reduce fuel consumption and greenhouse gas emissions of vehicles. This has led to many automotive manufacturers starting production of Plug-in Hybrid Electric Vehicles (PHEV's) and Battery Electric Vehicles (BEV's). Another method that helps to reduce the environmental effect of transportation is EcoRouting. The standard Global Positioning System (GPS) navigation offers route alternatives between user specified origin and destination. This technology provides multiple routes to the user and focuses on reducing the travel time to reach to the destination. EcoRouting is the method to determine a route that minimizes vehicle energy consumption, unlike traditional routing methods that minimize travel time. An EcoRouting system has been developed as a part of this thesis that takes in information such as speed limits, the number of stop lights, and the road grade to calculate the energy consumption of a vehicle along a route. A synthesis methodology is introduced that takes into consideration the distance between the origin and destination, the acceleration rate of the vehicle, cruise speed and jerk rate as inputs to simulate driver behavior on a given route. A new approach is presented in this thesis that weighs the energy consumption for different routes and chooses the route with the least energy consumption, subject to a constraint on travel time. A cost function for quantifying the effect of travel time is introduced that assists in choosing the EcoRoute with an acceptable limit on the travel time required to reach the destination. The analysis of the EcoRouting system with minimum number of conditional stops and maximum number of conditional stops is done in this thesis. The effect on energy consumption with the presence and absence of road-grade information along a route is also studied. A sensitivity study is performed to observe the change in energy consumption of the vehicle with a change in acceleration rates and road grade. Three routing scenarios are presented in this thesis to demonstrate the functionality of EcoRouting. The EcoRouting model presented in this thesis is also validated against an external EcoRouting research paper and the energy consumption along three routes is calculated. The EcoRoute solution is found to vary with the information given to the variable acceleration rate model. The synthesis and the results that are obtained show that parameters such as acceleration, deceleration, and road grade affect the overall energy consumption of a vehicle and are helpful in determining the EcoRoute.
Master of Science

In stainless steelmaking, around 68% of the total greenhouse gas emissions come from the processing of raw materials. Thus, it is important for steelmakers to make efforts together with their raw material suppliers to implement low-carbon initiatives. To facilitate such initiatives, assessment of raw materials will provide guidance. In this work, the assessment of materials consists of two parts: i) different production scenarios are studied by using a static process model coupled with life cycle assessment approach to investigate the reduction potential of environmental impacts for Mo and Ni alloys; ii) assessment of the effect of trace element content (phosphorus) in stainless steel scrap on steel’s manufacturing cost, resource consumption and environmental impact using an online static process model. The results show that the overall GHG emission of FeMo production varies between 3.16-14.79 t CO2-eq/t FeMo (i.e. 5.3-24.7 tCO2-eq/t Mo). The main variance comes from the mining and beneficiation stages and depends mainly on the ore’s beneficiation degree. However, whether molybdenum is extracted as a co-product from copper mine or not can have an even greater effect on the total GHG emission of molybdenum due to the allocation of the impacts.  In the case of nickel alloys, the GHG emissions for producing nickel metal, nickel oxide, ferronickel and nickel pig iron are 14, 30, 6 and 7 tCO2-eq/t alloy (i.e. 14, 40, 18, and 69 tCO2-eq/t Ni), respectively. Extracting sulfide ore through flash smelting process has been shown to have the least energy requirement and greenhouse gas emissions. In comparison to sulfide ore processing, oxide ore processed in an electric furnace is much more energy intensive and less environmental friendly primarily due to high content of gangue. However, by using a sustainable electricity source such as hydro-powered electricity, or applying a thermal heat recovery, it is possible to reduce the impact from electric furnace smelting of laterite. Furthermore, the use of stainless steel scraps with low phosphorous contents reduces slag amount, alloy consumption, production cost and carbon footprint. An estimation equation between phosphorous content and scrap’s value-in-use is obtained in the study to support the development of purchasing strategy. To conclude, the application of static process model based on mass and energy balance provides the possibility to assess raw materials’ environmental impact (energy consumption and GHG emissions) and to identify potentials to realize sustainable stainless steelmaking.
Vid tillverkning av rostfritt stål kommer cirka 68% av växthusgaserna ifrån råvaruanvändningen. Därför är det viktigt för ståltillverkare att göra en samordnad insats med sina levenrantörer för att reducera dessa utsläpp. Den här avhandlingen ämnar att undersöka råvaror ur två perspektiv: i) att utvärdera olika produktionsscenarier för  molybden och nickelleggeringar genom en statisk processmodell i kombination med livscykelanalys för att undersöka potentialen för att minska miljöbelastningen; ii) att undersöka hur spårämnesinnehållet (fosfor) i rostfritt stålskrot påverkar ståltillverkningskostnaden, resursförbrukningen och miljöpåverkan med ett webbaserat verktyg för processmodellen. Resultaten visar att växthusgasutsläppen från produktionen av FeMo varierar mellan 3.16-14.79 t CO2-eq/t FeMo (d.v.s. 5.3-24.7 tCO2-eq/t Mo). Variationen beror främst på malmets anrikningsgrad under malmbrytnings- och anrikningsprocessen. När molybden förekommer  i kopparmalm och utvinns som en co-produkt så kan det ha en större effekt på molybdens energiförbrukning och växthusgasutsläpp än vad malmens anrikningsgrad har. I fallet för tillverkning av nickelmetall, nickeloxid, ferronickel och nickeltackjärn är växthusgasutsläppen 14, 30, 6 respektive 7 tCO2-eq/t legering (motsvarande 14, 40, 18, respektive 69 tCO2-eq/t Ni). Användningen av sulfidmalm i flashsmältningsprocessen har visat sig ha lägst energibehov och växhusgasutsläpp medan användningen av oxidmalm i ljusbågsugn både är mer energiintensiv och utsläppsintensiv  på grund av en stor mängd oxider i nickelmalmen. Dessa utsläpp kan dock förbättras genom användningen av hållbar energi (till exempel el från vattenkraft), eller genom värmeåtervinning under processen. Utöver detta kan skrot med lågt fosforinnenhåll också användas vid tillverkningen av rostfritt stål för att minska slaggmängden, förbrukningen av legeringar, produktionskostnaden och växthusgasutläppen.  En ekvation mellan fosforinnehållet och skrotets värde föreslås här som underlag för att utveckla en inköpsstrategi för skrot. Sammanfattningsvis så kan en statisk processmodell baserad på mass- och energibalans tillämpas för att utvärdera råvarors miljöbelastning (energiförbrukning och växthusgasutsläpp) och identifiera potentialen för en hållbar tillverkning av rostfritt stål.

In the context of decarbonizing the electricity generation of French non-interconnected territories, the knowledge of future electricity demand, in particular annual and peak demand in the long-term, is crucial to design new renewable energy infrastructures. So far, these territories, mainly islands located in the Pacific and Indian ocean, relies mainly on fossil fuels powered facilities. Energy policies envision to widely develop renewable energies to move towards a low-carbon electricity mix by 2028.  This thesis focuses on the long-term forecasting of hourly electricity demand. A methodology is developed to design and select a model able to fit accurately historical data and to forecast future demand in these particular territories. Historical data are first analyzed through a clustering analysis to identify trends and patterns, based on a k-means clustering algorithm. Specific calendar inputs are then designed to consider these first observations. External inputs, such as weather data, economic and demographic variables, are also included.  Forecasting algorithms are selected based on the literature and they are than tested and compared on different input datasets. These input datasets, besides the calendar and external variables mentioned, include different number of lagged values, from zero to three. The combination of model and input dataset which gives the most accurate results on the testing set is selected to forecast future electricity demand. The inclusion of lagged values leads to considerable improvements in accuracy. Although gradient boosting regression features the lowest errors, it is not able to detect peaks of electricity demand correctly. On the contrary, artificial neural network (ANN) demonstrates a great ability to fit historical data and demonstrates a good accuracy on the testing set, as well as for peak demand prediction. Generalized additive model, a relatively new model in the energy forecasting field, gives promising results as its performances are close to the one of ANN and represent an interesting model for future research.  Based on the future values of inputs, the electricity demand in 2028 in Réunion was forecasted using ANN. The electricity demand is expected to reach more than 2.3 GWh and the peak demand about 485 MW. This represents a growth of 12.7% and 14.6% respectively compared to 2019 levels.
I samband med utfasningen av fossila källor för elproduktion i franska icke-sammankopplade territorier är kunskapen om framtida elbehov, särskilt årlig förbrukning och topplast på lång sikt, avgörande för att utforma ny infrastruktur för förnybar energi. Hittills är dessa territorier, främst öar som ligger i Stilla havet och Indiska oceanen, beroende av anläggningar med fossila bränslen. Energipolitiken planerar att på bred front utveckla förnybar energi för att gå mot en koldioxidsnål elmix till 2028.  Denna avhandling fokuserar på den långsiktiga prognosen för elbehov per timme. En metod är utvecklad för att utforma och välja en modell som kan passa korrekt historisk data och för att förutsäga framtida efterfrågan inom dessa specifika områden. Historiska data analyseras först genom en klusteranalys för att identifiera trender och mönster, baserat på en k-means klusteralgoritm. Specifika kalenderinmatningar utformas sedan för att beakta dessa första observationer. Externa inmatningar, såsom väderdata, ekonomiska och demografiska variabler, ingår också.  Prognosalgoritmer väljs utifrån litteraturen och de testas och jämförs på olika inmatade dataset. Dessa inmatade dataset, förutom den nämnda kalenderdatan och externa variabler, innehåller olika antal fördröjda värden, från noll till tre. Kombinationen av modell och inmatat dataset som ger de mest exakta resultaten på testdvärdena väljs för att förutsäga framtida elbehov. Införandet av fördröjda värden leder till betydande förbättringar i exakthet. Även om gradientförstärkande regression har de lägsta felen kan den inte upptäcka toppar av elbehov korrekt. Tvärtom, visar artificiella neurala nätverk (ANN) en stor förmåga att passa historiska data och visar en god noggrannhet på testuppsättningen, liksom för förutsägelse av toppefterfrågan. En generaliserad tillsatsmodell, en relativt ny modell inom energiprognosfältet, ger lovande resultat eftersom dess prestanda ligger nära den för ANN och representerar en intressant modell för framtida forskning.  Baserat på de framtida värdena på indata, prognostiserades elbehovet 2028 i Réunion med ANN. Elbehovet förväntas nå mer än 2,3 GWh och toppbehovet cirka 485 MW. Detta motsvarar en tillväxt på 12,7% respektive 14,6% jämfört med 2019 års nivåer.

This thesis is devoted to the study of the optimisation-based control techniques for the design of control strategies that contribute to improve the energy efficiency of smart manufacturing systems. Currently, manufacturing industry is suffering a transformation towards smart, flexible, and energy-efficient manufacturing systems. This transformation requires modularised and reconfigurable manufacturing systems to respond to changes in productions programs and to the time-varying pieces demand while keeping an energy-efficient operation. Thus, suitable control systems should be designed to satisfy the requirements of this transformation while minimising the energy consumption and maximising the plant profit. In this regard, optimisation-based controllers are suitable for the design of control systems that minimise the total energy consumption of such systems while remaining their productivity taking into account the operational conditions and the factors that affect them. First, this dissertation presents how optimisation-based control techniques can contribute to face the challenges of the smart manufacturing systems. Based on this review, manufacturing industry is classified by levels, i.e., machine, process line, and plant levels, for the design of optimisation-based controllers. Besides, to design control strategies that do not affect plant productivity, manufacturing systems are also classified according to the operations performed. Based on these classifications, control strategies are proposed to minimise either the total energy consumption of manufacturing systems or the energy costs related to the operation of such systems. At both machine and process line levels, control strategies are designed based on model predictive control approach to minimise their energy consumption. The underlying idea behind the proposed control strategies consists of managing independently those devices (or systems) that are not directly involved in the machining operations. Thus, energy consumption models are required to predict the total energy consumption profile of manufacturing systems and, based on this, to select the activation/deactivation instants of the manipulated devices that minimise their energy consumption and guarantee their proper operation. Next, due to at the process line level the size and complexity of manufacturing systems increases, a control strategy based on two control modes is proposed to reduce the computational burden of such controllers by switching from a control mode based on online optimisation to an autonomous control mode without solving an optimisation problem. Since the need for flexible and reconfigurable manufacturing systems, non-centralised control strategies are proposed at higher industrial levels to minimise their energy consumption. Thus, both cooperative and non-cooperative local controllers are designed considering a fixed system partitioning and using alternative direction methods of multipliers to solve the optimisations problems in a distributed fashion. Besides, due to the nature of the proposed control objectives, a way to define the consensus stage among the local controllers with coupled dynamics is proposed. Finally, the control strategies designed at plant level are based on the economic model predictive control approach to maximise the plant profit and minimise the operational costs related to the plant operation. At this level, control objectives are focused on determining the economic-optimal production programming of the plant that the control strategies at lower levels should follow. In this regard, the production programming of the plant is determined taking into account the pieces demand, the energy consumption of manufacturing systems, and the current energy market and their fluctuations. All control strategies proposed in this thesis are tested in simulation considering different scenarios designed based on the real operation of an automotive part manufacturing plant.
Esta tesis se basa en el estudio de las técnicas de control basadas en optimización para el diseño de estrategias de control que mejoren la eficiencia energética de los sistemas de manufactura inteligentes. La industria de manufactura se está transformando hacia sistemas de manufactura inteligentes, flexibles y eficientes energéticamente, que requiere de estructuras modulares y reconfigurables para poder responder a los cambios en la programación de la producción y la demanda de piezas. Así, se deben diseñar sistemas de control que cumplan los requerimientos de dicha transformación mientras minimizan el consumo de energía y maximizan la rentabilidad de la planta. En este sentido, los controladores basados en optimización son adecuados para el diseño de sistemas de control que minimicen el consumo de energía de dichos sistemas mientras mantienen su productividad teniendo en cuenta los factores que los afectan. Primero, se presentan como las técnicas de control basadas en optimización pueden contribuir a hacer frente a los desafíos impuestos por la industria de manufactura. Con base en esta revisión, la industria manufacturera se clasifica por niveles, nivel de máquina, línea de proceso, y planta, para el diseño de controladores basados en optimización. Además, para diseñar estrategias de control que no afecten la productividad de la planta, se propone una clasificación para estos sistemas en función de las operaciones realizadas. Con base en estas clasificaciones, se diseñan estrategias de control que minimicen el consumo de energía de los sistemas de manufactura o los costos asociados a dicho consumo. A los niveles de maquina y línea, se diseñaron estrategias de control para minimizar el consumo de energía de los sistemas de manufactura con base en el enfoque de control predictivo basado en modelo. Las estrategias propuestas se basan en la gestión independiente de aquellos dispositivos que no están directamente relacionados con las operaciones de mecanizado. Por lo tanto, modelos de consumo de energía fueron necesarios para predecir el perfil del consumo de energía de estos sistemas y, a partir de esto, seleccionar los instantes de activación/desactivación de los dispositivos manipulados que minimicen el consumo de energía y garanticen el correcto funcionamiento de dichos sistemas. Dado que al nivel de línea el tamaño y la complejidad de estos sistemas aumenta, se propone a una estrategia de control basada en dos modos de control para reducir la carga computacional mediante la conmutación de un modo de control basado en optimización a un modo autónomo que no requiere optimización. Dada la necesidad de sistemas de manufactura flexibles y reconfigurables, estrategias de control no centralizadas se proponen para minimizar el consumo de dichos sistemas a los niveles más altos. Para este fin, los sistemas de manufactura se dividieron en subsistemas, y se diseñaron controladores locales de tipo cooperativo y no cooperativo usando métodos alternativos de dirección de multiplicadores para resolver los problemas de optimización. Además, debido a la naturaleza de los objetivos de control propuesto, se propuso una forma de establecer el consenso entre los controladores locales con dinámicas acopladas. Finalmente, a nivel de planta, se diseñan estrategias de control con base en el enfoque control predictivo basado en modelo económico para maximizar la rentabilidad de la planta. A este nivel, los objetivos de control se centran en determinar la programación de la producción óptima que deberán seguir las estrategias de control diseñadas a niveles más bajos. Así, la programación de la producción de la planta se determina teniendo en cuenta la demanda de piezas, el consumo de energía total, y el mercado energético con sus fluctuaciones. Las estrategias de control propuestas en esta tesis se probaron en simulación considerando diferentes escenarios diseñados con base en la operación real de una planta de fabricación de piezas automotrices.
Aquesta tesi es centra principalment en l’estudi de les tècniques de control basades en optimització per al disseny d’estratègies que contribueixin a millorar l’eficiència energètica dels sistemes de manufactura intel·ligents. Actualment, la indústria manufacturera està experimentant una transformació cap a sistemes de manufactura intel·ligents, flexibles i eficients energèticament, impulsada pels avenços en dispositius de mesura, gestió de dades i eines de comunicació i connectivitat. Aquesta transformació requereix que els sistemes de manufactura siguin modulars i reconfigurables per poder respondre als canvis en la programació de la producció i de la demanda i disseny de les peces mentre continuen operant de manera eficient i sostenible. Per tant, per tal d’assolir una indústria de manufactura m’és intel·ligent, s’han de dissenyar sistemes de control adequats que permetin complir els requeriments d’aquesta transformació, així com també minimitzar el consum d’energia i maximitzar la rendibilitat de la planta. En aquest sentit, els controladors basats en optimització i les arquitectures de control no centralitzat podrien ser adequats per al disseny de sistemes de control que contribueixin a minimitzar el consum d’energia total d’aquests sistemes mentre mantenen la seva productivitat i tenen en compte les restriccions operatives i els factors externs que afecten aquests sistemes. Per tant, mitjançant l’ús d’estratègies de control avançat, els sistemes de control poden ser degudament actualitzats per incloure la informació sobre els canvis en l’operació dels sistemes de manufactura, així com també la variació del mercat energètic per minimitzar els costos d’energia durant l’operació de la planta. Primer, en aquesta tesi, es presenten i discuteixen les estratègies actualment implementades en la indústria manufacturera per millorar la seva eficiència energètica. En base a aquesta revisió, s’identifiquen les principals bretxes de recerca en aquest camp i es discuteix com les tècniques de control basades en optimització poden contribuir a fer front als desafiaments imposats per la nova era de la indústria manufacturera (Industry 4.0). Recolzant-se en la revisió de la literatura, es proposa classificar la indústria manufacturera per nivells, considerant el nivell de màquina, línia de procés i planta, per al disseny de controladors basats en optimització. A més, per tal de dissenyar estratègies de control que no afectin la productivitat de la planta, és a dir, el nombre de peces processades per unitat de temps, els elements constitutius dels sistemes de manufactura també es classifiquen en dispositius de mecanitzat i perifèrics en funció de les operacions realitzades. Els elements de la primera classe corresponen a aquells que estan directament involucrats en les operacions de mecanitzat, mentre que els de la segona classe són aquells que s’encarreguen de proveir els recursos requerits pels dispositius de mecanitzat. Després, en base a aquesta classificació, es proposen estratègies de control en cada nivell per minimitzar el seu consum d’energia o els costos associats a aquest consum. Per als nivells de màquina i línia de procés, es dissenyen estratègies de control per minimitzar el consum d’energia dels sistemes de manufactura en base a l’enfocament de control predictiu basat en model. Les estratègies proposades es basen en la idea de gestionar de manera independent els dispositius (o sistemes) perifèrics per tal de no afectar el temps de processament de les màquines tot mantenint l’operació dels dispositius de mecanitzat. Per tant, calen models de consum d’energia per a predir el perfil de consum d’energia dels sistemes de manufactura i, en base a aquesta predicció, seleccionar els instants d’activació / desactivació per als dispositius manipulats a partir dels quals es minimitzi el consum d’energia total i es pugui garantir el correcte funcionament d’aquests sistemes. D’altra banda, atès que al nivell de línia de procés la mida i la complexitat dels sistemes de manufactura augmenta, es proposa una estratègia de control basada en dos modes de control per tal de reduir la càrrega computacional i dissenyar controladors que puguin ser implementats en temps real. En aquest sentit, tenint en compte que els sistemes de manufactura presenten un comportament diari, es proposa un algoritme per detectar la periodicitat d’aquests sistemes i, després, commutar a un mode de control autònom que no requereixi resoldre un problema d’optimització en línia. D’altra banda, donada la necessitat de sistemes de manufactura flexibles i reconfigurables, es proposen estratègies de control no centralitzades per minimitzar el consum d’energia dels sistemes de fabricació als nivells més alts. Amb aquesta finalitat, els sistemes de manufactura es divideixen en subsistemes, i es dissenyen controladors locals de tipus cooperatiu i no cooperatiu utilitzant mètodes alternatius de direcció de multiplicadors per resoldre els problemes d’optimització de manera distribuïda. A més, a causa de la naturalesa de l’objectiu de control proposat, el qual està enfocat en minimitzar el consum d’energia dels sistemes de manufactura, es proposa una forma d’establir el consens entre els controladors locals amb dinàmiques acoblades. Després, les estratègies de control proposades són extrapolades al nivell de planta usant objectius de tipus econòmic, i es comparen les arquitectures de control centralitzat i no centralitzat pel que fa al seu acompliment en llac¸ tancat i la càrrega computacional requerida per trobar una solució. Finalment, a nivell de planta, es dissenyen estratègies de control en base a l’enfocament de control predictiu basat en model econòmic per tal de maximitzar la rendibilitat de la planta i minimitzar els costos associats a la seva operació. Per tant, a aquest nivell, els objectius de control se centren a determinar la programació de la producció òptima de la planta que hauran de seguir les estratègies de control dissenyades als nivells més baixos. En aquest sentit, la programació de la producció de la planta és determinada tenint en compte la demanda actual de peces, el consum d’energia dels sistemes de manufactura i el mercat energètic amb les seves fluctuacions. Totes les estratègies de control proposades en aquesta tesi es proven en simulació considerant diferents escenaris basats en l’operació real d’una planta de fabricació de peces automotrius.

In this MSc thesis, it is investigated what parameters are relevant for describing energy consumption of heavy haul freight trains and how these can be used to develop key performance indicators (KPIs) for energy efficiency. The possible set of KPI is bounded by data available from energy meters used in electric IORE class locomotives hauling iron ore trains in northern Sweden. Furthermore, the analysis is only concerned with energy efficiency at the rolling stock level, excluding losses in the electric power supply network. Based on a literature study, parameters of interest describing driver, operations and rolling stock energy efficiency have been identified. By means of simulation, a parametric study is performed, simulating a 30 ton axle load iron ore train with 68 wagons. Train modelling input is obtained from technical documentation or estimated through measurements and statistical analysis. A multi-particle representation of the train is used to calculate gradient resistance for the simulation, which is also applied to determine the curve resistance.  Results show that the motion resistance is simulated quite accurately, while the lack of a driver model in the simulation tool leads to overestimation of energy consumption. Taking this into account, the importance of the driver for energy efficiency can still clearly be showcased in the parametric study. Especially on long steep downhill sections, prioritising the electric brakes over mechanical brakes is demonstrated to have a huge influence on net energy consumption, as has the amount of coasting applied. With the same driver behaviour in all simulations, the savings in specific energy from increasing axle load to 32.5 tons is estimated. Moreover, a comparison of increased train length and axle load points towards higher savings for the latter. In the end, parametric study results are used to recommend a structure for a monitoring system of energy efficiency based on a set of KPIs. With a sufficiently high sampling rate of energy meter data, it is adequate for calculating driver related KPIs and some additional KPIs. More KPIs can be tracked with access to additional data, e.g. cargo load.
I detta examensarbete undersöks vilka parametrar som är relevanta för att beskriva energiförbrukning för tunga godståg och hur dessa kan nyttjas för att utveckla nyckeltal för energieffektivitet. Antalet möjliga nyckeltal avgränsas till sådana som kan beräknas med data från elmätare som används i elektriska littera IORE lok som drar tunga malmtåg i norra Sverige. Vidare så tar analysen endast hänsyn till energieffektivitet för rullande materiel, vilket utesluter förluster i elektriska kraftmatningsnätet. Baserad på en litteraturstudie har relevanta parametrar som beskriver förare, drift och rullande materiel identifierats. Med hjälp av simuleringar av ett malmtåg med 30 tons axellast och 68 vagnar så utförs en parameterstudie. Indata för tågmodelleringen erhålls från teknisk dokumentation respektive uppskattas genom mätningar och statistisk analys. En representation av tåget som flertalet partiklar tillämpas i simulering för att beräkna lutningsmotståndet. Dessutom används densamma för att ta fram kurvmotståndet. Resultaten visar att gångmotstånd simuleras ganska exakt, medan avsaknad av en förarmodell i simuleringsvertyget leder till överskattad energiförbrukning. Med hänsyn tagen till detta så kan betydelsen av föraren för energieffektivitet fortfarande påvisas mycket tydligt i parameterstudien. I synnerhet i långa branta nedförsbackar har prioritering av den elektriska bromsen framför den mekaniska bromsen mycket stor påverkan på nettoenergiförbrukningen, likaväl som hur mycket tåget frirullar. Med samma förarbeteende i samtliga simuleringar har besparingar i specifik energiförbrukning kunnat uppskattats för en ökning av axellasten till 32,5 ton. Dessutom pekar en jämförelse av ökad tåglängd och axellast mot att sistnämnda ger större besparingar. Slutligen så har resultaten från parameterstudien nyttjats för att rekommendera en struktur för ett uppföljningssystem av energieffektivitet baserad på en uppsättning av nyckeltal. Med tillräckligt hög samplingsfrekvens på data från elmätare är den adekvat för att beräkna vissa nyckeltal, framförallt relaterad till förare. Fler nyckeltal kan följas upp med mer tillgänglig data så som lastvikter.

The electrification of the truck is crucial to meet the strategic vision of the European Union (EU) to contribute to net-zero greenhouse gas emissions for all sectors of the economy and society. The battery-electric truck is very efficient to reduce the emissions and has also a lower Total Cost of Ownership (TCO) compared to diesel trucks. Thus, the energy consumption of the battery-electric truck needs to be analysed in detail, and the differences in the conventional powertrain, recuperation by regenerative braking during driving and charging during standing, need to be considered. This master thesis aims to analyse the energy consumption of the battery-electric truck during driving and standing charging. For driving cycle simulation the Vehicle Energy Consumption calculation TOol (VECTO) and MATLAB are used. Different variations, such as payload, rolling resistance, air drag, and Power Take Off (PTO), are considered in the driving cycle simulation. The driving cycle simulation is verified by calculating the energy balance and compared with the on-road test results. For the standing charging simulation, MATLAB is used to analyse the charging loss with different battery packs and charging speeds. The results are shown with the Sankey diagram and other illustrative tools. Seen from the simulation results, the usable energy of the battery pack is enough for the truck to complete the designed driving cycle. The main loss in the powertrain is the Power Electronic Converter (PEC) and the electric machine. To increase the range and reduce energy loss, using a higher efficiency PEC and electric machine is an efficient method. For the charging simulation, the current Combined Charging System (CCS) standard charging station can charge the battery-electric truck with adequate voltage and reasonable charging time. The main loss during the charging comes from the charging station.
Elektrificering av lastbilen är avgörande för att uppfylla Europeiska Unionens (EUs) strategiska vision att bidra till nettonollutsläpp av växthusgaser för alla sektorer i samhället. Den batterielektriska lastbilen är väldigt effektiv för att reducera utsläppen och är också mer ekonomisk med en lägre Total Cost of Ownership (TCO) jämfört med diesel lastbilar. Således behöver energiförbrukningen för den batterielektriska lastbilen analyseras i detalj, och skillnaderna i den konventionella drivlinan, återhämtning genom regenerativ bromsning under körning och laddning, måste övervägas. Detta examensarbete syftar till att analysera energiförbrukningen för den batterielektriska lastbilen under körning och laddning. För körcykelsimuleringar används the Vehicle Energy Consumption calculation TOol (VECTO) och MATLAB. Olika variationer, såsom nyttolast, rullmotstånd, luftmotstånd och Power Take Off (PTO), beaktas i körcykelsimuleringen. Körcykelsimuleringen verifieras genom att beräkna energibalansen som jämförs med experimentella testresultat utförda på väg. För laddningssimuleringen används MATLAB för att analysera laddningsförlusten med olika batteripaket och laddningshastigheter. Resultaten visas med Sankey diagram och andra illustrativa verktyg. Simuleringsresultaten visar att batteripaketets användbara energi är tillräckligt för att lastbilen ska kunna slutföra den planerade körcykeln. Den största förlusten i drivlinan är kopplat till the Power Electronic Converter (PEC) och den elektriska maskinen. För att öka räckvidden och minska energiförlusten är det ett effektivt sätt att en använda PEC och en elektrisk maskin med högre effektivitet. För laddningssimuleringen kan den nuvarande stationen med Combined Charging System (CCS) standard ladda batteriladdaren med tillräcklig spänning och med rimlig laddningstid. Huvudförlusten under laddningen kommer från laddstationen.

Fuel consumption reduction is one of the main challenges in the automotiveindustry due to its economical and environmental impacts as well as legalregulations. While fuel consumption reduction is important for all vehicles,it has larger benefits for commercial ones due to their long operational timesand much higher fuel consumption. Optimal control of multiple energy buffers within the vehicle proves aneffective approach for reducing energy consumption. Energy is temporarilystored in a buffer when its cost is small and released when it is relativelyexpensive. An example of an energy buffer is the vehicle body. Before goingup a hill, the vehicle can accelerate to increase its kinetic energy, which canthen be consumed on the uphill stretch to reduce the engine load. The simplestrategy proves effective for reducing fuel consumption. The thesis generalizes the energy buffer concept to various vehicular componentswith distinct physical disciplines so that they share the same modelstructure reflecting energy flow. The thesis furthermore improves widely appliedcontrol methods and apply them to new applications. The contribution of the thesis can be summarized as follows: • Developing a new function to make the equivalent consumption minimizationstrategy (ECMS) controller (which is one of the well-knownoptimal energy management methods in hybrid electric vehicles (HEVs))more robust. • Developing an integrated controller to optimize torque split and gearnumber simultaneously for both reducing fuel consumption and improvingdrivability of HEVs. • Developing a one-step prediction control method for improving the gearchanging decision. • Studying the potential fuel efficiency improvement of using electromechanicalbrake (EMB) on a hybrid electric city bus. • Evaluating the potential improvement of fuel economy of the electricallyactuated engine cooling system through the off-line global optimizationmethod. • Developing a linear time variant model predictive controller (LTV-MPC)for the real-time control of the electric engine cooling system of heavytrucks and implementing it on a real truck.

QC 20160128

The potential of carbon emission regulations applied to an individual building will encourage building owners to purchase utility-provided green power or to employ onsite renewable energy generation. As both cases are based on intermittent renewable energy sources, demand side control is a fundamental precondition for maximizing the effectiveness of using renewable energy sources. Such control leads to a reduction in peak demand and/or in energy demand variability, therefore, such reduction in the demand profile eventually enhances the efficiency of an erratic supply of renewable energy. The combined operation of active thermal energy storage and passive building thermal mass has shown substantial improvement in demand-side control performance when compared to current state-of-the-art demand-side control measures. Specifically, "model-based" optimal control for this operation has the potential to significantly increase performance and bring economic advantages. However, due to the uncertainty in certain operating conditions in the field its control effectiveness could be diminished and/or seriously damaged, which results in poor performance. This dissertation pursues improvements of current demand-side controls under uncertainty by proposing a robust supervisory demand-side control strategy that is designed to be immune from uncertainty and perform consistently under uncertain conditions. Uniqueness and superiority of the proposed robust demand-side controls are found as below: a. It is developed based on fundamental studies about uncertainty and a systematic approach to uncertainty analysis. b. It reduces variability of performance under varied conditions, and thus avoids the worst case scenario. c. It is reactive in cases of critical "discrepancies" observed caused by the unpredictable uncertainty that typically scenario uncertainty imposes, and thus it increases control efficiency. This is obtainable by means of i) multi-source composition of weather forecasts including both historical archive and online sources and ii) adaptive Multiple model-based controls (MMC) to mitigate detrimental impacts of varying scenario uncertainties. The proposed robust demand-side control strategy verifies its outstanding demand-side control performance in varied and non-indigenous conditions compared to the existing control strategies including deterministic optimal controls. This result reemphasizes importance of the demand-side control for a building in the global carbon economy. It also demonstrates a capability of risk management of the proposed robust demand-side controls in highly uncertain situations, which eventually attains the maximum benefit in both theoretical and practical perspectives.

This thesis addressed the need for improved analysis and interpretation of primary meter half-hourly energy consumption data. The current work offers a novel benchmarking technique that was tested for 6 types of municipal buildings. This approach is different from conventional annual benchmarking mainly because it uses electricity and gas data in half-hourly periods, together with outside temperature data. A survey to European local authorities’ metering and monitoring practices was conducted in order to assess municipal energy managers' current procedures and needs in terms of data analysis to assess building energy performance and to identify potential energy saving opportunities. The benchmarking approach was developed considering the energy managers’ needs, but also the state-of the art in terms of building energy monitoring techniques, particularly building energy signatures, and the analysis techniques used on electricity grid demand forecasting. The benchmarking approach is based on the use of a metric composed of several indicators that are related to the load demand shape profile and the building energy signature. The comparison of indicators for buildings of the same type using standard scores identifies uncommon load demand profile characteristics and/or gas dependency on outside temperature in specific buildings. The metric is able to support the identification of potential energy wastage, which is linked to the detection of opportunities to save energy. The benchmarking technique was tested in 81 municipal building owned by Leicester City Council. This methodology can be applied to any non-domestic building equipped with primary meters for registering half-hourly electricity and gas consumption. In theory, this approach can also be applied to residential buildings, and to other short time series data types, for example quarter-hourly or 10 minutes interval data. The main contribution of this thesis is to improve the objectivity of building primary meter half-hourly electricity and gas consumption data analysis and interpretation by using quantitative parameters, instead of subjective visualisation techniques. The interpretation of building consumption data in short time series periods can now be streamlined, automated and perhaps incorporated in existing energy analysis software. This thesis raises questions that can lead to future research projects aiming to improve the metric and also to enlarge the scope of its application to national and European scale, to other building types and to other utilities.

The study investigated critical factors that affect office building energy performance in Nigeria compared to the UK; and developed an assessment and benchmarking framework for identifying appropriate operational, technical and behavioural solutions to improve energy performance of existing buildings. The mixed methods, multiple-case study approach was adopted for collecting energy used data from selected ten existing office buildings in both countries. A literature review was used to established worldwide view on factors influencing building energy performance. The selected factors were transformed into theoretical framework variables. These variables were further translated into an operational sustainability as an audit tool for establishing initial cases’ energy performance assessment. Operational energy data from case buildings were collected via: electricity bills, meter readings and fuels’ receipts/ invoices. Also, three online questionnaire surveys: a post occupancy evaluation for the assessment of comfort and energy performance of the ten buildings; a survey of facilities managers’ perception of managerial, operational and technical issues; and a model validation survey for confirmation of established factors from earlier two surveys, was employed. Likewise, a one-on-one semi-structured interview was utilised for owners/ managing directors and facilities managers of case buildings. Whilst, Structural equation modelling, analysis based on the validation survey, was employed to examine the dependencies and interdependencies of the critical factors. The IBM, SPSS 22 and AMOS 23 were used for the exploratory factor analysis and confirmatory factor analysis based on maximum likelihood estimations; which produced validated measurement and structural models. The study identified 52 critical factors that were transformed into 17 most critical factors impacting building energy performance in Nigeria and UK. The model identified new indicators for building energy performance; and established causality between building energy performance, management policy, operations and strategic driver as standard metrics for building energy efficiency assessment and benchmark. Likewise, it established a strong network of strategic drivers underpinned by strategic sustainability policy/ facilities management as mediator. The Nigeria buildings’ performance was found to depend largely on the context in which they operate in apart from the weather. Whereas, validity of climate variability as a critical factor of the UK case buildings’ energy performance was established. Institutionalised regulatory framework is suggested for Nigeria and sub-Saharan Africa countries as a control measure. The building energy performance model and its operational sustainability tool could be used as energy assessment and benchmark. It serves as a dashboard that encapsulates the energy efficiency performance and the absolute impacts of intervening factors. Finally, it presents insight into a critical path for intervention schemes as implemented; and the use of the strategic sustainability policy/ facilities management as mediation for improving building energy performance.

Till följd av EU:s miljömål 2020 har svenska statliga och kommunala miljömål satts upp för att minska energianvändningen. EU:s 2020-mål syftar till att minska energianvändningen i Europa med 20 procent fram till år 2020 från det att målet sattes upp 2010. På kommunal nivå har detta inneburit att fastighetsbolag, privata men främst kommunala aktörer, har tvingats att se över sin energianvändning. Arbetet i denna rapport föranleds av en av de besparingsåtgärder som har vidtagits nämligen att komplettera befintliga fjärrvärmeuppvärmningssystem med värmepumpar. Uppdraget avser utveckling och utvärdering av en beräkningsmodell för denna typ av system. För att skapa ett pålitligt beslutsunderlag i arbetet med att minska energianvändningen krävs välgrundade beräkningar. Befintliga beräkningsmodeller lägger stor vikt vid ekonomiska faktorer och tar därmed liten hänsyn till tekniska aspekter och omgivande faktorer. Målet med arbetet har varit att skapa en beräkningsmodell vilken i större utsträckning speglar den faktiska situationen och påvisar ett mer välgrundat beslutsunderlag. Arbetet inleddes med en litteraturstudie vilken innefattade relevant forskning och grundläggande fakta om värmepumpar och fjärrvärmesystem. Beräkningsmodellen har utvecklats löpande under projektet där nya funktioner samt ny information ständigt tillkommit. Projektet har resulterat i en fungerande beräkningsmodell vilken innefattar fler parametrar än tidigare motsvarigheter. Tillsammans med uppdragsgivaren sattes tre olika mål upp för beräkningsmodellen, dessa var:  Att på ett intuitivt sätt presentera information och data.  Att skapa en transparent struktur vilken är enkel att följa.  Att skapa ett flöde genom modellen vilket följer en önskad arbetsgång. Dessa mål anses ha blivit uppfyllda, dock finns möjlighet till vidareutveckling. Modellen skulle exempelvis kunna kompletteras med mer automatiserade optimeringsberäkningar och justeras för att hantera andra typer av systemlösningar.
As a result of the EU's environmental 2020 goal, the Swedish government and municipal boards has put up internal goals to reduce energy consumption. EU 2020 goal aims to reduce energy consumption in Europe by 20 percent by the year 2020 from that the target was set in 2010. At the municipal level, this has meant that private but mainly public housing properties have been required to review their energy use. The work in this report is based on one of the savings measures that have been taken, which is to supplement existing district heating systems with exhaust air heat pumps. The project aims to develop a calculation model for evaluation and computation for this type of system. To create a reliable basis for decisions, efforts were made to produce just calculations. Existing computational models are focused on economic factors and thus takes little consideration of technological aspects and environmental factors. The aim of the work has been to create a computational model which more closely reflects the real situation and demonstrates a more informative decision basis. The work began with a literature review which included relevant research and basic facts about exhaust air heat pumps and district heating systems. The calculation model has been developed continuously during the project in which new functions and new information constantly has been added. The project has resulted in a working computational model which includes many more parameters than previous reviewed models. Together with the client three sub goals were set up for the model, these were:  To intuitively present information and data.  To create a transparent structure which is simple to follow.  To create a flow through the model, which follows a desired work process. These goals are considered to have been fulfilled in the developed calculation model, however, it's possible to further develop this model. The model could, for example, be supplemented with more automated optimization calculations and be adjusted to handle other kinds of heating systems.

The use of Lithium-ion batteries in the automotive industry has increased tremendously in the last few years. The anticipated increase in demand of lithium to power electric and hybrid cars has prompted researchers to examine the long term sustainability lithium as a transportation resource. To provide a better understanding of future availability, this thesis presents a systems framework for the key processes and materials and energy flows involved in the electric vehicle lithium-ion battery life cycle, on a global scale. This framework tracks the flow of lithium and energy inputs and outputs from extraction, to production, to on road use, and all the way to end of life recycling and disposal. This process flow model is the first step in developing a life cycle analysis model for lithium that will eventually help policymakers assess the future role of lithium battery recycling, and at what point in time establishing a recycling infrastructure becomes imminent.