Dissertations / Theses on the topic 'Energy management in building'

Buildings are responsible for a significant portion of energy consumption worldwide. Intelligent buildings have been devised as a potential solution, where energy consumption and building use are harmonised. At the heart of the intelligent building is the building energy management system (BEMS), the central platform which manages and coordinates all the building monitoring and control subsystems, such as heating and lighting loads. There is often a disconnect between the BEMS and the building it is installed in, leading to inefficient operation, due to incongruous commissioning of sensors and control systems. In these cases, the BEMS has a lack of knowledge of the building form and function, requiring further complex optimisation, to facilitate efficient all year round operation. Flawed BEMS configurations can then lead to ‘sick buildings’. Recently, building energy performance simulation (BEPS) has been viewed as a conceptual solution to assist in efficient building control. Building energy simulation models offer a virtual environment to test many scenarios of BEMS operation strategies and the ability to quickly evaluate their effects on energy consumption and occupant comfort. Challenges include having an accurate building model, but recent advances in building information modelling (BIM) offer the chance to leverage existing building data, which can be translated into a form understood by the building simulator. This study will address these challenges, by developing and integrating a BEMS, with a BIM for BEPS assisted predictive control, and assessing the outcome and potential of the integration.

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.

The level of concern regarding the total energy consumption in new building clusters and urban districts (BCDs) has increased recently. Rising living standards have led to a significant increase in building energy consumption over the past few decades. A great potential for energy savings exists through energy quality management (EQM) for new BCDs. Quality of energy measures the useful work potential of certain energy. EQM in this thesis is defined as reducing energy demand, applying distributed renewable energy sources, and utilizing energy technology in sustainable way. According to this definition, tasks of EQM include energy supply system optimization and energy demand prediction. Based on EQM, the optimization of BCDs’ energy supply systems aims to search for the most appropriate scenario, which is a trade-off between various aspects, such as energy performance and environmental impacts as well as system reliability. A novel multi-objective optimization approach for new BCDs is established in this thesis. Optimization algorithm is known as Genetic Algorithm (GA), which is used to address non-linear optimization problems. Two case studies are included in this thesis: the U.K. eco-town residential BCDs case and the Norway office BCDs case. The U.K. case examines the application possibility of the approach in practical design. Optimization objectives involved in this case are the life-cycle global warming potential of the system and the system exergy efficiency. The total life-cycle global warming potential is minimized while the exergy efficiency is maximized. Different types of energy supply system scenarios are recommended with different optimization objective combinations (equal-importance, slightly exergy efficiency-oriented and slightly environment-oriented). The results show that the proposed approach can feasibly be an optimal design tool in practical use. To provide deeper insights into the problem, the Norway case checks the expansibility of inserting additional objectives into the approach. Loss of Power Supply Probability (LPSP), which is one of the system reliability indicators, is additionally included in the optimization objectives. For this case, the approach guarantees the optimal scenarios that cannot exceed the desired LPSP with minimum life-cycle global warming potential and maximum exergy efficiency. Optimal scenarios with different desired LPSP values (0, 1%, and 5%) are compared. Comparison results demonstrate that optimal scenarios change significantly along with variations of the desired LPSP values. Therefore, system reliability is proven as one of the most important objectives for renewable energy system optimization. In the future, this approach can be applied to complex problems with more objectives. Besides energy supply system optimization, an effective and precise BCDs energy demand model is needed. This model should be capable of providing reliable inputs (energy demand and load profiles) for energy supply system optimization and reducing unnecessary energy consumption. In principle, energy demand in BCDs is a complex task because numerous design criteria influence energy performance, which is hard to plan and pre-calculate. Establishing such a model would require a thorough decision base that prioritizes these design criteria and generally distinguishes the more important criteria from the less important ones. The study uses general survey aims to collect and identify the design criteria that affect the BCDs energy demand model and to evaluate the priorities of each criterion using the fuzzy Analytical Hierarchy Process (AHP) method. Four main criteria – location, building characteristics, government, and outdoor surrounding characteristics – are established, along with 13 secondary criteria. The results show that the use of the AHP method can accurately guide the energy demand model and automatically rank significant criteria. The method can provide the weighting value for each criterion as well as the relative ranking for the energy demand model. This thesis aims to provide a systematic and holistic EQM method for BCDs energy system design at the beginning of the decision-making stage.

QC 20130221

There is widespread acceptance of the need to reduce energy consumption within the built environment. Despite this, there are often large discrepancies between the energy performance aspiration and operational reality of modern buildings. The application of existing mitigation measures appears to be piecemeal and lacks a whole-system approach to the problem. This Engineering Doctorate aims to identify common reasons for performance discrepancies and develop a methodology for risk mitigation. Existing literature was reviewed in detail to identify individual factors contributing to the risk of a building failing to meet performance aspirations. Risk factors thus identified were assembled into a taxonomy that forms the basis of a methodology for identifying and evaluating performance risk. A detailed case study was used to investigate performance at whole-building and sub-system levels. A probabilistic approach to estimating system energy consumption was also developed to provide a simple and workable improvement to industry best practice. Analysis of monitoring data revealed that, even after accounting for the absence of unregulated loads in the design estimates, annual operational energy consumption was over twice the design figure. A significant part of this discrepancy was due to the space heating sub-system, which used more than four times its estimated energy consumption, and the domestic hot water sub-system, which used more than twice. These discrepancies were the result of whole-system lifecycle risk factors ranging from design decisions and construction project management to occupant behaviour and staff training. Application of the probabilistic technique to the estimate of domestic hot water consumption revealed that the discrepancies observed could be predicted given the uncertainties in the design assumptions. The risk taxonomy was used to identify factors present in the results of the qualitative case study evaluation. This work has built on practical building evaluation techniques to develop a new way of evaluating both the uncertainty in energy performance estimates and the presence of lifecycle performance risks. These techniques form a risk management methodology that can be applied usefully throughout the project lifecycle.

With the ever-growing concerns of environmental and climate concerns for energy consumption in our society, it is crucial to develop novel solutions that improve the efficient utilization of distributed energy resources for energy efficiency and demand response (DR). As such, there is a need to develop targeted energy programs, which not only meet the requirement of energy goals for a community but also take the energy use patterns of individual households into account. To this end, a sound understanding of the energy behavior of customers at the neighborhood level is needed, which requires operational analytics on the wealth of energy data from customers and devices. In this dissertation, we focus on data-driven solutions for customer energy behavior characterization with applications to distributed energy management and flexibility provision. To do so, the following problems were studied: (1) how different customers can be segmented for DR events based on their energy-saving potential and balancing peak and off-peak demand, (2) what are the opportunities for extracting Time-of-Use of specific loads for automated DR applications from the whole-house energy data without in-situ training, and (3) how flexibility in customer demand adoption of renewable and distributed resources (e.g., solar panels, battery, and smart loads) can improve the demand-supply problem. In the first study, a segmentation methodology form historical energy data of households is proposed to estimate the energy-saving potential for DR programs at a community level. The proposed approach characterizes certain attributes in time-series data such as frequency, consistency, and peak time usage. The empirical evaluation of real energy data of 400 households shows the successful ranking of different subsets of consumers according to their peak energy reduction potential for the DR event. Specifically, it was shown that the proposed approach could successfully identify the 20-30% of customers who could achieve 50-70% total possible demand reduction for DR. Furthermore, the rebound effect problem (creating undesired peak demand after a DR event) was studied, and it was shown that the proposed approach has the potential of identifying a subset of consumers (~5%-40% with specific loads like AC and electric vehicle) who contribute to balance the peak and off-peak demand. A projection on Austin, TX showed 16MWh reduction during a 2-h event can be achieved by a justified selection of 20% of residential customers. In the second study, the feasibility of inferring time-of-use (ToU) operation of flexible loads for DR applications was investigated. Unlike several efforts that required considerable model parameter selection or training, we sought to infer ToU from machine learning models without in-situ training. As the first part of this study, the ToU inference from low-resolution 15-minute data (smart meter data) was investigated. A framework was introduced which leveraged the smart meter data from a set of neighbor buildings (equipped with plug meters) with similar energy use behavior for training. Through identifying similar buildings in energy use behavior, the machine learning classification models (including neural network, SVM, and random forest) were employed for inference of appliance ToU in buildings by accounting for resident behavior reflected in their energy load shapes from smart meter data. Investigation on electric vehicle (EV) and dryer for 10 buildings over 20 days showed an average F-score of 83% and 71%. As the second part of this study, the ToU inference from high-resolution data (60Hz) was investigated. A self-configuring framework, based on the concept of spectral clustering, was introduced that automatically extracts the appliance signature from historical data in the environment to avoid the problem of model parameter selection. Using the framework, appliance signatures are matched with new events in the electricity signal to identify the ToU of major loads. The results on ~1500 events showed an F-score of >80% for major loads like AC, washing machine, and dishwasher. In the third study, the problem of demand-supply balance, in the presence of varying levels of small-scale distributed resources (solar panel, battery, and smart load) was investigated. The concept of load complementarity between consumers and prosumers for load balancing among a community of ~250 households was investigated. The impact of different scenarios such as varying levels of solar penetration, battery integration level, in addition to users' flexibility for balancing the supply and demand were quantitatively measured. It was shown that (1) even with 100% adoption of solar panels, the renewable supply cannot cover the demand of the network during afternoon times (e.g., after 3 pm), (2) integrating battery for individual households could improve the self-sufficiency by more than 15% during solar generation time, and (3) without any battery, smart loads are also capable of improving the self-sufficiency as an alternative, by providing ~60% of what commercial battery systems would offer. The contribution of this dissertation is through introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility of the aggregate daily energy load profiles for a community. When combined, the findings of this research can serve to the field of utility-scale energy analytics for the integration of DR and improved reshaping of network energy profiles (i.e., mitigating the peaks and valleys in daily demand profiles).
Doctor of Philosophy
Buildings account for more than 70% of electricity consumption in the U.S., in which more than 40% is associated with the residential sector. During recent years, with the advancement in Information and Communication Technologies (ICT) and the proliferation of data from consumers and devices, data-driven methods have received increasing attention for improving the energy-efficiency initiatives. With the increased adoption of renewable and distributed resources in buildings (e.g., solar panels and storage systems), an important aspect to improve the efficiency by matching the demand and supply is to add flexibility to the energy consumption patterns (e.g., trying to match the times of high energy demand from buildings and renewable generation). In this dissertation, we introduced data-driven solutions using the historical energy data of consumers with application to the flexibility provision. Specific problems include: (1) introducing a ranking score for buildings in a community to detect the candidates that can provide higher energy saving in the future events, (2) estimating the operation time of major energy-intensive appliances by analyzing the whole-house energy data using machine learning models, and (3) investigating the potential of achieving demand-supply balance in communities of buildings under the impact of different levels of solar panels, battery systems, and occupants energy consumption behavior. In the first study, a ranking score was introduced that analyzes the historical energy data from major loads such as washing machines and dishwashers in individual buildings and group the buildings based on their potential for energy saving at different times of the day. The proposed approach was investigated for real data of 400 buildings. The results for EV, washing machine, dishwasher, dryer, and AC show that the approach could successfully rank buildings by their demand reduction potential at critical times of the day. In the second study, machine learning (ML) frameworks were introduced to identify the times of the day that major energy-intensive appliances are operated. To do so, the input of the model was considered as the main circuit electricity information of the whole building either in lower-resolution data (smart meter data) or higher-resolution data (60Hz). Unlike previous studies that required considerable efforts for training the model (e.g, defining specific parameters for mathematical formulation of the appliance model), the aim was to develop data-driven approaches to learn the model either from the same building itself or from the neighbors that have appliance-level metering devices. For the lower-resolution data, the objective was that, if a few samples of buildings have already access to plug meters (i.e., appliance level data), one could estimate the operation time of major appliances through ML models by matching the energy behavior of the buildings, reflected in their smart meter information, with the ones in the neighborhood that have similar behaviors. For the higher-resolution data, an algorithm was introduced that extract the appliance signature (i.e., change in the pattern of electricity signal when an appliance is operated) to create a processed library and match the new events (i.e., times that an appliance is operated) by investigating the similarity with the ones in the processed library. The investigation on major appliances like AC, EV, dryer, and washing machine shows the >80% accuracy on standard performance metrics. In the third study, the impact of adding small-scale distributed resources to individual buildings (solar panels, battery, and users' practice in changing their energy consumption behavior) for matching the demand-supply for the communities was investigated. A community of ~250 buildings was considered to account for realistic uncertain energy behavior across households. It was shown that even when all buildings have a solar panel, during the afternoon times (after 4 pm) in which still ~30% of solar generation is possible, the community could not supply their demand. Furthermore, it was observed that including users' practice in changing their energy consumption behavior and battery could improve the utilization of solar energy around >10%-15%. The results can serve as a guideline for utilities and decision-makers to understand the impact of such different scenarios on improving the utilization of solar adoption. These series of studies in this dissertation contribute to the body of literature by introducing data-driven solutions/investigations for characterizing the energy behavior of households, which could increase the flexibility in energy consumption patterns.

With an increasing awareness of energy consumption and CO 2emission in the population, several initiatives to reduce CO2emissions have been presented all around the world. The main part of these initiatives is a reduction of the energy consumption for existing buildings, while the others concern the building of eco-districts with low-energy infrastructures and even zero-energy infrastructures. In this idea of reducing the energy consumption and of developing new clean areas, this master thesis will deal with the high energy quality services for new urban districts. In the scope of this master thesis project, the new concept of sustainable cities and of clusters of buildings will be approached in order to clearly understand the future challenges that the world’s population is going to face during this century. Indeed, due to the current alarming environmental crisis, the need to reduce human impacts on the environment is growing more and more and is becoming inescapable. We will present a way to react to the current situation and to counteract it thanks to new clean technologies and to new analysis approaches, like the exergy concept. Through this report, we are going to analyze the concepts of sustainable cities and clusters of buildings as systems, and focus on their energy aspects in order to set indoor climate parameters and energy supply parameters to ensure high energy quality services supplies to high performance buildings. Thanks to the approach of the exergy concept, passive and active systems such as nocturnal ventilation or floor heating and cooling systems have been highlighted in order to realize the ‘energy saving’ opportunities that our close environment offers. This work will be summarized in a methodology that will present a way to optimize the energy use of all services aspects in a building and the environmental friendly characteristics of the energy resources mix, which will supply the buildings’ low energy demands.

The electrical grid can no longer be considered a unidirectional means of distributing energy from conventional plants to the final users, but a Smart Grid, where strong interaction between producers and users takes place. In this context, the importance of independent renewable generation is constantly increasing, and new tools are needed in order to reliably manage conventional power plant operation, grid balancing, real-time unit dispatching, demand constraints and energy market requirements. This dissertation is focused on two aspects of this general problem: cost-optimal management of smart buildings in a Demand-Response framework, and estimation of photovoltaic generation forecasting models. In the first part of this thesis a novel Model Predictive Control approach for integrated management of HVAC, electrical and thermal storage, and photovoltaic generation in building is presented. The proposed methodology also considers participation of the building in a Demand-Response program, which allows the consumer to become an active player in the electricity system. The related optimization problems turn out to be computationally appealing, even uncertainty sources is also addressed by means of a two-step procedure. The second part deals with the problem of estimating photovoltaic generation forecasting models in scenarios where measurements of meteorological variables (i.e., solar irradiance and temperature) at the plant site are not available. This scenario is relevant to electricity network operation, when a large number of photovoltaic plants are deployed in the grid. In particular, two methods have been developed. The first approach makes use of raw cloud cover data provided by a weather service combined with power generation measurements to estimate the parameters of a novel class of models. The second approach is based on a set of tests performed on the generated power time series aimed at detecting data portions that were generated under clear sky conditions. These data are then used for fit the parameters of the PVUSA model to the theoretical clear sky irradiance. All the methods covered in this thesis have been extensively validated either using industry-standard simulation frameworks or via experiments performed on real data.

This report is the result of a minor field study (MFS) performed in the Dominican Republic. The country strongly relies on energy sources from adjacent countries, currently the nation depends on fossil fuel for 86 % of its electricity generation and in 2011, 8.6 % of its GDP was spent on fossil fuel imports. Since the country has a growing middle-class the demand for energy is increasing intensively and makes the necessity of a transition to a more sustainable energy system crucial for future growth. This project mainly focuses on the energy use in buildings and to analyze the situation, Emilio Rodríguez Demorizi library at Instituto Tecnológico de Santo Domingo was taken as case study. In order to evaluate the current situation an energy audit was performed, for simulations RETScreen was used, an energy management tool that enables modeling of a buildings energy use. The program allows the user to inspect the current consumption and demands and later propose a solution in order to make the investigated object more efficient. Components such as, lighting, electric equipment, ventilation and climate system are taken into account. Also the behavioral needs of the occupants in the building have been analyzed. However, no technical solution will actually be implemented and the study will be restricted to the area of Santo Domingo. The result of the study showed that more electricity was used in the afternoon compared to mornings and evenings. The biggest factor for this increase is due to the use of climate systems which accounts for 71% of the yearly electricity consumption. An improved case was suggested, where upgrades were made in climate systems and electrical equipment and simulations in RETScreen show that electricity use can be decreased by 56 % if the proposed case was to be implemented. An investigation has been made on a proposed solar project, it would only cover 4% of the buildings annual electricity need in the current state and 10 % of the annual need in the proposed case, a conclusion has been made that at the moment the solar project would not be financially viable, considering the size of the investments that would be needed. However, in the future when more advanced technology is affordable and available it might be worth bearing in mind. Furthermore, the behavioral needs of the occupants in the building substantially contribute to the energy utilization in the building, major reductions can be made if cooling which is the main factor for electricity consumption, was used only when and where needed.
Detta kandidatexamensarbete är resultatet av en MFS-studie utförd i Dominikanska Republiken. Landet är starkt beroende av energikällor från angränsande länder, för närvarande är 86 % av elektricitet producerad från fossila bränslen, 2011 spenderade landet 8,6 % av dess BNP på bränsle import. Då landet har en växande medelklass ökar efterfrågan på energi och gör behovet av en övergång till hållbar energiteknik avgörande för framtida tillväxt. Denna rapport fokuserar huvudsakligen på energianvändning i byggnader. Som fallstudie har biblioteket Emilio Rodríguez Demorizi på Instituto Tecnológico de Santo Domingo använts. För att utvärdera situationen har en energikartläggning genomförts på byggnaden, för att analysera resultatet har energihanteringsverktyget RETScreen använts, ett verktyg som möjliggör modellering av byggnaders energianvändning. Programmet ger användaren möjligheten att inspektera nuvarande användning och krav och senare även föreslå förbättringar inom energieffektivisering, minskningar av växthusgaser och även finansiella förbättringar. Komponenter såsom belysning, elektrisk utrustning, ventilation och klimatsystem beaktas. Även beteendet hos de som använder byggnaden är en viktig faktor att ta hänsyn till. Ingen faktiskt lösning kommer att föreslås och studien kommer att begränsas till Santo Domingo-området. Resultatet visar att elanvändningen är högre på eftermiddagen jämfört med på kvällen. Den största faktorn till denna ökning är klimatsystemet som står för 71 % av den årliga elektricitetsförbrukningen. Ett förbättrat fall har föreslagits där uppgraderingar i klimatsystem och elektronisk utrustning införts. Simuleringar i RETScreen visar att förbrukningen kan minskas med 56 % om det förbättrade fallet implementeras. En utredning har genomförts på ett förslaget solpanelsprojekt, i byggnadens nuvarande tillstånd så skulle det täcka 4 % av den årliga elanvändningen, för det föreslagna fallet skulle det täcka ca 10 %. Slutsatsen har dragits att projektet inte är ekonomiskt hållbart, med tanke på de enorma investeringar som krävs. Däremot så är det något man bör se över när mer effektiv utrustning finns tillgänglig. Vidare så är beteendet av de som använder byggnaden en stor orsak till den höga energianvändningen. Betydande besparingar kan göras om klimatanläggningen, vilket är det största bidraget till den höga elanvändning, används endast var och när det är i behov.

The introduction of building management systems in large buildings have improved the control of building services and provided energy savings. However, current building management systems are limited by the physical level of integration of the building's services and the lack of intelligence provided in the control algorithms. This thesis proposes a new approach to the design and operation of building management systems using rule-based artificial intelligence techniques. The main aim of is to manage the services in the building in a more co-ordinated and intelligent manner than is possible by conventional techniques. This approach also aims to reduce the operational cost of the building by automatically tuning the energy consumption in accordance with occupancy profile of the building. A rule-based design methodology is proposed for building management systems. The design adopts the integrated structure made possible by the introduction of a common communications network for building services. The 'intelligence' is coded in the form of rules in such a way that it is both independent of any specific building description and easy to facilitate subsequent modification and addition. This is achieved using an object-oriented approach and classifying the range of data available into defined classes. The rules are divided into two knowledge-bases which are concerned with the building's control and its facilities management respectively. A wide range of rule-based features are proposed to operate on this data structure and are classified in terms of the data classes on which they operate. The concepts presented in this thesis were evaluated using software simulations, mathematical analysis and some hardware implementation. The conclusions of this work are that a rule-based building management system could provide significant enhancements over existing systems in terms of energy savings and improvements for both the building's management staff and its occupants.

The environmental and economic consequences of climate change are severe and are being exacerbated by increased global carbon emissions. In the United States, buildings account for over 40% of all domestic and 7.4% of all global CO2 emissions and therefore represent an important target for energy conservation initiatives. Even marginal energy savings across all buildings could have a profound effect on carbon emission mitigation. In order to realize the full potential of energy savings in the building sector, it is essential to maximize the energy efficiency of both buildings and the behavior of occupants who occupy them. In this vein, systems that collect and communicate building energy-use information to occupants (i.e. eco-feedback systems) have been demonstrated to motivate building occupants to significantly reduce overall building energy consumption. Furthermore, advancements in building sensor technologies and data processing capabilities have enabled the development of advanced eco-feedback systems that also allow building occupants to share energy-use data with one another and to collectively act to reduce energy consumption. In addition to monitoring building occupant energy-use, these systems are capable of collecting data about specific conservation actions taken by occupants and their interactions with different features of the eco-feedback system. However, despite recent advancements in eco-feedback and building sensor technologies, very few systems have been specifically designed to enable research on the effectiveness of different behavior-based energy conservation strategies in commercial buildings. Consequently, very little research has been conducted on how access to such systems impacts the energy-use behavior of building occupants. In this dissertation, I describe how my research over the past three years has advanced an understanding of how eco-feedback systems can impact the energy-use behavior of commercial building occupants. First, I present a novel eco-feedback system that I developed to connect building occupants over energy-use data and empower them to conserve energy while also collecting data that enables controlled studies to quantify the impacts of a wide variety of energy conservation strategies. Next, I present a commercial building study in which this eco-feedback system was used to investigate the effects of organizational network dynamics on the energy-use of individuals. I then introduce a new set of metrics based on individual energy-use data that enables the classification of individuals and building occupant networks based on their energy-use efficiency and predictability. I describe the principles behind the construction of these metrics and demonstrate how these quantitative measures can be used to increase the efficacy of behavior-based conservation campaigns by enabling targeted interventions. I conclude the dissertation with a discussion about the limitations of my research and the new research avenues that it has enabled.
Ph. D.

This thesis investigates the relationship between facilities management (FM) and design on construction projects in the Middle East region. Input provided by facilities management, the party responsible for developing and maintaining a facility support services system, is found to be vital for enhancing the design’s supportive function and preventing operation problems from occurring during the occupancy phase. An extensive literature review is undertaken to study both FM and design practices, their responsibilities during a facility’s life cycle and the types of services each discipline provides. The nature of facilities management input into design is also explored. The review of literature reveals a limited integration among the facilities management and design professions, a problem mainly caused by the nature of project delivery processes that prevent external input into the design. Accordingly, the research aims of investigating integration in practice and determining the actions to be taken to improve the situation are developed. The survey method is chosen for carrying out the research, involving FM and design practitioners. Semi-structured interviews are utilised for collecting qualitative rich information on professional views and experiences. Research findings disclose the nature of the relationship between facilities managers and designers as well as the status of their current collaboration on construction projects in the Middle East. FM-related concerns occurring during the occupancy phase are identified to show the prominence of their consideration during the design stage. This research also presents the various benefits of achieving successful integration and identifies the different means which could be implemented to improve the process of integration and avoid negative consequences currently affecting facilities and their occupants.

Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector. Energy management tools have been developed to promise appropriate energy savings. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios, while building automation systems (BAS) works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. By integrating the energy simulated data and actual operational data, the accuracy of a building energy model can be increased while the calibrated energy model can be applied as a benchmark for guiding the operational strategies. This research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. A systematic literature review has been conducted in investigating the most popular used data formats and data exchange methods for the integration of BIM/BES and BAS, the results showed the industry foundation classes (IFC) was the most common choice for BIM tools mainly and database is a key solution for managing huge actual operational datasets, which was a reference for the next step in research. Then a BIM-based framework was proposed to supporting the data exchange process among BIM/BES/BAS. 4 modules including BIM Module, Operational Data Module, Energy Simulation Module and Analysis and Visualization Module with an interface were designed in the framework to document energy-related information and to allow its exchange between BES and BAS. A prototype of the framework was developed as a platform and a case study of an entire office suite was conducted using the platform to validate this framework. The results showed that the proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.
Doctor of Philosophy
Building energy efficiency is progressively becoming a crucial topic in the architecture, engineering, and construction (AEC) sector, promising appropriate energy savings can be achieved over the life cycle of buildings through proper design, construction, and operation. Energy management tools have been developed towards this end. Building energy simulation (BES) is a tool mainly used to analyze and compare the energy consumption of various design/operation scenarios. These instances include the selection of both new and retrofit designs and for building codes, building commissioning, and real-time optimal control, among others. The main challenge surrounding BES is the discrepancy between quantitative results and actual performance data. Building automation systems (BAS), or a part of BAS which is often referred to as building energy management systems (BEMS), works as another energy management tool to monitor, measure and collect operational data, all in an effort to optimize energy consumption. The key disadvantage to the more general tool of BAS in energy management is that the data sets collected by BAS are typically too large to be analyzed effectively. One potential solution to the lack of effective energy management analysis may lie in the integration of BES and BAS. Actual operational data can be compared with simulation results in assessing the accuracy of an energy model while the energy model can be applied as a benchmark for evaluating the actual energy consumption and optimizing control strategies. The presented research predicted that building information modeling (BIM) would link BES and BAS by acting as a visual model and a database throughout the lifecycle of a building. The intent of the research was to use BIM to document energy-related information and to allow its exchange between BES and BAS. Thus, the energy-related data exchange process would be simplified, and the productive efficiency of facility management processes would increase. More specifically, this research posits the framework of integrating BIM, BES, and BAS to produce a seamless and real-time energy-related information exchange system. The proposed framework enables automated or semi-automated multiple-model development and data analytics processes. In addition, the research explored how BIM can enhance the application of energy modeling during building operation processes as a means to improve overall energy performance and facility management productivity.

Modern civilization has developed principally through man's harnessing of forces. For centuries man had to rely on wind, water and animal force as principal sources of power. The advent of the industrial revolution, electrification and the development of new technologies led to the application of wood, coal, gas, petroleum, and uranium to fuel new industries, produce goods and means of transportation, and generate the electrical energy which has become such an integral part of our lives. The geometric growth in energy consumption, coupled with the world's unrestricted growth in population, has caused a disproportionate use of these limited natural resources. The resulting energy predicament could have serious consequences within the next half century unless we commit ourselves to the philosophy of effective energy conservation and management. National legislation, along with the initiative of private industry and growing interest in the private sector has played a major role in stimulating the adoption of energy-conserving laws, technologies, measures, and practices. It is a matter of serious concern in the United States, where ninety-five percent of the commercial and industrial facilities which will be standing in the year 2000 - many in need of retrofit - are currently in place. To conserve energy, it is crucial to first understand how a facility consumes energy, how its users' needs are met, and how all internal and external elements interrelate. To this purpose, the major thrust of this report will be to emphasize the need to develop an energy conservation plan that incorporates energy auditing and surveying techniques. Numerous energy-saving measures and practices will be presented ranging from simple no-cost opportunities to capital intensive investments.

With growing concerns on global energy demand and climate change, it is important to focus on efficient utilization of electricity in commercial buildings, which contribute significantly to the overall electricity consumption. Accordingly, there has been a number of Building Energy Management (BEM) software/hardware solutions to monitor energy consumption and other measurements of individual building loads. BEM software serves as a platform to implement smart control strategies and stores historical data. Although BEM software provides such lucrative benefits to building operators, in terms of energy savings and personalized control, these benefits are not harnessed by most small to mid-sized buildings due to a high cost of deployment and maintenance. A cloud-based BEM system can offer a low-cost solution to promote ease of use and support a maintenance-free installation. In a typical building, a conventional router has a public address and assigns private addresses to all devices connected to it. This led to a network topology, where the router is the only device in the Internet space with all other devices forming an isolated local area network behind the router. Due to this scenario, a cloud-based BEM software needs to pass through the router to access devices in a local area network. To address this issue, some devices, during operation, make an outbound connection to traverse through the router and provide an interface to itself on the Internet. Hence, based on their capability to traverse through the router, devices in a local area network can be distinguished as cloud and non-cloud devices. Cloud-based BEM software with sufficient authorization can access cloud devices. In order to access devices adhering to non-cloud protocols, cloud-based BEM software requires a device in the local area network which can perform traversal through the router on behalf of all non-cloud devices. Such a device acts as an IoT gateway, to securely interconnect devices in a local area network with cloud-based BEM software. This thesis focuses towards architecting, designing and prototyping an Internet-of-Things (IoT) gateway which can perform traversal on behalf of non-cloud devices. This IoT gateway enables cloud-based BEM software to have a comprehensive access to supported non-cloud devices. The IoT gateway has been designed to support BACnet, Modbus and HTTP RESTful, which are the three widely adopted communication protocols in the building automation and control domain. The developed software executes these three communication protocols concurrently to address requests from cloud-based BEM system. The performance of the designed architecture is independent of the number of devices supported by the IoT gateway software.
Master of Science

The influence of building occupancy and user behaviour on energy usage has been identified as a source of uncertainty in current understanding of operational buildings, and yet it is rarely directly monitored. Gathering data on the occupancy of buildings in use is essential to improve understanding of how energy is used relative to the actual energy requirements of building users. This thesis covers the application of occupancy measurement and processing techniques in order to address the gap in knowledge around the contextual understanding of how occupants’ changing use of a building affects this building’s optimum energy demand in real time. Through targeted studies of running buildings, it was found that typical current occupancy measurement techniques do not provide sufficient context to make energy management decisions. Useable occupancy information must be interpreted from raw data sources to provide benefit: in particular, many slower response systems need information for pre-emptive control to be effective and deliver comfort conditions efficiently, an issue that is highlighted in existing research. Systems utilising novel technologies were developed and tested, targeted at the detection and localisation of occupants’ personal mobile devices, making opportunistic use of the existing hardware carried by most building occupants. It was found that while these systems had the potential for accurate localisation of occupants, this was dependent on personal hardware and physical factors affecting signal strength. Data from these sources was also used alongside environmental data measurements in novel algorithms to combine sensor data into a localised estimation of occupancy rates and to estimate near-future changes in occupancy rate, calculating the level of confidence in this prediction. The developed sensor combination model showed that a selected combination of sensors could provide more information than any single data source, but that the physical characteristics and use patterns of the monitored space can affect how sensors respond, meaning a generic model to interpret data from multiple spaces was not feasible. The predictive model showed that a trained model could provide a better prediction of near-future occupancy than the typically assumed fixed schedule, up to an average of approximately two hours. The systems developed in this work were designed to facilitate the proactive control of buildings services, with particular value for slower-response systems such as heating and ventilation. With the application of appropriate control logic, the systems developed can be used to allow for greater energy savings during low or non-occupied periods, while also being more robust to changing occupant patterns and behaviours.

Building management system (BMS) offers a wide range of measurements and historical data about the building but few types of researches use these data to analyze the building performance. This study aims to explore the indoor climate and building insulation by taking advantage of the BMS of the study case, which 767 sensors are installed in the room and wall structures and the signal data are available at the online web application. In addition, during the inspection, several error sensors and meters are detected are discussed as feedback for the system. It is concluded that the building management system is a good tool to study the building performance in different aspects and the measurements from the sensors are helpful but need validation by conducting a further field measurement in the building.

The persistent increase in the price of energy, the clamour to preserve our environment from the harmful effects of the anthropogenic release of greenhouse gases from the combustion of fossil fuels and the need to conserve these rapidly depleting fuels has resulted in the need for the deployment of industry best practices in energy conservation through energy efficiency improvement processes like the waste heat recovery technique. In 2006, it was estimated that approximately 20.66% of energy in the UK is consumed by industry as end-user, with the process industries (chemical industries, metal and steel industries, food and drink industries) consuming about 407 TWh, 2010 value stands at 320.28 TWh (approximately 18.35%). Due to the high number of food and drink industries in the UK, these are estimated to consume about 36% of this energy with a waste heat recovery potential of 2.8 TWh. This work presents the importance of waste heat recovery in the process industries in general, and in the UK food industry in particular, with emphasis on the fryer section of the crisps manufacturing process, which has been identified as one of the energy-intensive food industries with high waste heat recovery potential. The work proposes the use of a dual heat source ORC system for the recovery and conversion of the waste heat from the fryer section of a crisps manufacturing plant to electricity. The result, obtained through modelling and simulation, shows that the proposed technology can produce about 92% of the daily peak electricity need of the plant which is currently 216 kW. Also, the economic analysis shows that the proposed technology is viable (even at an inflation rate of 5.03% and discounted rate of 6%), with a payback period of approximately three years and net present value of over £2.2 million if the prices of electricity and carbon is at an average value of £0.16 and £13.77 respectively throughout the 30 years service life of the plant. The life cycle assessment study shows that the proposed technology can reduce the CO2 emission by 139,580 kg/year if the electricity produced is used to displace that which would have been produced from a conventional coal-fired power plant.

This thesis highlights the importance of stakeholders' interactions in enhancing the energy efficiency of housing. It uses stakeholder management as a frame work to quantify the influence of stakeholders on energy efficiency outcomes. This framework is applied and tested on a number of owner-occupied housing in Australia to analyse the effect of stakeholders' attributes (such as their roles, responsibilities, and interests) on the energy performance of houses.

This study was conducted as part of California State University of Long Beach (CSULB) Research Foundation project tittle “Internet of Things (IoT) and Ubiquitous Sensing in University Building Energy Management: Design, Optimization and Technology Demonstration” funded by the California Energy Commission (CEC) EEETD grant and led by Dr. Massoud Nazari. The Emerging Energy Efficient Technology Demonstrations (EEETD) grant of the CEC is aimed to demonstrate and market the deployment of innovative pre-commercial energy efficiency technologies in existing buildings to promote early adoption of energy efficiency systems that will reduce energy consumption as well as costs for the building owner.

Despite the technological advancements for building management systems the actual market penetration is still relatively small in the commercial sector. One of the reasons for not investing on upgrades includes informational awareness. In the hopes to aid in that particular aspect, the following study provides a breakdown of the process for the commissioning of the new management system and demonstrates the scalability of the overall project by first focusing on the existing building such as identifying the building’s energy end uses and circuit configuration for lighting, HVAC, and plug loads. Second, it details the customization for the new EMS, and the interoperability of the new smart devices and network architecture.

This thesis summarizes the results from several studies with connection to sustainability in construction and management of real estate. Here, the concept sustainability includes environmental, social and economic dimensions and focus is on the actors with the best possibilities to impact real estate, namely the real estate owners and the developers. The thesis consists of six papers. Real estate owners’ perception of and incentives and strategies for sustainability was studied in four ways: incentives for energy efficiency and other sustainability issues in connection to renovation (papers I and II), factors that characterize firms with an ambitious approach to energy efficiency (paper V) and economic incentives for energy efficiency (paper VI). Developers’ behavior and impact on sustainability was studied in two ways: how developers’ planning and construction methods may influence energy consumption for future residents (paper III) and how developers relate to requirements for building environmental certification levels (paper IV). The first paper aims to clarify how housing firms see and treat energy efficiency matters in connection to renovation of multi-family buildings constructed during the 1960’s and 70’s. Interviews with housing firms resulted in four ideal housing firm types illustrating that housing firms have more or less incentives to improve energy efficiency. The second paper aimed to study a model for renovation of buildings in a residential area in peripheral Stockholm and to assess how it considers environmental, social and economic sustainability as well as technical concerns. Paper V builds on the results in paper I and aims to identify factors, on a firm level as well as in the surroundings of the firm, that characterize housing firms who own multi-family buildings from the 1960’s and 70’s and who have an ambitious approach to energy efficiency. Paper VI uses information from energy performance certificates to study whether better energy performance increases the selling price of single-family homes, which would increase owners’ incentives to improve energy efficiency. Paper III takes its starting point in an indicated shift in developers’ planning and construction practices for laundry facilities in owner-occupied multi-family buildings. The paper aims to clarify whether a shift has actually occurred from communal laundry rooms to in-unit laundry appliances and to illuminate the impact this could have on residents’ energy consumption for laundry. Paper IV reports the study of how developers who have adopted the environmental certification system LEED relate to the requirements for specific certification levels and how updated requirements risk undermining developers’ incentives for sustainable construction.
Den här avhandlingen sammanfattar arbetet från flera studier med koppling till hållbarhet inom bygg och förvaltning av fastigheter. Begreppet hållbarhet omfattar här tre dimensioner: miljömässig, social och ekonomisk hållbarhet, och fokus ligger på de aktörer som har mest möjlighet att påverka fastigheterna, nämligen fastighetsägare och projektutvecklare. I avhandlingen ingår sex uppsatser. Fastighetsägares uppfattning av och incitament och strategier för hållbarhet undersöktes på fyra olika sätt: incitament för energieffektivisering och andra hållbarhetsfrågor i samband med renovering (uppsats I och II), faktorer som karaktäriserar företag med ett ambitiöst förhållningssätt i energieffektiviseringsfrågor (uppsats V) samt ekonomiska incitament för energieffektivisering (uppsats VI). Projektutvecklares beteende och påverkan på hållbarhet undersöktes på två sätt: hur projektutvecklares planering och byggmetoder kan påverka energianvändningen för framtida boende (uppsats III) och hur projektutvecklare förhåller sig till kravnivåer i miljöcertifiering av byggnader (uppsats IV). Den första uppsatsen syftar till att belysa hur bostadsföretag ser på och behandlar energieffektiviseringsfrågor i samband med renovering av flerbostadshus byggda under miljonprogrammet. Baserat på intervjuer med bostadsföretag resulterade den explorativa studien i konstruktionen av fyra idealtyper av bostadsföretag med mer eller mindre incitament för att energieffektivisera. Den andra uppsatsen syftade till att undersöka en modell för renovering av miljonprogramshus i ett bostadsområde i Stockholms ytterområden och bedöma hur den tar hänsyn till miljömässig, social och ekonomisk hållbarhet tillsammans med tekniska överväganden. Uppsats V bygger på resultaten i uppsats I och syftar till att urskilja faktorer, såväl på företagsnivå som i företagets omgivning, som karaktäriserar bostadsföretag som äger flerbostadshus från miljonprogramsåren och som har en ambitiös hållning i energieffektiviseringsfrågor. Uppsats VI använder information från energideklarationer för att undersöka om bättre energiprestanda ökar försäljningspriset på småhus, något som skulle öka ägarens incitament för energieffektivisering. Uppsats III utgår från en indikerad förändring i projektutvecklares planering och byggmetoder av tvättinrättningar i flerbostadshus med bostadsrätt. Uppsatsen syftar till att klarlägga om en förändring har skett från gemensam tvättstuga till tvättmöjligheter i den egna bostaden och belysa vilken effekt det skulle kunna ha på de boendes energianvändning för tvätt. I uppsats IV klarläggs hur projektutvecklare som bygger enligt miljöcertifieringssystemet LEED förhåller sig till kraven för att uppnå nivåerna för att klassificeras och hur uppdaterade kravnivåer riskerar att undergräva projektutvecklarnas incitament för att bygga hållbart.

QC 20141218

As society develops, energy needs and the warnings of global warming have become main areas of focus in many areas of human life. One such aspect, the building sector, needs to take responsibility for a significant portion of energy use. Researchers need to concentrate on applying innovative methods for controlling the growth of energy use. Apart from improving energy efficiency by reducing energy use and improving the match between energy supply and demand, energy quality issues have become a key topic of interest. Energy quality management (EQM) is a technique that aims to optimally utilize the exergy content of various renewable energy sources. The evaluation of the optimum energy systems for specific districts is an essential part of EQM. The optimum energy system must follow the concept of “sustainability.” In other words, the optimization process should select the most suitable energy systems, which fulfill various sustainable requirements such as high energy/exergy performance, low environmental impacts and economic cost, as well as acceptable system reliability. A common approach to dealing with complex criteria involves multi-objective optimization, whereby multi-objective optimization is applied in the context of EQM of building clusters and districts (BCDs). In the present thesis, a multi-objective optimization process is proposed that applies a genetic algorithm (GA) to address non-linear optimization problems. Subsequently, four case studies are used to analyze how the multi-objective optimization process supports EQM of BCDs. Detailed information about these cases is provided below: 1. Basic case (UK): This case is used to investigate the application possibility of the approach in BCD energy system design and to analyze the optimal scenario changes, along with variations of optimization objective combinations. This approach is proven to be time-effective 2. Case 1 (Norway): The use of renewable energy sources can be highly intermittent and dependent on local climatic conditions; therefore, energy system reliability is a key parameter be considered for the renewable energy systems. This section defines system reliability as a constraint function and analyzes the system changes caused by the varying reliability constraints. According to the case, system reliability has been proven to be one of the most important objectives for the optimization of renewable energy systems. 3. Case 2 (China): In this section, the approach is applied in order to search for the optimal hybrid system candidates for a net-zero exergy district (NZEXD) in China. Economic analysis is included in this case study. Through the optimization process, the proposed approach is proven to be flexible and capable of evaluating distinct types of energy scenarios with different objective functions. Moreover, the approach is able to solve practical issues, such as identifying the most feasible options to the stepwise energy system transition for a specific case. 4. Case 3 (China): This section makes two major contributions. The first is to test the expansibility of inserting additional objectives into the approach; a parametric study is then applied to investigate the effects of different energy parameters. The second contribution is the conclusion that the optimum energy systems might vary significantly, depending on certain parameters. According to the analyses in these case studies, the multi-objective optimization approach is capable of being a tool for future BCDs’ energy system design. It should also be noted that the findings from the case studies – especially the parametric study – might provide some interesting research topics for future work.

QC 20151211

Sustainability has been at the centre of global attention for decades. One of the most challenging areas toward sustainability is the agricultural sector. Here, the commercial greenhouse is one of the most effective cultivation methods with a yield per cultivated area up to 10 times higher than for open land farming. However, this improvement comes with a higher energy demand. Therefore, the significance of energy conservation and management in the commercial greenhouse has been emphasized to enable cost efficient crop production. This Doctoral Thesis presents an assessment of energy pathways for improved greenhouse performance by reducing the direct energy inputs and by conserving energy throughout the system. A reference theoretical model for analyzing the energy performance of a greenhouse has been developed using TRNSYS. This model is verified using real data from a conventional greenhouse in Stockholm (Ulriksdal). With this, a number of energy saving opportunities (e.g. double glazing) were assessed one by one with regards to the impact on the annual heating, cooling and electricity demand. Later, a multidimensional energy saving method, the “Closed Greenhouse”, was introduced. The closed greenhouse is an innovative concept with a combination of many energy saving opportunities. In the ideal closed greenhouse configuration, there are no ventilation windows, and the excess heat, in both sensible and latent forms, needs to be stored using a seasonal thermal energy storage. A short term (daily) storage can be used to eliminate the daily mismatch in the heating and cooling demand as well as handling the hourly fluctuations in the demand. The key conclusion form this work is that the innovative concept “closed greenhouse” can be cost-effective, independent of fossil fuel and technically feasible regardless of climate condition. For the Nordic climate case of Sweden, more than 800 GWh can be saved annually, by converting all conventional greenhouses into this concept. Climate change mitigation will follow, as a key impact towards sustainability. In more detail, the results show that the annual heating demand in an ideal closed greenhouse can be reduced to 60 kWhm-2 as compared to 300 kWhm-2 in the conventional greenhouse. However, by considering semi-closed or partly closed greenhouse concepts, practical implementation appears advantageous. The required external energy input for heating purpose can still be reduced by 25% to 75% depending on the fraction of closed area. The payback period time for the investment in a closed greenhouse varies between 5 and 8 years depending on the thermal energy storage design conditions. Thus, the closed greenhouse concept has the potential to be cost effective. Following these results, energy management pathways have been examined based on the proposed thermo-economic assessment. From this, it is clear that the main differences between the suggested scenarios are the type of energy source, as well as the cooling and dehumidification strategies judged feasible, and that these are very much dependent on the climatic conditions Finally, by proposing the “solar blind” concept as an active system, the surplus solar radiation can be absorbed by PVT panels and stored in thermal energy storage for supplying a portion of the greenhouse heating demand. In this concept, the annual external energy input for heating purpose in a commercial closed greenhouse with solar blind is reduced by 80%, down to 62 kWhm-2 (per unit of greenhouse area), as compared to a conventional configuration. Also the annual total useful heat gain and electricity generation, per unit of greenhouse area, by the solar blind in this concept is around 20 kWhm-2 and 80 kWhm-2, respectively. The generated electricity can be used for supplying the greenhouse power demand for artificial lighting and other devices. Typically, the electricity demand for a commercial greenhouse is about 170 kWhm-2. Here, the effect of “shading” on the crop yield is not considered, and would have to be carefully assessed in each case.
Hållbarhet har legat i fokus under decennier. En av de mest utmanande områdena är jordbrukssektorn, där. kommersiella växthus är ett av de mest effektiva odlingsalternativen med en avkastning per odlad yta upp till 10 gånger högre än för jordbruk på friland. Dock kommer denna förbättring med ett högre energibehov. Därför är energieffektivisering i kommersiella växthus viktig för att möjliggöra kostnadseffektiv odling. Denna doktorsavhandling presenterar en utvärdering av olika energiscenarios för förbättring av växthusens prestanda genom att minska extern energitillförsel och spara energi genom i systemet som helhet. För studien har en teoretisk modell för analys av energiprestanda i ett växthus utvecklats med hjälp av TRNSYS. Denna modell har verifierats med hjälp av verkliga data från ett konventionellt växthus i Stockholm (Ulriksdal). Med denna modell har ett antal energibesparingsåtgärder (som dubbelglas) bedömts med hänsyn till de totala värme-, kyl-och elbehoven. En flerdimensionell metod för energibesparing, det s.k. "slutna växthuset", introduceras. Det slutna växthuset är ett innovativt koncept som är en kombination av flera energibesparingsmöjligheter. I den ideala slutna växthuskonfigurationen finns det inga ventilationsfönster och värmeöverskott, både sensibel och latent, lagras i ett energilager för senare användning. Daglig lagring kan användas för att eliminera den dagliga obalansen i värme-och kylbehovet. Ett säsongslager introduceras för att möjliggöra användandet av sommarvärme för uppvärmning vintertid. Den viktigaste slutsatsen från detta arbete är att ett sådant innovativt koncept, det "slutna växthuset" kan vara kostnadseffektiv, oberoende av fossila bränslen och tekniskt genomförbart oavsett klimatförhållanden. För det svenska klimatet kan mer än 800 GWh sparas årligen, genom att konvertera alla vanliga växthus till detta koncept. Det årliga värmebehovet i ett idealiskt slutet växthus kan reduceras till 60 kWhm-2 jämfört med 300 kWhm-2 i ett konventionellt växthus. Energibesparingen kommer även att minska miljöpåverkan. Även ett delvis slutet växthus, där en del av ytan är slutet, eller där viss kontrollerad ventilation medges, minskar energibehovet samtidigt som praktiska fördelar har kunnat påvisas. Ett delvis slutet växthus kan minska energibehovet för uppvärmning med mellan 25% och 75% beroende på andelen sluten yta. En framräknad återbetalningstid för investeringen i ett slutet växthus varierar mellan 5 och 8 år beroende på design av energilagringssystemet. Sålunda har det slutna växthuskonceptet potential att vara kostnadseffektiv. Mot bakgrund av dessa lovande resultat har sedan scenarios för energy management analyserats med hänsyn till termo-ekonomiska faktorer. Från detta är det tydligt att de viktigaste skillnaderna mellan de föreslagna scenarierna är den typ av energikälla, samt kyl- och avfuktningsstrategier som används, och dessa val är mycket beroende av klimatförhållandena. Slutligen, föreslås ett nytt koncept, en s.k. "solpersienn", vilket är ett aktivt system där överskottet av solstrålningen absorberas av PVT-paneler och lagras i termiskenergilager för att tillföra en del av växthuseffekten värmebehov. I detta koncept minskar den årliga externa energitillförseln för uppvärmning i ett slutet växthus med 80%, ner till 62 kWhm-2. Den totala värme- och elproduktionen, med konceptet "solpersienn" blir cirka 20 kWhm-2 respektive 80 kWhm-2. Elproduktion kan användas för artificiell belysning och annan elektrisk utrustning i växthuset.

QC 20130910

Lo sfruttamento della flessibilità energetica negli edifici rappresenta una delle soluzioni più promettenti per consentire il passaggio a sistemi energetici ad alta penetrazione di rinnovabili. Disporre di un edificio flessibile significa poter applicare efficientemente strategie di gestione della domanda (Demand Side Management, DSM) che rappresentano uno degli aspetti principali caratterizzanti il concetto di Smart Grid. Il DMS è definito come l'insieme di tutte quelle strategie volte a influenzare gli usi dell'elettricità degli utenti in modo da produrre cambiamenti nella forma della loro curva di carico. Data la crescente domanda elettrica del settore residenziale, soprattutto per la diffusione di impianti di riscaldamento e raffrescamento alimentati elettricamente (es. split e pompe di calore), gli edifici mostrano una predisposizione a produrre variazioni programmate della loro domanda elettrica, grazie ai diversi livelli di inerzia termica in essi già disponibile (es. massa termica dell’involucro o dispositivi dedicati come serbatoi di acqua calda o fredda). Inoltre, grazie a tecniche di controllo avanzate, possono sfruttare diverse fonti energetiche per soddisfare i propri fabbisogni termici, riducendo al contempo i prelievi dalla rete elettrica. Il lavoro presentato in questa tesi si inserisce in questo contesto. L'obiettivo è quello di fornire un'analisi dei diversi aspetti che caratterizzano la flessibilità ottenibile dalla gestione dei carichi termici ed elettrici negli edifici residenziali dotati di pompe di calore. L'analisi si estende progressivamente dal contesto dello scenario progettuale dei singoli edifici a quello operativo degli aggregati. Nella tesi inoltre sono presentate metodologie innovative di quantificazione in aggiunta alla simulazione di diversi casi di studio. In generale, tutti i risultati consentono di confermare la potenzialità degli edifici nella fornitura di servizi di flessibilità energetica.
The exploitation of energy flexibility in buildings represents one of the most promising solutions to allow the transition to energy systems with a high penetration of renewable energy sources. Having a high flexible building means to be able to efficiently apply demand side management strategies (DSMs) which represent one of the main aspects characterizing the concept of Smart Grid. DMS is defined as the set of all those strategies aimed at influencing customer uses of electricity in ways that will produce desired changes in the utility’s load shape. Given the increasing electricity demand in the residential sector, especially for the diffusion of heating and cooling systems electrically powered (e.g., split systems and heat pumps), buildings show a predisposition to produce variations in the electrical demand, due to the different levels of thermal inertia already available in them (e.g., the thermal mass embedded in the envelope or dedicated devices as cold and/or hot water tank). Moreover, thanks to advanced control techniques, buildings could exploit different energy sources to satisfy their thermal requirements, while reducing withdrawals from the power grid. The work presented in this thesis fits into this context. The objective is to provide an overview of the different aspects that characterize the energy flexibility obtainable from the management of thermal and electrical loads in residential buildings equipped with heat pumps. The analysis is gradually extended from the context of the design scenario of single buildings to the operative analysis of clusters of buildings. Novel methodologies of quantification and evaluation are introduced in addition with the examination of different simulation-based case studies. In general, all the analyses allow to confirm the great potential of residential buildings in providing energy flexibility services.

Today's energy policy has to be more stringent if the necessary decrease in energy use is to befulfilled. Considerable investments are needed and it is important to realize that this will eventually pay off in the economy although it will take longer time than usual. Instead o fcalculating on a two year return for a building complex, it could result in a ten year return but then the building most likely are more energy efficient and pays off in the long run.Energy efficiency measurements are the cheapest and most important way to reach the 50/50 goal but one of the biggest issues is the unawareness for energy efficiency options in the building sector. Energy efficiency measures must be carefully planned and performed way ahead of time because the right opportunity to improve a building seldom appears. The technology exists, the information is there and some of the financial support is also there. Still there are many who don't invest; or invest with poor results because of insufficient knowledge or understanding. Financial barrier is the most common obstacle when it comes to reducing energy usage and that's because energy savings isn't a clear investment or investors don't have the adequate understanding many opportunities are lost.It is very important to have clear goals and a support system that produce efficient investments. Property owners today do not invest enough in energy reducing measurements because they under-value the future energy price. The support systems and building regulations should only support refurbishment projects that intend to do deep refurbishment measurements. The policies and building regulations have to be adjusted to a necessary level as soon as possible so these few opportunities are seized. Cooperation, not only between companies, organizations and institutions but countries as well, contributes a lot in the pursuit of lowering the energy demand. This is probably the reason why the Nordic countries of Europe are well adjusted in the present building market. Clear and ambitious goals are the key to have good communication and effects in the building sector just as the Nordic countries have had throughout the twenties century. Sweden is raising awareness; cooperation and investing in both education and refurbishments that will help Sweden reach the energy efficiency targets of the year 2050. Although three factors are crucial; involving the whole building stock, applying an effective financial support system and creating clear goals of how the 50 / 50 target will be reached. The goal for the Swedish building stock is to reduce its total purchased energy demand,including household electricity by 50 % by the year of 2050 compared to the amount of 1995. To examine if the goal is reasonable, a detailed case study, that is limited to one hotel in Oslo, built 2009, was simulated in IDA ICE 4.7 and then virtually improved. The ventilation system was upgraded, the indoor temperature tolerance was adjusted and the lighting was upgraded.The result from the computer calculations indicates very significant possibilities for energy efficiency for this building. This also confirms the general impression of a significant general potential for increased energy efficiency within the building sector. It is possible to achieve the goal for a 50 % reduction for purchased energy for the building sector, but it will need incitement that creates a better economy for energy efficiency investments and very stringent energy requirements also for existing buildings.

In recent years, the energy shortage has become a barrier to social development as there is a shortage of resources, especially non-renewable resources. In order to improve the current situation of human settlement for future generations, a series of environmental protection activities and rational utilizations of natural resources have been carried out at a global level on the theme of "Sustainable Development". Along with the quantitative growth of Chinese public buildings, especially the Large-Scale Public Buildings, the levels of energy consumption are rising and this phenomenon has been seen as a key point of energy management from a national view. This paper discusses the realities of energy consumption and the energy-saving policies of public buildings in the world’s major developed countries, and sums up the current condition of Chinese energy consumption in relation to public buildings. With regard to these factors, this paper tries to find approaches for solving the existing problems in each construction section. Less developed construction technology in China leads to a waste of materials and labor force. There is a lack of professional personnel to handle the operational management and a lack of consideration for energy efficiency in the designing process. There is not a suitable framework for compiling statistics and data on energy consumption. There are still many management problems such as inadequate policy standards in operability or implementation, unclear positioning in governmental management, ineffective incentive or punitive mechanisms, and a defective state system which results directly in an undeveloped service system for energy efficiency. Based on all of the problems listed above, this paper suggests solutions in four areas, namely Policy, Energy Statistics, Management and Education, in order to give academic support for the overhaul of Large-Scale Public Buildings towards greater energy efficiency in China.

State energy plans are created at the request of a sitting governor or State Legislature in order to provide guidance set goals for the state’s energy sector. These plans will be critical indicators of energy trends such as the future market share of coal, natural gas, and renewables. If the future of energy in the United States is to be remotely sustainable, low-carbon policies must headline state plans. The strength of a state’s energy plan in terms of sustainability is directly related to that state’s willingness to prioritize and commit to incorporating energy sources that produce negligible carbon emissions. Questions about the role of efficiency can be answered by the political need for short-run payoffs that do not necessarily align with the long-term goals of sustainability (Kern & Smith, 2008). The nature of the American political system is that representatives want to be able to bring immediate results to their constituents, results that are usually shown in the short-run by efficiency programs. While the state energy plans in question (California, Arizona, Pennsylvania, Iowa, South Carolina, and Virginia) engage with sustainability at varying levels of strength, they deal mostly in weak sustainability by failing to commit to renewables. Historical reliance on energy efficiency and its accompanying theories of growth has created a climate in which state energy plans do not generally realize their enormous potential to lead the national transition away from fossil fuels.

Nowadays, energy is absolutely necessary all over the world. Taking into account the advantages that it presents in transport and the needs of homes and industry, energy is transformed into electricity. Bearing in mind the expansion of electricity, initiatives like Horizon 2020, pursue the objective of a more sustainable future: reducing the emissions of carbon and electricity consumption and increasing the use of renewable energies. As an answer to the shortcomings of the traditional electrical network, such as large distances to the point of consumption, low levels of flexibility, low sustainability, low quality of energy, the difficulties of storing electricity, etc., Smart Grids (SG), a natural evolution of the classical network, has appeared. One of the main components that will allow the SG to improve the traditional grid is the Energy Management System (EMS). The EMS is necessary to carry out the management of the power network system, and one of the main needs of the EMS is a prediction system: that is, to know in advance the electricity consumption. Besides, the utilities will also require predictions to manage the generation, maintenance and their investments. Therefore, it is necessary to dispose of the systems of prediction of the electrical consumption that, based on the available data, forecast the consumption of the next hours, days or months, in the most accurate way possible. It is in this field where the present research is placed since, due to the proliferation of sensor networks and more powerful computers, more precise prediction systems have been developed. Having said that, a complete study has been realized in the first work, taking into account the need to know, in depth, the state of the art, in relation to the load forecasting topic. On the basis of acquired knowledge, the installation of sensor networks, the collection of consumption data and modelling, using Autoregressive (AR) models, were performed in the second work. Once this model was defined, in the third work, another step was made, collecting new data, such as building occupancy, meteorology and indoor ambience, testing several paradigmatic models, such as Multiple Linear Regression (MLR), Artificial Neural Network (ANN) and Support Vector Regression (SVR), and establishing which exogenous data improves the prediction accuracy of the models. Reaching this point, and having corroborated that the use of occupancy data improves the prediction, there was the necessity of generating techniques and methodologies, in order to have the occupancy data in advance. Therefore, several attributes of artificial occupancy were designed, in order to perform long-term hourly consumption predictions, in the fourth work.
A dia d’avui l’energia és un bé completament necessari arreu del món. Degut als avantatges que presenta en el transport i a les necessitats de les llars i la indústria, l’energia és transformada en energia elèctrica. Tenint en compte la total expansió i domini de l’electricitat, iniciatives com Horitzó 2020, tenen per objectiu un futur més sostenible: reduint les emissions de carboni i el consum i incrementant l’ús de renovables. Partint dels defectes de la xarxa elèctrica clàssica, com són gran distància al punt de consum, poca flexibilitat, baixa sostenibilitat, baixa qualitat de l’energia, dificultats per a emmagatzemar energia, etc. apareixen les Smart Grid (SG), una evolució natural de la xarxa clàssica. Un dels principals elements que permetrà a les SG millorar les xarxes clàssiques és l’Energy Management System (EMS). Així doncs, per a que l’EMS pugui dur a terme la gestió dels diversos elements, una de les necessitats bàsiques dels EMS serà un sistema de predicció, o sigui, saber per endavant quin consum hi haurà en un entorn determinat. A més, les empreses subministradores d’electricitat també requeriran de prediccions per a gestionar la generació, el manteniment i fins i tot les inversions a llarg termini. Així doncs ens calen sistemes de predicció del consum elèctric que, partint de les dades disponibles, ens subministrin el consum que hi haurà d’aquí a unes hores, uns dies o uns mesos, de la manera més aproximada possible. És dins d’aquest camp on s’ubica la recerca que presentem. Degut a la proliferació de xarxes de sensors i computadors més potents, s’han pogut desenvolupar sistemes de predicció més precisos. A tall de resum, en el primer treball, i tenint en compte que s’havia de conèixer en profunditat l’estat de la qüestió en relació a la predicció del consum elèctric, es va fer una anàlisi completa de l’estat de l’art. Un cop fet això, i partint del coneixement adquirit, en el segon treball es va dur a terme la instal•lació de les xarxes de sensors, la recollida de dades de consum i el modelatge amb models lineals d’auto-regressió (AR). En el tercer treball, un cop fets els models es va anar un pas més enllà recollint dades d’ocupació, de meteorologia i ambient interior, provant diferents models paradigmàtics com Multiple Linear Regression (MLR), Artificial Neural Network (ANN) i Support Vector Regression (SVR) i establint quines dades exògenes milloren la predicció dels models. Arribat a aquest punt, i havent corroborat que l’ús de dades d’ocupació millora la predicció, es van generar tècniques per tal de disposar de les dades d’ocupació per endavant, o sigui a hores vista. D’aquesta manera es van dissenyar diferents atributs d’ocupació artificials, permetent-nos fer prediccions horàries de consum a llarg termini. Aquests conceptes s’expliquen en profunditat al quart treball.

L’intégration des énergies renouvelables produites par un bâtiment et les réseaux de fourniture, qui sont amenés à proposer des tarifications et des puissances disponibles variables au cours de la journée, entraînent une grande variabilité de la disponibilité de l’énergie. Mais les besoins des utilisateurs ne sont pas forcément en accord avec cette disponibilité. La gestion de l’énergie consiste alors à faire en sorte que les moments de consommation des installations coïncident avec les moments où celle-ci est disponible. Notre objectif a été de proposer une stratégie de commande prédictive, distribuée et hiérarchisée, pour gérer efficacement l’énergie de l’habitat. Les aspects prédictifs de notre approche permettent d’anticiper les besoins et les variations de la tarification énergétique. L’aspect distribué va permettre d’assurer la modularité de la structure de commande, pour pouvoir intégrer différents usages et différentes technologies de manière simple et sans faire exploser la combinatoire du problème d’optimisation résultant
Electrical system is under a hard constraint: production and consumption must be equal. The production has to integrate non-controllable energy resources and to consider variability of local productions. While buildings are one of the most important energy consumers, the emergence of information and communication technologies (ICT) in the building integrates them in smart-grid as important consumer-actor players. Indeed, they have at their disposal various storage capacities: thermal storage, hot-water tank and also electrical battery. In our work we develop an hierarchical and distributed Building Energy Management Systems based on model predictive control in order to enable to shift, to reduce or even to store energy according to grid informations. The anticipation enables to plan the energy consumption in order to optimize the operating cost values, while the hierarchical architecture enables to treat the high resolution problem complexity and the distributed aspect enables to ensure the control modularity bringing adaptability to the controller

Thesis: Ph. D. in Architecture: Building Technology, Massachusetts Institute of Technology, Department of Architecture, 2019
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 109-117).
To reduce greenhouse gas emissions associated with their buildings' energy use, owners frequently rely on building energy models that are calibrated to existing conditions for evaluation of potential energy efficiency retrofits. Development of such calibrated models requires the estimation of a series of building characteristics, a process which is extremely effort-intensive even for a single building and, therefore, almost prohibitive for large campus projects which often include hundreds of diverse-use buildings. There is a need for a framework that combines established urban energy model generation techniques with data-driven methods to reduce the manual and computational cost of developing calibrated baseline campus energy models, allow for real time evaluation of future building upgrades, and display their consequences to decision makers on an ongoing basis. This dissertation addresses this need by proposing new workflows for different development stages of models designed to evaluate future energy scenarios for large institutional campuses. First, the strengths and limitations of different urban modeling methodologies are assessed (modeling approach). Next, a methodology to employ statistical surrogate models is proposed for rapid estimation of unknown building properties (auto-calibration). Finally, a continuous energy performance tracking framework is presented to enable university campuses to manage their building related greenhouse gas emissions over time (continuous planning). As a proof of concept, the complete method has been implemented and tested at the author's home institution. Auto-calibration and continuous planning can be implemented independently or combined, and the dissertation includes a discussion about their possible impact if applied across the building stock.
by Shreshth Nagpal.
Ph. D. in Architecture: Building Technology
Ph.D.inArchitecture:BuildingTechnology Massachusetts Institute of Technology, Department of Architecture

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.

The recent increase of electric cars adoption will inuence the electricity demand in the distributionnetworks which risks to be higher than the maximum power available in the grid, if not well planned. Forthis reason, it is on the DSOs and TSOs's interest to plan carefully coordinated charging of a bulk of EVsas well as assess the possibility of EVs acting as energy storages with the Vehicle-to-Grid (V2G) or Vehicleto-Building (V2B) capability. When parked and plugged into the electric grid, EVs will absorb energy andstore it, being also able to deliver electricity back to the grid/building (V2G/B system).This can be anoptimized process, performed by an aggregator, gathering multiple EVs that discharge the battery into thegrid at peak time and charge when there is low demand i.e. overnight and o-peak hours.Numerous studies have investigated the possibility of aggregating multiple EVs and optimizing theircharging and discharging schedules for peak load reduction or energy arbitrage with participation in theelectricity market. However, no study was found for optimizing a shared eet of EVs with daily reservationsfor dierent users trying to perform V2B. In this study an optimization modelling algorithm (mixed integerlinear problem - MILP) that manages the possible reservations of the shared eet of EVs, coordinates thecharging and discharging schedules, and provides V2B (Vehicle-to-Building), with the objective of minimizingenergy costs and accounting with battery ageing has been developed. A case study with real data for abuilding is carried out modelling dierent number of EVs for two dierent days in year 2017, one in Marchand other in June.Results show that the prots are higher for all cases when introducing V2B as compared to a no optimizationscenario: V2B with battery degradation (50 ore/kWh) has decreased daily variable electricity costsbetween 54 and 59% in March and 60 and 63% for June when compared without smart charging. Integrationof battery degradation cost in V2B applications is necessary and inuences signicantly the chargingand discharging strategies adopted by EV and nally the total daily costs: The total daily cost increaseby maximal 10% for the day in March and 13% for the day in June when comparing the scenario that hasstationary battery and uses only-charging model for EVs with the scenario applying V2B mode consideringa degradation cost of 80 ore/kWh.
Ö kningen av antalet elbilar kommer att påverka lasten i elnätet som riskerar att bli högre än kapacitetom det inte är väl planerat. Därför är det i elnätsföretags intresse att samordna laddningen av de flesta elbilarna samt att utvärdera möjligheterna att använda elbilar som energilager gentemot elnätet (Vehicleto-Grid,V2G) eller byggnader (Vehicle-to-Building, V2B). Vid parkering och anslutning till elnätet kommer elbilar att ladda energi och lagra den, samtidigt de kan leverera el tillbaka till elnätet eller byggnaden (V2G/V2B). Detta kan vara en optimerad process som utförs av en aggregator genom att ladda flera elbilar i låglasttimmar och ladda ur dem under höglasttimmar.Många studier har undersökt möjligheten att aggregera flera elbilar och optimera laddningsoch urladdningsplaner för topplastreduktion eller energiarbitrage på elmarknaden. Ingen studie har dock hittats för att optimera en gemensam flotta av elbilar med dagliga reservationer för olika användare som försöker utföra V2B. Denna studie har utvecklat en optimeringsmodell (blandad heltalsprogrammering MILP) som hanterar möjliga reservationer av en flotta av elbilar, koordinerar laddning och urladdning planering, och utför V2B för att minimera energikostnader med hänsyn till batteriets åldrande. En fallstudie för en byggnad genomfördes modellering av olika antal elbilar för två dagar 2017, en i mars och andra i juni.Resultaten visar att vinsten är högre i samtliga fall då man introducerar V2B jämfört med scenario utan optimering: V2B med batteriladdningskostnad 50 öre/kWh minskade dagliga rörliga elkostnader mellan 54% och 59% i mars och mellan 60% och 63% i juni jämfört med utan smart laddning. Att inkludera batteriladdningskostnaden i V2B-applikationer är nödvändigt och har en signifikant inverkan på laddningsstrategierna och de totala kostnaderna: De totala dagliga kostnaderna ökar med upp till 10% i mars och upp till 13% i juni då man jämför scenariot att bara ladda elbilar och ha stationärt batteri med scenariot V2B med hänsyntill batteriladdningskostnad 80 öre/kWh.

Following several years of research and development around the subject of BIM, its impact on the design and handover of buildings is now becoming visible across the construction industry. Changes in design procedures and information management methods indicate the potential for greater utilisation of a Common Data Environment in areas other than design. To identify how these changes are influencing the engineering design process, and adapt this process to the needs and requirements of building performance management requires consideration of multiple factors, relating mainly to the stakeholders and processes employed in these procedures. This thesis is the culmination of a four year Engineering Doctorate exploring how BIM could be used to support non-domestic building energy performance management. It begins with an introduction to the research aim and objectives, then presents a thorough review of the subject area and the methodologies employed for the research. Research is split between eight sequential tasks using literature review, interviews, data analysis and case-study application from which findings, conclusions and key recommendations are made. Findings demonstrate disparity between different information environments and provide insight into the necessary steps to enable connection between BIM and monitored building energy performance information. They highlight the following factors essential to providing an information environment suitable for BIM applied performance management: Skills in handling information and the interface between various environments; Technology capable of producing structured and accurate information, supporting efficient access for interconnection with other environments; and Processes that define the standards to which information is classified, stored and modified, with responsibility for its creation and modification made clear throughout the building life-cycle. A prototype method for the linking of BIM and monitored building energy performance data is demonstrated for a case-study building, encountering many of the technical barriers preventing replication on other projects. Methodological challenges are identified using review of existing building design and operation procedures. In conclusion the research found that BIM is still in its infancy, and while efforts are being made to apply it in novel ways to support efficient operation, several challenges remain. Opportunities for building energy performance improvement may be visualised using the modelling environment BIM provides, and the ability to interface with descriptive performance data suggests the future potential for BIM utilisation post-handover.

This thesis researched the accuracy of measured energy data in comparison to estimated hand calculation data and estimated building energy performance simulation data. In the facility management industry, there is minimal evidence that building energy performance software is being used as a benchmark against measured energy usage within a building. Research was conducted to find examples of measured energy data compared to simulated data. The study examined the accuracy of a simulation software and hand calculations to measured energy data. Data suggests that comparisons may be made between building energy performance simulated data and measured data, though comparisons are solely based on each individual case. Data suggests that heating load simulation data is more accurate for benchmarks than cooling load simulation data. Importing models into Autodesk Green Building Studio (GBS) was not as successful as was expected. When only four of the initial ten building models chosen imported successfully, the remaining twenty-five other building models were imported. Only two of the twenty-five models successfully imported into GBS. The sample size of this research changed from ten to six. The results of this study show that GBS simulated data was close to actual data for the heating loads. For the cooling loads, however, GBS simulated data was consistently low in comparison to the actual data. The results of this study show that hand calculations were consistently low and not as close as GBS simulated data when compared to the actual data for the heating loads. The opposite was true with the cooling loads as hand calculations were consistently high in comparison to actual data.

In recent years, the energy shortage has become a barrier to social development as there is a shortage of resources, especially non-renewable resources. In order to improve the current situation of human settlement for future generations, a series of environmental protection activities and rational utilizations of natural resources have been carried out at a global level on the theme of "Sustainable Development". Along with the quantitative growth of Chinese public buildings, especially the Large-Scale Public Buildings, the levels of energy consumption are rising and this phenomenon has been seen as a key point of energy management from a national view. This paper discusses the realities of energy consumption and the energy-saving policies of public buildings in the world’s major developed countries, and sums up the current condition of Chinese energy consumption in relation to public buildings. With regard to these factors, this paper tries to find approaches for solving the existing problems in each construction section. Less developed construction technology in China leads to a waste of materials and labor force. There is a lack of professional personnel to handle the operational management and a lack of consideration for energy efficiency in the designing process. There is not a suitable framework for compiling statistics and data on energy consumption. There are still many management problems such as inadequate policy standards in operability or implementation, unclear positioning in governmental management, ineffective incentive or punitive mechanisms, and a defective state system which results directly in an undeveloped service system for energy efficiency. Based on all of the problems listed above, this paper suggests solutions in four areas, namely Policy, Energy Statistics, Management and Education, in order to give academic support for the overhaul of Large-Scale Public Buildings towards greater energy efficiency in China.
www.ima.kth.se

The European Performance of Buildings Directive and the United Kingdom Climate Change Act have resulted in a range of measures aimed at lowering building energy consumption. This framework uses regulatory, market based and other levers to encourage reductions in consumption and associated carbon emissions. Parallel to this is a set of drivers generated by social, economic and broader professional responsibilities. These include reputational pressures, personal and organisation ambitions, economic risks and societal pressures. This study used a mixed methodology to define this combination of influences as the ‘contextual pressures’. An initial literature review was combined with a phase of empirical research through participant observation in the early stage development of the CarbonBuzz platform to add practitioner insight to the framework. The role that energy information feedback currently plays in design, construction and management practice was then investigated. Three data collection and analysis phases were carried out: an industry-wide web-based survey; secondary energy consumption data from the CarbonBuzz platform and semi-structured interviews aimed at understanding which pressures have greatest impact on actors’ practice. A framework is proposed for the future role that crowd sourced energy information feedback could play in design, construction and management practice. The final phase synthesises the quantitative and qualitative data to identify what a future crowd sourced data platform must offer to meet the aspirations and motivations of actors working within the contextual pressures and the macro-aim of lower carbon emissions. This concludes with suggested alterations required to the contextual pressures to facilitate this. Recommendations are made for adjustments to the framework to increase participation in building evaluation targeted at the specifics of the energy gap and the motivations of industrial actors. Finally, further work is identified to facilitate and evaluate any future changes to the contextual pressures.

In the UK, buildings contribute around one third of the energy-related greenhouse gas emissions. Space heating and cooling systems are among the biggest power consumers in buildings. Thus, improvement of energy efficient of HVAC systems will play a significant role in achieving the UK carbon reduction target. This research aims to develop a novel Building Energy Management System (BEMS) to reduce the energy consumption of the HVAC system while fulfilling occupants’ thermal comfort requirements. The proposed system not only considers the occupants’ adaptations when making decisions on the set temperature, but also influences occupants’ behaviours by providing them with suggestions that help eliminate unnecessary heating and cooling. Multi-agent technologies are applied to design the BEMS’s architecture. The Epistemic-Deontic-Axiologic (EDA) agent model is applied to develop the structure of the agents inside the system. The EDA-based agents select their optimal action plan by considering the occupants’ thermal sensations, their behavioural adaptations and the energy consumption of the HVAC system. Each aspect is represented by its relevant objective function. Newly-developed personal thermal sensation models and group-of-people-based thermal sensation models generated by support vector machine based algorithms are applied as objective functions to evaluate the occupants’ thermal sensations. Equations calculating heating and cooling loads are used to represent energy consumption objectives. Complexities of adaptive behaviours and confidence of association rules between behaviours and thermal sensations are used to build objective functions of behavioural adaptations. In order to make decisions by considering the above objectives, novel multi-objective decision-making algorithms are developed to help the BEMS system make optimal decisions on HVAC set temperature and suggestions to the occupants. Simulation results prove that the newly-developed BEMS can help the HVAC system reduce energy consumption by up to 10% while fulfilling the occupants’ thermal comfort requirements.

Commercial buildings consume more than 40% of the total energy consumption in the United States. Almost 90% of these buildings are small- and medium-sized buildings that do not have a Building Energy Management (BEM) system. The reasons behind this are – lack of awareness, unavailability of inexpensive packaged solutions, and disincentive to invest in a BEM system if the tenant is not the owner. Several open source tools and technologies have emerged recently that can be used for building automation and energy management. However, none of these systems is turnkey and deployment ready. They also lack consistent and intuitive navigation, security, and performance required for a BEM system. The overall project - of which this thesis research is a part - addresses the design and implementation of an open source secure web based user platform to monitor, schedule, control, and perform functions needed for a BEM system serving small and medium-size buildings. The focus of this work are: principles of intuitive graphical user interface design, abstracting device functions into a comprehensive data model, identifying threats and vulnerabilities, and implementing a security framework for the web platform. Monitor and control solutions for devices such as load controllers and sensors are abstracted and their decentralized control strategies are proposed and implemented using an open source robust scalable user platform accessible locally and remotely. The user platform is open-source, scalable, provides role-based access, dynamic, and modular in design. The comprehensive data model includes a user management model, device model, session model, and a scheduling model. The data model is designed to be flexible, robust and can be extended for any new device type. Security risks are analyzed using a threat model to identify security goals. The proposed security framework includes user authentication, device approval, role-based access, secure information exchange protocols, and web platform security. Performance of the user interface platform is evaluated for responsiveness in different screen sizes, page response times, throughput, and the performance of client side entities.
Master of Science

The objective of the proposed research is to develop an intelligent load modeling, identification, and prediction technology to provide granular load energy consumption and performance details and drive building energy reduction, demand reduction, and proactive equipment maintenance. Electricity consumption in commercial and residential sectors accounts for about 70% of the total electricity generation in United States. Buildings are the most important consumers, and contribute to over 80% of the consumptions in these two sectors. To reduce electrical energy spending and carbon emission, several studies from Pacific Northwest National Lab (PNNL) and National Renewable Energy Lab (NREL) prove that if equipped with the proper technologies, a commercial or a residential building can potentially improve energy savings of buildings by up to about 10% to 30% of their usage. However, the market acceptance of these new technologies today is still not sufficient, and the reason is generally acknowledged to be the lack of solution to quantify the contributions of these new technologies to the energy savings, and the invisibility of the loads in buildings. A non-intrusive load monitoring (NILM) system is proposed in this dissertation, which can identify every individual load in buildings and record the energy consumption, time-of-day variations and other relevant statistics of the identified load, with no access to the individual component. The challenge of such a non-intrusive load monitoring is to find features that are unique for a particular load and then to match a measured feature of an unknown load against a database or library of known. Many problems exist in this procedure and the proposed research is going to focus on three directions to overcome the bottlenecks. They are respectively fundamental load studies for a model-driven feature extraction, adaptive identification algorithms for load space extendibility, and the practical simplifications for the real industrial applications. The simulation results show the great potentials of this new technology in building energy monitoring and management.