Dissertations / Theses on the topic 'Energy consumption – Ontario – Forecasting'

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1

Bae, Kyungcho. "Energy consumption forecasting: Econometric model vs state space model." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/187010.

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This study examines the forecasting performance of two major multivariate methodologies: econometric modeling and multivariate state space modeling. The same variables are used in both models to facilitate comparison. They are evaluated by both expost and exante accuracy of U.S. energy consumption forecasts. Econometric models are highly simplified and a model selection procedure is applied to the models. Two different formats of multivariate state space models are examined: economic structure and identity structure. Goodrich's algorithm is employed to estimate the state space models. The state space models in both the econometric structure and the identity structure provided generally good estimates, usually, but not always, these forecasts were more accurate than those by the single econometric models.
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2

He, Miaofen 1976. "Assessing the economic feasibility of a carbon tax on energy inputs in Ontario's pulp and paper industry : an econometric analysis." Thesis, McGill University, 2001. http://digitool.Library.McGill.CA:80/R/?func=dbin-jump-full&object_id=31232.

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Knowledge of price responsiveness of energy is important for designing effective price-based controls to curb the GHG emissions in Canada. The translog and logit models are developed in this study to analyze the demand for four types of energy inputs: coal, electricity, natural gas and refined petroleum products in Ontario's pulp and paper industry. The results suggest that the industry is inelastic to price change of energy consumed. Tests indicate that the translog model behaves slightly better than the logit model. The translog model was then applied to study the feasibility of imposing a carbon tax on energy inputs on Ontario's pulp and paper industry, which indicated that this sector does not seem to response to changes in energy inputs prices. Therefore, a carbon tax does not seem to be a good policy option for decreasing greenhouse gas emissions in this sector.
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3

Dodds, Gordon Ivan. "Modelling and forecasting electricity demand using aggregate and disaggregate data." Thesis, Queen's University Belfast, 1988. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.306073.

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4

McCafferty, Peter. "Forecasting electricity demand in the industrial sector based on disaggregate data." Thesis, Queen's University Belfast, 1991. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.385049.

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5

Cheng, Yuanzhi. "Forecasting by learning methods: The gross domestic product, total energy consumption and petroleum consumption of the United States." Diss., The University of Arizona, 1994. http://hdl.handle.net/10150/186631.

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This study generalizes the applications of learning curve theory. It extends the simple power learning model in two ways: (1) by extending the model to include other sift variables, the extensive learning model; (2) by generalizing the functional relationship to give greater flexibility in modelling the learning curve, the translog learning model. Through empirical analyses of gross domestic product, total energy consumption, petroleum consumption, and petroleum products consumption, different learning curve models are explored and compared.
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6

Muendej, Krisanee. "Predictions of monthly energy consumption and annual patterns of energy usage for convenience stores by using multiple and nonlinear regression models." Texas A&M University, 2004. http://hdl.handle.net/1969.1/1221.

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Thirty convenience stores in College Station, Texas, have been selected as the samples for an energy consumption prediction. The predicted models assist facility energy managers for making decisions of energy demand/supply plans. The models are applied to historical data for two years: 2001 and 2002. The approaches are (1) to analyze nonlinear regression models for long term forecasting of annual patterns compared with outdoor temperature, and (2) to analyze multiple regression models for the building type regardless of outdoor temperature. In the first approach, twenty four buildings are categorized as base load group and no base group. Average temperature, cooling efficiencies, and cooling knot temperature are estimated by nonlinear regression models: segment and parabola models. The adjusted r-square results in good performance up to ninety percent accuracy. In the second approach, the other selected six buildings are categorized as no trend group. This group does not respond to outdoor temperature. As the result, multiple a regression model is formed by combination of variables from the nonlinear models and physical building variables of cooling efficiency, cooling temperature, light bulbs, area, outdoor temperature, and orientation of fronts. This model explains up to sixty percent of all convenience stores' data. In conclusion, the accuracy of prediction models is measured by the adjusted r-square results. Among these three models, the multiple regression model shows the highest adjusted r-square (0.597) over the parabola (0.5419) and segment models (0.4806). When the three models come to the application, the multiple regression model is best fit for no trend data type. However, when it is used to predict the energy consumption with the buildings that relate to outdoor temperature, segment and parabola model provide a better prediction result.
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7

Hadjipaschalis, Constantinos. "An investigation of artificial neural networks applied to monthly electricity peak demand and energy forecasting." Thesis, Imperial College London, 1998. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.286627.

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8

Mangisa, Siphumlile. "Statistical analysis of electricity demand profiles." Thesis, Nelson Mandela Metropolitan University, 2013. http://hdl.handle.net/10948/d1011548.

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An electricity demand profile is a graph showing the amount of electricity used by customers over a unit of time. It shows the variation in electricity demand versus time. In the demand profiles, the shape of the graph is of utmost importance. The variations in demand profiles are caused by many factors, such as economic and en- vironmental factors. These variations may also be due to changes in the electricity use behaviours of electricity users. This study seeks to model daily profiles of energy demand in South Africa with a model which is a composition of two de Moivre type models. The model has seven parameters, each with a natural interpretation (one parameter representing minimum demand in a day, two parameters representing the time of morning and afternoon peaks, two parameters representing the shape of each peak, and two parameters representing the total energy per peak). With the help of this model, we trace change in the demand profile over a number of years. The proposed model will be helpful for short to long term electricity demand forecasting.
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9

Sakva, Denys. "Evaluation of errors in national energy forecasts /." Link to online version, 2005. https://ritdml.rit.edu/dspace/handle/1850/1166.

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10

Trujillo, Iliana Cardenes. "Quantifying the energy consumption of the water use cycle." Thesis, University of Oxford, 2017. https://ora.ox.ac.uk/objects/uuid:df481801-cce1-4824-986c-612f4673b8eb.

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The management and delivery of water and wastewater consume significant amounts of energy, mostly in the form of electricity. With increasing populations, climate change, water quality issues and increasing energy prices, it is more important than ever to understand energy consumption patterns. Energy usually represents the largest operational cost in water utilities around the world, yet there is limited work aiming to quantify the specific relationship between water and its associated energy, and understand its implications for future decision-making. This thesis presents variousmethodological approachesto quantify and understand energy use in water infrastructure systems, as well as how to incorporate them in decision-making processes. The main hypotheses are as follows: firstly, a detailed understanding of the use of energy in water infrastructure systems can facilitate more efficient and sustainable water infrastructure systems and, secondly, that incorporating energy into planning for water and wastewater resources can help understand the impacts of decisions and establish trade-offs between actions. To test these hypotheses, the thesis presents an analytical approach to various areas. Firstly, it identifies, maps and quantifies the energy consumption patterns within a water infrastructure system. This is then used to identify inefficiencies and areas of potential energy saving. Secondly, it incorporates detailed energy costs into short and long-term water resources management and planning. Thirdly, it evaluates trade-offs between energy costs and changing effluent quality regulations in wastewater resources. The Thames River basin, in the south-east of England, is used as a case study to illustrate the approach. The results demonstrate that a systematic approach to the quantification of energy use in a water infrastructure system can identify areas of inefficiencies that can be used to make decisions with regards to infrastructure planning. For example, water systems have significant geo-spatial variations in energy consumption patterns that can be addressed specifically to reduce negative trade-offs. The results also show that incorporating detailed energy information into long-term water resources planning can alter the choices made in water supply options, by providing more complete information. Furthermore, methodologically, they show how several methodological approaches can be used to support more complete decision-making in water utilities to reduce short and long-term costs. In this particular case study, the results show that there are important differences in energy consumption by region, and significant differences in the seasonal and energy patterns of water infrastructure systems. For example, water treatment was shown to be the largest consumer of energy within the whole system, compared with pumping or wastewater treatment; but wastewater treatment energy consumption was shown to be the fastest growing over time due to changes in water quality regulatory frameworks. The results show that more stringent effluent standards could result in at least a doubling of electricity consumption and an increase of between 1.29 and 2.30 additional million tonnes of CO2 a year from treating wastewater in large works in the UK. These are projected to continue to increase if the decarbonisation of the electricity grid does not occur fast enough. Finally, the thesis also shows that daily energy consumption can be reduced by up to 18% by optimally routing water through a water network. optimization of water networks, and that a change in discount rates could change the daily operating costs by 19%, that in turn leads to a resulting different set of optimal investment options in a water supply network.
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11

Nedzingahe, Livhuwani. "Forecasting models for operational and tactical requirements in electricity consumption: The case of the Ferrochrome Sector in South Africa." Thesis, University of Limpopo (Medunsa Campus ), 2010. http://hdl.handle.net/10386/1150.

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Thesis (Mathematics) -- University of Limpopo, 2010
Forecasting electricity consumption is a challenge for most power utilities. In South Africa the anxiety posed by electricity supply disruption is a cause for concern in sustainable energy planning. Accurate forecasting of future electricity consumption has been identified as an essential input to this planning process. Forecasting electricity consumption has been widely researched and several methodologies suggested. However, various methods that have been proposed by a number of researchers are dependent on environment and market factors related to the scope of work under study making portability a challenge. The aim of this study is to investigate models to forecast short term electricity consumption for operational use and medium term electricity consumption for tactical use in the Ferrochrome sector in South Africa. An Autoregressive Moving Average method is suggested as an appropriate tool for operational planning. The Holt-Winter Linear seasonal smoothing method is suggested for tactical planning. Keywords: Forecasting, electricity consumption, operational planning, tactical planning, ARIMA, Holt-Winter Linear seasonal smoothing, Ferrochrome sector
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12

Leung, Chun Kai. "Sectoral consumption of oil in China, 1990-2006." HKBU Institutional Repository, 2009. http://repository.hkbu.edu.hk/etd_ra/1078.

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13

Si, Yau-li, and 史有理. "Forecasts of electricity demand and their implication for energy developments in Hong Kong." Thesis, The University of Hong Kong (Pokfulam, Hong Kong), 1990. http://hub.hku.hk/bib/B31976384.

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14

SILVA, FELIPE LEITE COELHO DA. "BAYESIAN STOCHASTIC EXTENSION OF DETERMINISTIC BOTTOM-UP APPROACH FOR THE LONG TERM FORECASTING OF ENERGY CONSUMPTION." PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO, 2017. http://www.maxwell.vrac.puc-rio.br/Busca_etds.php?strSecao=resultado&nrSeq=33006@1.

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PONTIFÍCIA UNIVERSIDADE CATÓLICA DO RIO DE JANEIRO
COORDENAÇÃO DE APERFEIÇOAMENTO DO PESSOAL DE ENSINO SUPERIOR
PROGRAMA DE EXCELENCIA ACADEMICA
O comportamento do consumo de energia elétrica do setor industrial tem sido amplamente investigado ao longo dos últimos anos, devido a sua importância econômica, social e ambiental. Mais especificamente, o consumo de eletricidade dos subsetores da indústria brasileira exerce grande importância para o sistema energético brasileiro. Neste contexto, as projeções de longo prazo do seu consumo de energia elétrica para um país ou uma região são informações de grande relevância na tomada de decisão de órgãos e entidades que atuam no setor energético. A abordagem bottom-up determinística tem sido utilizada para obter a previsão de longo prazo em diversas áreas de pesquisa. Neste trabalho, propõe-se uma metodologia que combina a abordagem bottom-up com os modelos lineares hierárquicos para a previsão de longo prazo considerando os cenários de eficiência energética. Além disso, foi utilizada a inferência bayesiana para a estimação dos parâmetros do modelo, permitindo a incorporação de incerteza nessas previsões. Os resultados utilizando os dados de consumo de eletricidade de subsetores da indústria brasileira mostraram que a metodologia proposta consegue capturar a tajetória do consumo de eletricidade, em particular, dos subsetores de papel e celulose, e de metais não-ferrosos e outros de metalurgia. Por exemplo, os intervalos de credibilidade de 95 por cento construídos a partir do modelo estocástico contemplam os valores reais observados nos anos de 2015 e 2016.
The electricity consumption behaviour in the Brazilian industry has been extensively investigated over the past years due to its economic, social and environmental importance. Specifically, the electricity consumption of the subsectors of Brazilian industry have great importance for the Brazilian energy system. In this context, the long-term projections of energy consumption of a country or region are highly relevant information to decision-making of organs and entities operating in the energy sector. The deterministic bottom-up approach has been used for the long-term forecast in several areas of research. In this paper, we propose a methodology that combines the bottom-up approach with hierarchical linear models for long-term forecasting considering energy efficiency scenarios. In addition, Bayesian inference was used to estimate the parameters of the model, allowing the uncertainty incorporation in these forecasts. The results using the electricity consumption data from subsectors of the Brazilian industry showed that the proposed methodology is able to capture the trajectory of their electricity consumption, in particular of the pulp and paper, and of non-ferrous metals and other metallurgical subsectors. For example, the 95 percent credibility intervals constructed from the stochastic model contemplate the actual values observed in the years 2015 and 2016.
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15

CARON, MATHIEU. "Long-term forecasting model for future electricity consumption in French non-interconnected territories." Thesis, KTH, Skolan för industriell teknik och management (ITM), 2021. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-299457.

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In the context of decarbonizing the electricity generation of French non-interconnected territories, the knowledge of future electricity demand, in particular annual and peak demand in the long-term, is crucial to design new renewable energy infrastructures. So far, these territories, mainly islands located in the Pacific and Indian ocean, relies mainly on fossil fuels powered facilities. Energy policies envision to widely develop renewable energies to move towards a low-carbon electricity mix by 2028.  This thesis focuses on the long-term forecasting of hourly electricity demand. A methodology is developed to design and select a model able to fit accurately historical data and to forecast future demand in these particular territories. Historical data are first analyzed through a clustering analysis to identify trends and patterns, based on a k-means clustering algorithm. Specific calendar inputs are then designed to consider these first observations. External inputs, such as weather data, economic and demographic variables, are also included.  Forecasting algorithms are selected based on the literature and they are than tested and compared on different input datasets. These input datasets, besides the calendar and external variables mentioned, include different number of lagged values, from zero to three. The combination of model and input dataset which gives the most accurate results on the testing set is selected to forecast future electricity demand. The inclusion of lagged values leads to considerable improvements in accuracy. Although gradient boosting regression features the lowest errors, it is not able to detect peaks of electricity demand correctly. On the contrary, artificial neural network (ANN) demonstrates a great ability to fit historical data and demonstrates a good accuracy on the testing set, as well as for peak demand prediction. Generalized additive model, a relatively new model in the energy forecasting field, gives promising results as its performances are close to the one of ANN and represent an interesting model for future research.  Based on the future values of inputs, the electricity demand in 2028 in Réunion was forecasted using ANN. The electricity demand is expected to reach more than 2.3 GWh and the peak demand about 485 MW. This represents a growth of 12.7% and 14.6% respectively compared to 2019 levels.
I samband med utfasningen av fossila källor för elproduktion i franska icke-sammankopplade territorier är kunskapen om framtida elbehov, särskilt årlig förbrukning och topplast på lång sikt, avgörande för att utforma ny infrastruktur för förnybar energi. Hittills är dessa territorier, främst öar som ligger i Stilla havet och Indiska oceanen, beroende av anläggningar med fossila bränslen. Energipolitiken planerar att på bred front utveckla förnybar energi för att gå mot en koldioxidsnål elmix till 2028.  Denna avhandling fokuserar på den långsiktiga prognosen för elbehov per timme. En metod är utvecklad för att utforma och välja en modell som kan passa korrekt historisk data och för att förutsäga framtida efterfrågan inom dessa specifika områden. Historiska data analyseras först genom en klusteranalys för att identifiera trender och mönster, baserat på en k-means klusteralgoritm. Specifika kalenderinmatningar utformas sedan för att beakta dessa första observationer. Externa inmatningar, såsom väderdata, ekonomiska och demografiska variabler, ingår också.  Prognosalgoritmer väljs utifrån litteraturen och de testas och jämförs på olika inmatade dataset. Dessa inmatade dataset, förutom den nämnda kalenderdatan och externa variabler, innehåller olika antal fördröjda värden, från noll till tre. Kombinationen av modell och inmatat dataset som ger de mest exakta resultaten på testdvärdena väljs för att förutsäga framtida elbehov. Införandet av fördröjda värden leder till betydande förbättringar i exakthet. Även om gradientförstärkande regression har de lägsta felen kan den inte upptäcka toppar av elbehov korrekt. Tvärtom, visar artificiella neurala nätverk (ANN) en stor förmåga att passa historiska data och visar en god noggrannhet på testuppsättningen, liksom för förutsägelse av toppefterfrågan. En generaliserad tillsatsmodell, en relativt ny modell inom energiprognosfältet, ger lovande resultat eftersom dess prestanda ligger nära den för ANN och representerar en intressant modell för framtida forskning.  Baserat på de framtida värdena på indata, prognostiserades elbehovet 2028 i Réunion med ANN. Elbehovet förväntas nå mer än 2,3 GWh och toppbehovet cirka 485 MW. Detta motsvarar en tillväxt på 12,7% respektive 14,6% jämfört med 2019 års nivåer.
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16

Wöstmann, Corinna. "Energy Consumption - Forecasting for Linköping and Helsingborg : A Comparison of SARIMA Modelling and Double Seasonal Holt-Winters Exponential Smoothing." Thesis, Linnéuniversitetet, Institutionen för matematik (MA), 2013. http://urn.kb.se/resolve?urn=urn:nbn:se:lnu:diva-28318.

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Energy consumption forecasting is important for ecient planning of electricity production in order to minimize costs and also to ensure that the demand will be covered. In this thesis two methods are suggested: a combination of SARIMA modelling and regression, and forecasting by using the double seasonal Holt-Winters algorithm for exponential smoothing. Both methods are tested on energy consumption data for the regions Linkoping and Helsingborg provided by Bixia. Private and industry clients are regarded separately for regular days and holidays. The exponential smoothing method leads to good results for dierent forecasting horizons. Therefore an easier method, that is at least competitive to forecasts with SARIMA models, is found.
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17

Buzatoiu, Roxana. "Long Term Forecasting of Industrial Electricity Consumption Data With GRU, LSTM and Multiple Linear Regression." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2020. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-289632.

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Accurate long-term energy consumption forecasting of industrial entities is of interest to distribution companies as it can potentially help reduce their churn and offer support in decision making when hedging. This thesis work presents different methods to forecast the energy consumption for industrial entities over a long time prediction horizon of 1 year. Notably, it includes experimentations with two variants of the Recurrent Neural Networks, namely Gated Recurrent Unit (GRU) and Long-Short-Term-Memory (LSTM). Their performance is compared against traditional approaches namely Multiple Linear Regression (MLR) and Seasonal Autoregressive Integrated Moving Average (SARIMA). Further on, the investigation focuses on tailoring the Recurrent Neural Network model to improve the performance. The experiments focus on the impact of different model architectures. Secondly, it focuses on testing the effect of time-related feature selection as an additional input to the Recurrent Neural Network (RNN) networks. Specifically, it explored how traditional methods such as Exploratory Data Analysis, Autocorrelation, and Partial Autocorrelation Functions Plots can contribute to the performance of RNN model. The current work shows through an empirical study on three industrial datasets that GRU architecture is a powerful method for the long-term forecasting task which outperforms LSTM on certain scenarios. In comparison to the MLR model, the RNN achieved a reduction in the RMSE between 5% up to to 10%. The most important findings include: (i) GRU architecture outperforms LSTM on industrial energy consumption datasets when compared against a lower number of hidden units. Also, GRU outperforms LSTM on certain datasets, regardless of the choice units number; (ii) RNN variants yield a better accuracy than statistical or regression models; (iii) using ACF and PACF as dicovery tools in the feature selection process is unconclusive and unefficient when aiming for a general model; (iv) using deterministic features (such as day of the year, day of the month) has limited effects on improving the deep learning model’s performance.
Noggranna långsiktiga energiprognosprognoser för industriella enheter är av intresse för distributionsföretag eftersom det potentiellt kan bidra till att minska deras churn och erbjuda stöd i beslutsfattandet vid säkring. Detta avhandlingsarbete presenterar olika metoder för att prognostisera energiförbrukningen för industriella enheter under en lång tids förutsägelsehorisont på 1 år. I synnerhet inkluderar det experiment med två varianter av de återkommande neurala nätverken, nämligen GRU och LSTM. Deras prestanda jämförs med traditionella metoder, nämligen MLR och SARIMA. Vidare fokuserar undersökningen på att skräddarsy modellen för återkommande neurala nätverk för att förbättra prestanda. Experimenten fokuserar på effekterna av olika modellarkitekturer. För det andra fokuserar den på att testa effekten av tidsrelaterat funktionsval som en extra ingång till RNN -nätverk. Specifikt undersökte den hur traditionella metoder som Exploratory Data Analysis, Autocorrelation och Partial Autocorrelation Funtions Plots kan bidra till prestanda för RNN -modellen. Det aktuella arbetet visar genom en empirisk studie av tre industriella datamängder att GRU -arkitektur är en kraftfull metod för den långsiktiga prognosuppgiften som överträffar ac LSTM på vissa scenarier. Jämfört med MLR -modellen uppnådde RNN en minskning av RMSE mellan 5 % upp till 10 %. De viktigaste resultaten inkluderar: (i) GRU -arkitekturen överträffar LSTM på datauppsättningar för industriell energiförbrukning jämfört med ett lägre antal dolda enheter. GRU överträffar också LSTM på vissa datauppsättningar, oavsett antalet valenheter; (ii) RNN -varianter ger bättre noggrannhet än statistiska modeller eller regressionsmodeller; (iii) att använda ACF och PACF som verktyg för upptäckt i funktionsvalsprocessen är otydligt och ineffektivt när man siktar på en allmän modell; (iv) att använda deterministiska funktioner (t.ex. årets dag, månadsdagen) har begränsade effekter på att förbättra djupinlärningsmodellens prestanda.
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Волощук, Андрій Володимирович, and Andrii Volodymyrovych Voloshchuk. "Методи і засоби автоматизованого збору та прогнозування середньострокового енергоспоживання на рівні області." Master's thesis, Тернопільський національний технічний університет імені Івана Пулюя, 2021. http://elartu.tntu.edu.ua/handle/lib/36537.

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У кваліфікаційній роботі магістра досліджено методи і засоби автоматизованого збору та прогнозування середньострокового енергоспоживання на рівні області. Проведено аналіз сучасних тенденцій проектування комп’ютерних систем у сфері управління енергетичними ресурсами і встановлено, що доцільними та ефективними технологіями їхнього проектування є використання IoT пристроїв, як кінцевих пристроїв та пристроїв керування у відповідних мережах комунікації. Обгрунтовано та спроектовано архітектуру інтелектуального лічильника електроспоживання, що дало можливість забезпечити оптимізацію та прогнозування енергоспоживання та раціональної експлуатації підключених пристроїв. Досліджено моделі прогнозування енергоспоживання на основі авторегресії, експоненційного згладжування та нейронних мереж, а також виконано їхню програмну реалізовано за допомогою мови програмування Python, що дало можливість одержати точні результати прогнозу з похибкою на рівні 3%-4%.
In the master's thesis the methods and means of automated collection and forecasting of medium-term energy consumption at the regional level are investigated. An analysis of current trends in the design of computer systems in the field of energy management and found that appropriate and effective technologies for their design is the use of IoT devices as end devices and control devices in the relevant communication networks. The architecture of the intelligent electricity consumption meter was substantiated and designed, which made it possible to provide optimization and forecasting of energy consumption and rational operation of connected devices. Models of energy consumption forecasting based on autoregression, exponential smoothing and neural networks were studied, and their software was implemented using the Python programming language, which made it possible to obtain accurate forecast results with an error of 3% -4%.
ПЕРЕЛІК ОСНОВНИХ УМОВНИХ ПОЗНАЧЕНЬ, СИМВОЛІВ І СКОРОЧЕНЬ ...8 ВСТУП ... 9 РОЗДІЛ 1 АНАЛІЗ СУЧАСНИХ СИСТЕМ ЗБОРУ ТА ПРОГНОЗУВАННЯ ЕНЕРГОСПОЖИВАННЯ ...13 1.1. Аналіз особливостей споживачів електричної енергії ...13 1.2. Аналіз виробництва і споживання електроенергії в Україні ...17 1.3. Аналіз сучасних трендів при проектуванні комп’ютерних систем управління енергетичними ресурсами ...20 1.4. Аналіз підходів до прогнозування енергоспоживання ...22 РОЗДІЛ 2 МАТЕМАТИЧНЕ ТА АПАРАТНЕ ЗАБЕЗПЕЧЕННЯ СИСТЕМИ ОБЛІКУ ТА ПРОГНОЗУВАННЯ СПОЖИВАННЯ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ ...31 2.1. Проектування інтелектуальної системи обліку електричної енергії ...31 2.2. Обґрунтування моделей прогнозування середньострокового енергоспоживання на рівні області ...41 2.2.1. Моделі авторегресії ...41 2.2.2. Підхід експоненційного згладжування при прогнозуванні споживання електроенергії ...49 2.2.3. Методи машинного навчання ...52 РОЗДІЛ 3 АПРОБАЦІЯ СИСТЕМИ ОБЛІКУ ЕЛЕКТРИЧНОЇ ЕНЕРГІЇ ТА ПРОГРАМНА ІМПЛЕМЕНТАЦІЯ МОДЕЛЕЙ ПРОГНОЗУВАННЯ ЕЛЕКТРОСПОЖИВАННЯ ...56 3.1. Архітектура і результати обліку електричної енергії у системі середньострокового прогнозування споживання електроенергії ...56 3.2. Аналіз даних для прогнозування споживання електроенергії ...61 3.3. Дослідження та аналіз характеристик часового ряду енергоспоживання на рівні області ... 64 3.4. Побудова моделей прогнозування енергоспоживання на рівні області та порівняння результатів моделювання ...707 3.4.1. Моделі на основі експоненційного згладжування ...71 3.4.2. Моделі на основі нейронних мереж ...77 РОЗДІЛ 4 ОХОРОНА ПРАЦІ ТА БЕЗПЕКА В НАДЗВИЧАЙНИХ СИТУАЦІЯХ ...81 4.1. Охорона праці ...81 4.2. Вплив стихійних лих, аварій (катастроф) та їх наслідки ...83 ВИСНОВКИ ...88 СПИСОК ВИКОРИСТАНИХ ДЖЕРЕЛ ...90 Додаток А Тези конференцій ...93
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19

Katayama, Munechika. "Dynamic analysis in productivity, oil shock, and recession." Diss., Connect to a 24 p. preview or request complete full text in PDF format. Access restricted to UC campuses, 2008. http://wwwlib.umi.com/cr/ucsd/fullcit?p3315857.

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Thesis (Ph. D.)--University of California, San Diego, 2008.
Title from first page of PDF file (viewed September 3, 2008). Available via ProQuest Digital Dissertations. Vita. Includes bibliographical references (p. 98-104).
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20

Oliveira, Silvia Fernanda. "Predicting the growth of industrial production: an analysis from the demand for electric energy in the state of Ceara." Universidade Federal do CearÃ, 2011. http://www.teses.ufc.br/tde_busca/arquivo.php?codArquivo=7387.

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The timeliness with which economic agents make their decisions encourages the development of tools in order to anticipate changes in economic aggregates. Following this, the study analyses and develops models to forecast the industrial production from Cearà energy consumption measured by COELCE. Vector Autoregressive models (VAR) are estimated and scenarios are constructed for the period 2011-2012. The estimates were robust and the simulations indicated an increase of 3.9% in industrial production Cearà until 2012, comparing with the values observed in 2010, without seasonal effects.
A tempestividade com a qual os agentes econÃmicos tomam suas decisÃes estimula o desenvolvimento de ferramentas que permitam antecipar as mudanÃas nos agregados econÃmicos. Deste modo, o estudo realiza uma anÃlise e previsÃo da produÃÃo industrial cearense a partir do consumo de energia elÃtrica mensurado pela COELCE. Modelos Vetoriais Auto-regressivos (VAR) sÃo estimados e cenÃrios sÃo construÃdos para o perÃodo 2011-2012. As estimativas se mostraram robustas com um elevado poder de explicaÃÃo do modelo e as simulaÃÃes indicaram um crescimento de 3,9% da produÃÃo industrial cearense atà 2012 com base no valor verificado em 2010, jà desconsiderando as influÃncias sazonais.
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21

Moura, Fernando Alves de. "Previsão do consumo de energia elétrica por setores através do modelo SARMAX." Universidade de São Paulo, 2011. http://www.teses.usp.br/teses/disponiveis/12/12139/tde-15122011-180812/.

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A previsão do consumo de energia elétrica do Brasil é muito importante para os órgãos reguladores do setor. Uma série de metodologias têm sido utilizadas para a projeção desse consumo. Destacam-se os modelos de regressão com dados em painel, modelos de cointegração e defasagem distribuída, modelos estruturais de séries temporais e modelos de Box & Jenkins de séries temporais, dentre outros. Neste trabalho estimar-se um modelo de previsão do consumo comercial, industrial e residencial de energia brasileiro por meio de modelos SARMAX. Nesses modelos o consumo de energia pode ser estimado por meio de uma regressão linear múltipla considerando diversas variáveis macroeconômicas como variáveis explicativas. Os resíduos desse modelo são explicados por meio de um modelo de Box & Jenkins. Neste estudo realiza-se uma pesquisa bibliográfica sobre fatores que influenciam no consumo de energia elétrica e levantam-se variáveis proxies para prever este consumo no Brasil. Utiliza-se uma base de dados mensal no período entre Janeiro de 2003 e Setembro de 2010 para construção de cada um dos três modelos de previsão citados. Utilizase uma amostra de validação de Outubro de 2010 até Fevereiro de 2011. Realiza-se a avaliação dos modelos estimados em termos de adequação às premissas teóricas e ao desempenho nas medidas de acurácia MAPE, RMSE e coeficiente de determinação ajustado. Os modelos estimados para o consumo de energia elétrica dos setores comercial, industrial e residencial obtêm um MAPE de 2,05%, 1,09% e 1,27%; um RMSE de 144,13, 185,54 e 158,40; e um coeficiente de determinação ajustado de 95,91%, 93,98% e 96,03% respectivamente. Todos os modelos estimados atendem os pressupostos de normalidade, ausência de autocorrelação serial e ausência de heterocedasticidade condicionada dos resíduos. Os resultados confirmaram a viabilidade da utilização das variáveis macroeconômicas testadas para estimar o consumo de energia elétrica por setores e a viabilidade da metodologia para a previsão destas séries na amostra de dados selecionada.
The prediction of electricity consumption in Brazil is very important to the industry regulators. A number of methodologies have been used for the projection of this consumption. Noteworthy are the regression models with data in panel, co-integration and distributed lag models, time series structural models and Box & Jenkins time series models among others. In this work we intend to estimate a forecasting model of the Brazilian commercial, industrial and residential consumption of energy by means of SARMAX models. In these models the power consumption can be estimated by a multiple linear regression considering various macro-economic variables as explanatory variables. The residues of this model are explained by a Box & Jenkins model. In this study it is carried out a bibliographic research on factors that influence energy consumption and proxy variables are risen to predict the consumption in Brazil. The consumption of electricity is estimated for the commercial, industrial and residential sectors. It is used a monthly data base over the period between January 2003 and September 2010 for the construction of each of the three prediction models mentioned. It is used a validation sample from October 2010 to February 2011. It is carried out the assessment of the estimated models in terms of compliance with the theoretical premises and the performance on measures of accuracy MAPE, RMSE and adjusted determinant coefficient. The estimated models for the energy consumption of commercial, industrial and residential sectors obtain a MAPE of 2.05%, 1.09% and 1.27%; a RMSE of 144.13, 185.54 and 158.40; and a adjusted determinant coefficient of 95.91%, 93.98% and 96.03% respectively. All estimated models satisfy the assumptions of normality, absence of serial autocorrelation and absence of conditioned heteroscedasticity of the residues. The results confirmed the viability of the usage of the macroeconomic variables tested to estimate the energy consumption by sector and the viability of the methodology for the prediction of these series in the selected data sample.
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22

Giacometto, Torres Francisco Javier. "Adaptive load consumption modelling on the user side: contributions to load forecasting modelling based on supervised mixture of experts and genetic programming." Doctoral thesis, Universitat Politècnica de Catalunya, 2017. http://hdl.handle.net/10803/457631.

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This research work proposes three main contributions on the load forecasting field: the enhancement of the forecasting accuracy, the enhancement of the model adaptiveness, and the automatization on the execution of the load forecasting strategies implemented. On behalf the accuracy contribution, learning algorithms have been implemented on the basis of machine learning, computational intelligence, evolvable networks, expert systems, and regression approaches. The options for increase the forecasting quality, through the minimization of the forecasting error and the exploitation of hidden insights and miscellaneous properties of the training data, are equally explored in the form of feature based specialized base learners inside of a modelling ensemble structure. Preprocessing and the knowledge discovery algorithms are also implemented in order to boost the accuracy trough cleaning of variables, and to enhance the autonomy of the modelling algorithm via non-supervised intelligent algorithms respectively. The Adaptability feature has been enhanced by the implementation of three components inside of an ensemble learning strategy. The first one corresponds to resampling techniques, it ensures the replication of the global probability distribution on multiple independent training sub-sets and consequently the training of base learners on representatives spaces of occurrences. The second one corresponds to multi-resolution and cyclical analysis techniques; through the decomposition of endogenous variables on their time-frequency components, major insights are acquired and applied on the definition of the ensemble structure layout. The third one corresponds to Self-organized modelling algorithms, which provides of fully customized base learner's. The Autonomy feature is reached by the combination of automatic procedures in order to minimize the interaction of an expert user on the forecasting procedure. Experimental results obtained, from the application of the load forecasting strategies proposed, have demonstrated the suitability of the techniques and methodologies implemented, especially on the case of the novel ensemble learning strategy.
Este trabajo de investigación propone tres aportaciones principales en el campo de la previsión de consumos: la mejora en la exactitud de la predicción, la mejora en la adaptabilidad del modelo ante diferentes escenarios de consumo y la automatización en la ejecución de los algoritmos de modelado y predicción. La mejora de precisión que ha sido introducida en la estrategia de modelado propuesta ha sido obtenida tras la implementación de algoritmos de aprendizaje supervisados pertenecientes a las siguientes familias de técnicas: aprendizaje de máquinas, inteligencia computacional, redes evolutivas, sistemas expertos y técnicas de regresión. Otras las medidas implementadas para aumentar la calidad de la predicción han sido: la minimización del error de pronóstico a través de la extracción de información basada en análisis multi-variable, la combinación de modelos expertos especializados en atributos específicos del perfil de consumo, el uso de técnicas de pre procesamiento para aumentar la precisión a través de la limpieza de variables, y por último implementación de la algoritmos de clasificación no supervisados para obtener los atributos y las clases características del consumo. La mejora en la adaptación del algoritmo de modelado se ha conseguido mediante la implementación de tres componentes al interior de la estrategia de combinación de modelos expertos. El primer componente corresponde a la implementación de técnicas de muestreo sobre cada conjunto de datos agrupados por clase; esto asegura la replicación de la distribución de probabilidad global en múltiples y estadísticamente independientes subconjuntos de entrenamiento. Estos sub conjuntos son usados para entrenar los modelos expertos que consecuentemente pasaran a formar los modelos base de la estructura jerárquica que combina los modelos expertos. El segundo componente corresponde a técnicas de análisis multi-resolución. A través de la descomposición de variables endógenas en sus componentes tiempo-frecuencia, se abstraen e implementan conocimientos importantes sobre la forma de la estructura jerárquica que adoptaran los modelos expertos. El tercero componente corresponde a los algoritmos de modelado que generan una topología interior auto organizada, que proporciona de modelo experto base completamente personalizado al perfil de consumo analizado. La mejora en la automatización se alcanza mediante la combinación de procedimientos automáticos para minimizar la interacción de un usuario experto en el procedimiento de predicción. Los resultados experimentales obtenidos, a partir de la aplicación de las estrategias de predicción de consumos propuestas, han demostrado la idoneidad de las técnicas y metodologías implementadas; sobre todo en el caso de la novedosa estrategia para la combinación de modelos expertos.
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23

Drevna, Michael J. "An application of Box-Jenkins transfer functions to natural gas demand forecasting." Ohio : Ohio University, 1985. http://www.ohiolink.edu/etd/view.cgi?ohiou1183999594.

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24

Brégère, Margaux. "Stochastic bandit algorithms for demand side management Simulating Tariff Impact in Electrical Energy Consumption Profiles with Conditional Variational Autoencoders Online Hierarchical Forecasting for Power Consumption Data Target Tracking for Contextual Bandits : Application to Demand Side Management." Thesis, université Paris-Saclay, 2020. http://www.theses.fr/2020UPASM022.

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L'électricité se stockant difficilement à grande échelle, l'équilibre entre la production et la consommation doit être rigoureusement maintenu. Une gestion par anticipation de la demande se complexifie avec l'intégration au mix de production des énergies renouvelables intermittentes. Parallèlement, le déploiement des compteurs communicants permet d'envisager un pilotage dynamique de la consommation électrique. Plus concrètement, l'envoi de signaux - tels que des changements du prix de l'électricité – permettrait d'inciter les usagers à moduler leur consommation afin qu'elle s'ajuste au mieux à la production d'électricité. Les algorithmes choisissant ces signaux devront apprendre la réaction des consommateurs face aux envois tout en les optimisant (compromis exploration-exploitation). Notre approche, fondée sur la théorie des bandits, a permis de formaliser ce problème d'apprentissage séquentiel et de proposer un premier algorithme pour piloter la demande électrique d'une population homogène de consommateurs. Une borne supérieure d'ordre T⅔ a été obtenue sur le regret de cet algorithme. Des expériences réalisées sur des données de consommation de foyers soumis à des changements dynamiques du prix de l'électricité illustrent ce résultat théorique. Un jeu de données en « information complète » étant nécessaire pour tester un algorithme de bandits, un simulateur de données de consommation fondé sur les auto-encodeurs variationnels a ensuite été construit. Afin de s'affranchir de l'hypothèse d'homogénéité de la population, une approche pour segmenter les foyers en fonction de leurs habitudes de consommation est aussi proposée. Ces différents travaux sont finalement combinés pour proposer et tester des algorithmes de bandits pour un pilotage personnalisé de la consommation électrique
As electricity is hard to store, the balance between production and consumption must be strictly maintained. With the integration of intermittent renewable energies into the production mix, the management of the balance becomes complex. At the same time, the deployment of smart meters suggests demand response. More precisely, sending signals - such as changes in the price of electricity - would encourage users to modulate their consumption according to the production of electricity. The algorithms used to choose these signals have to learn consumer reactions and, in the same time, to optimize them (exploration-exploration trade-off). Our approach is based on bandit theory and formalizes this sequential learning problem. We propose a first algorithm to control the electrical demand of a homogeneous population of consumers and offer T⅔ upper bound on its regret. Experiments on a real data set in which price incentives were offered illustrate these theoretical results. As a “full information” dataset is required to test bandit algorithms, a consumption data generator based on variational autoencoders is built. In order to drop the assumption of the population homogeneity, we propose an approach to cluster households according to their consumption profile. These different works are finally combined to propose and test a bandit algorithm for personalized demand side management
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25

Söderberg, Max Joel, and Axel Meurling. "Feature selection in short-term load forecasting." Thesis, KTH, Skolan för elektroteknik och datavetenskap (EECS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-259692.

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This paper investigates correlation between energy consumption 24 hours ahead and features used for predicting energy consumption. The features originate from three categories: weather, time and previous energy. The correlations are calculated using Pearson correlation and mutual information. This resulted in the highest correlated features being those representing previous energy consumption, followed by temperature and month. Two identical feature sets containing all attributes1 were obtained by ranking the features according to correlation. Three feature sets were created manually. The first set contained seven attributes representing previous energy consumption over the course of seven days prior to the day of prediction. The second set consisted of weather and time attributes. The third set consisted of all attributes from the first and second set. These sets were then compared on different machine learning models. It was found the set containing all attributes and the set containing previous energy attributes yielded the best performance for each machine learning model. 1In this report, the words ”attribute” and ”feature” are used interchangeably.
I denna rapport undersöks korrelation och betydelsen av olika attribut för att förutspå energiförbrukning 24 timmar framåt. Attributen härstammar från tre kategorier: väder, tid och tidigare energiförbrukning. Korrelationerna tas fram genom att utföra Pearson Correlation och Mutual Information. Detta resulterade i att de högst korrelerade attributen var de som representerar tidigare energiförbrukning, följt av temperatur och månad. Två identiska attributmängder erhölls genom att ranka attributen över korrelation. Tre attributmängder skapades manuellt. Den första mängden innehåll sju attribut som representerade tidigare energiförbrukning, en för varje dag, sju dagar innan datumet för prognosen av energiförbrukning. Den andra mängden bestod av väderoch tidsattribut. Den tredje mängden bestod av alla attribut från den första och andra mängden. Dessa mängder jämfördes sedan med hjälp av olika maskininlärningsmodeller. Resultaten visade att mängden med alla attribut och den med tidigare energiförbrukning gav bäst resultat för samtliga modeller.
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26

Maréchal, Kevin. "The economics of climate change and the change of climate in economics: the implications for climate policy of adopting an evolutionary perspective." Doctoral thesis, Universite Libre de Bruxelles, 2009. http://hdl.handle.net/2013/ULB-DIPOT:oai:dipot.ulb.ac.be:2013/210278.

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1. Contextual outline of the PhD Research

Climate change is today often seen as one of the most challenging issue that our civilisation will have to face during the 21st century. This is especially so now that the most recent scientific data have led to the conclusion that the globally averaged net effect of human activities since 1750 has been one of warming (IPCC 2007, p. 5) and that continued greenhouse gas emissions at or above current rates would cause further warming (IPCC, 2007 p. 13). This unequivocal link between climate change and anthropogenic activities requires an urgent, world-wide shift towards a low carbon economy (STERN 2006 p. iv) and coordinated policies and measures to manage this transition.

The climate issue is undoubtedly a typical policy question and as such, is considered amenable to economic scrutiny. Indeed, in today’s world economics is inevitable when it comes to arbitrages in the field of policy making. From the very beginning of international talks on climate change, up until the most recent discussions on a post-Kyoto international framework, economic arguments have turned out to be crucial elements of the analysis that shapes policy responses to the climate threat. This can be illustrated by the prominent role that economics has played in the different analyses produced by the Intergovernmental Panel on Climate Change (IPCC) to assess the impact of climate change on society.

The starting point and the core idea of this PhD research is the long-held observation that the threat of climate change calls for a change of climate in economics. Borrowing from the jargon used in climate policy, adaptation measures could also usefully target the academic discipline of economics. Given that inherent characteristics of the climate problem (e.g. complexity, irreversibility, deep uncertainty, etc.) challenge core economic assumptions, mainstream economic theory does not appear as appropriately equipped to deal with this crucial issue. This makes that new assumptions and analyses are needed in economics in order to comprehend and respond to the problem of climate change.

In parallel (and without environmental considerations being specifically the driving force to it), the mainstream model in economics has also long been (and still is) strongly criticised and disputed by numerous scholars - both from within and outside the field of economics. For the sake of functionality, these criticisms - whether they relate to theoretical inconsistencies or are empirically-based - can be subsumed as all challenging part of the Cartesian/Newtonian legacy of economics. This legacy can be shown to have led to a model imprinted with what could be called “mechanistic reductionism”. The mechanistic side refers to the Homo oeconomicus construct while reductionism refers to the quest for micro-foundations materialised with the representative agent hypothesis. These two hypotheses constitute, together with the conjecture of perfect markets, the building blocks of the framework of general equilibrium economics.

Even though it is functional for the purpose of this work to present them separately, the flaws of economics in dealing with the specificities of the climate issue are not considered independent from the fundamental objections made to the theoretical framework of mainstream economics. The former only make the latter seem more pregnant while the current failure of traditional climate policies informed by mainstream economics render the need for complementary approaches more urgent.

2. Overview of the approach and its main insights for climate policy

Starting from this observation, the main objective of this PhD is thus to assess the implications for climate policy that arise from adopting an alternative analytical economic framework. The stance is that the coupling of insights from the framework of evolutionary economics with the perspective of ecological economics provides a promising way forward both theoretically as well as on a more applied basis with respect to a better comprehension of the socioeconomic aspects related to the climate problem. As claimed in van den Bergh (2007, p. 521), ecological economics and evolutionary economics “share many characteristics and can be combined in a fruitful way" - which renders the coupling approach both legitimate and promising.

The choice of an evolutionary line of thought initially stems from its core characteristic: given its focus on innovation and system change it provides a useful approach to start with for assessing and managing the needed transition towards a low carbon economy. Besides, its shift of focus towards a better understanding of economic dynamics together with its departure from the perfect rationality hypothesis renders evolutionary economics a suitable theoretical complement for designing environmental policies.

The notions of path-dependence and lock-in can be seen as the core elements from this PhD research. They arise from adopting a framework which is founded on a different view of individual rationality and that allows for richer and more complex causalities to be accounted for. In a quest for surmounting the above-mentioned problem of reductionism, our framework builds on the idea of ‘multi-level selection’. This means that our analytical framework should be able to accommodate not only for upward but also for downward causation, without giving analytical priority to any level over the other. One crucial implication of such a framework is that the notion of circularity becomes the core dynamic, highlighting the importance of historicity, feedbacks and emergent properties.

More precisely, the added value of the perspective adopted in this PhD research is that it highlights the role played by inertia and path-dependence. Obviously, it is essential to have a good understanding of the underlying causes of that inertia prior to devising on how to enforce a change. Providing a clear picture of the socio-economic processes at play in shaping socio-technical systems is thus a necessary first step in order to usefully complement policy-making in the field of energy and climate change. In providing an analytical basis for this important diagnosis to be performed, the use of the evolutionary framework sheds a new light on the transition towards low-carbon socio-technical systems. The objective is to suggest strategies that could prove efficient in triggering the needed transition such as it has been the case in past “lock-in” stories.

Most notably, the evolutionary framework allows us to depict the presence of two sources of inertia (i.e at the levels of individuals through “habits” and at the level of socio-technical systems) that mutually reinforce each other in a path-dependent manner. Within the broad perspective on path dependence and lock-in, this PhD research has first sketched the implications for climate policy of applying the concept of ‘technological lock-in’ in a systemic perspective. We then investigated in more details the notion of habits. This is important as the ‘behavioural’ part of the lock-in process, although explicitly acknowledged in the pioneer work of Paul David (David, 1985, p. 336), has been neglected in most of subsequent analyses. Throughout this study, the notion of habits has been studied at both the theoretical and applied level of analysis as well as from an empirical perspective.

As shown in the first chapters of the PhD, the advantage of our approach is that it can incorporate theories that so far have been presented opposite, partial and incomplete perspectives. For instance, it is shown that our evolutionary approach not only is able to provide explanation to some of the puzzling questions in economics (e.g. the problem of strong reciprocity displayed by individual in anonymous one-shot situations) but also is very helpful in bringing a complementary explanation with respect to the famous debate on the ‘no-regret’ emission reduction potential which agitates the experts of climate policy.

An emission reduction potential is said to be "no regret" when the costs of implementing a measure are more than offset by the benefits it generates such as, for instance, reduced energy bills. In explaining why individuals do not spontaneously implement those highly profitable energy-efficient investments ,it appears that most prior analyses have neglected the importance of non-economic obstacle. They are often referred to as “barriers” and partly relate to the ‘bounded rationality’ of economic agent. As developed in the different chapters of this PhD research, the framework of evolutionary economics is very useful in that it is able to provide a two-fold account (i.e. relying on both individual and socio-technical sources of inertia) of this limited rationality that prevent individuals to act as purely optimising agents.

Bearing this context in mind, the concept of habits, as defined and developed in this study, is essential in analysing the determinants of energy consumption. Indeed, this concept sheds an insightful light on the puzzling question of why energy consumption keeps rising even though there is an evident increase of awareness and concern about energy-related environmental issues such as climate change. Indeed, if we subscribe to the idea that energy-consuming behaviours are often guided by habits and that deeply ingrained habits can become “counter-intentional”, it then follows that people may often display “locked-in” practices in their daily energy consumption behaviour. This hypothesis has been assessed in our empirical analysis whose results show how the presence of strong energy-consuming habitual practices can reduce the effectiveness of economic incentives such as energy subsidies. One additional delicate factor that appears crucial for our purpose is that habits are not fully conscious forms of behaviours. This makes that individuals do not really see habits as a problem given that it is viewed as easily changed.

In sum, based on our evolutionary account of the situation, it follows that, to be more efficient, climate policies would have to both shift the incumbent carbon-based socio-technical systems (for it to shape decisions towards a reduction of greenhouse gas emissions) and also deconstruct habits that this same socio-technical has forged with time (as increased environmental awareness and intentions formulated accordingly are not sufficient in the presence of strong habits).

Accordingly, decision-makers should design measures (e.g. commitment strategies, niche management, etc.) that, as explained in this research, specifically target those change-resisting factors and their key features. This is essential as these factors tend to reduce the efficiency of traditional instruments. Micro-level interventions are thus needed as much as macro-level ones. For instance, it is often the case that external improvements of energy efficiency do not lead to lower energy consumption due to the rebound effect arising from unchanged energy-consuming habits. Bearing this in mind and building on the insights from the evolutionary approach, policy-makers should go beyond the mere subsidisation of technologies. They should instead create conditions enabling the use of the multi-layered, cumulative and self-reinforcing character of economic change highlighted by evolutionary analyses. This means supporting both social and physical technologies with the aim of influencing the selection environment so that only the low-carbon technologies and practices will survive.

Mentioned references:

David, P. A. (1985), Clio and the economics of QWERTY, American Economic Review 75/2: 332–337.

IPCC, 2007, ‘Climate Change 2007: The Physical Science Basis’, Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change [Solomon, S. D. Qin, M. Manning, Z. Chen, M. Marquis, K.B. Averyt, M. Tignor and H.L. Miller (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 996 pp.

Stern, N. 2006, ‘Stern Review: The economics of Climate Change’, Report to the UK Prime Minister and Chancellor, London, 575 p. (www.sternreview.org.uk)

van den Bergh, J.C.J.M. 2007, ‘Evolutionary thinking in environmental economics’, Journal of Evolutionary Economics 17(5): 521-549.


Doctorat en Sciences économiques et de gestion
info:eu-repo/semantics/nonPublished

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Flor, Ambrosi Mario Alberto. "Método para descubrir patrones espacio-temporales de comportamiento de usuarios eléctricos utilizando herramientas de aprendizaje automático." Doctoral thesis, Universitat de Girona, 2021. http://hdl.handle.net/10803/673421.

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This research presents a novel approach to define geographic areas with typical electricity consumption profiles from smart meter records, using machine learning and spatial analysis methods. Distribution system operators must guarantee the quality and reliability of the electric service. To achieve this objective, electricity distribution utilities need to know in detail and with an adequate periodicity the consumption profiles of their customers. Modern telemetering devices, such as smart meters, open the door to an immense amount of data and new analysis, due to a higher frequency and precision of consumer electrical consumption. However, conventional methods cannot deal with the voluminous and fast gathered data by smart meters. The objective of this research is to apply machine learning techniques combined with spatial analysis to generate more efficient and accurate load profiles in the areas of study. The study analyzes a voluminous database of measurements gathered during 4 years by 1000 georeferenced smart meters located in the city of Guayaquil in Ecuador. In the study an unsupervised learning methodology to group and classify the time series of energy measurements, using the dynamic time warping technique to discover typical load profiles according to their characteristic weekly consumption, is applied. Next, we perform a restricted space-time analysis to define geographic areas with constant and predictable behavior
questa investigació presenta un enfocament nou per definir zones geogràfiques amb perfils típics de consum elèctric a partir dels registres de mesuradors intel·ligents, utilitzant mètodes d'aprenentatge automàtic i anàlisi espacial. Les empreses d'electricitat han de garantir la qualitat i fiabilitat del servei elèctric. Per aconseguir aquest objectiu, les empreses de distribució d'electricitat requereixen conèixer en detall i amb una periodicitat adequada els perfils de consum dels seus clients. Els moderns dispositius de telemesura, com els mesuradors intel·ligents, obren la porta a una immensa quantitat de dades i nous anàlisis, a causa de la major freqüència i precisió de la informació del consumidor, però els mètodes convencionals no poden abordar els voluminosos i ràpids conjunts de dades que generen aquests dispositius. L'objectiu d'aquesta investigació és utilitzar tècniques d'aprenentatge automàtic combinades amb l'anàlisi espacial per generar perfils de càrrega més eficients i precisos a les zones d'estudi. L'estudi analitza una voluminosa base de dades amb mesuraments de 4 anys recollits per 1000 mesuradors intel·ligents georeferenciats a la ciutat de Guayaquil a l’Equador. A l'estudi s'implementa una metodologia d'aprenentatge no supervisat per agrupar i classificar les sèries temporals de mesures d'energia, utilitzant la tècnica de deformació dinàmica de temps per descobrir perfils de càrrega típics d'acord amb el seu consum característic setmanal. A continuació, es va realitzar una anàlisi espai temporal restringit per definir zones geogràfiques amb comportament constant i predictible
Programa de Doctorat en Tecnologia
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28

Mendes, Hugo Miguel Nogueira. "Energy consumption forecasting: a proposed framework." Master's thesis, 2020. http://hdl.handle.net/10071/22280.

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With the development of underdeveloped countries and the digitization of societies, energy consumption is expected to continue to show high growth in the coming decades. While there is still a strong focus on fossil fuels for energy generation, the implementation of energy policies is crucial to gradually shift to renewable sources and the consequent reduction in CO2 emissions. Buildings are currently the sector that consumes the most energy. To contribute for a better energy consumption efficiency, it was proposed a framework, to be applied to buildings or households, to allow users to know their energy consumption and the possibility to forecast it. Different data analysis techniques for time series were used to provide information to the user about their energy consumption as well as to validate important data characteristics, namely stationarity and the existence of seasonality, which can have an impact in the forecasting models. For the definition of the forecasting models, state of the art was done to identify used models for energy consumption forecasting, and three models were tested for both types of data, univariate and multivariate. For the univariate data, the tested models were SARIMA, Holt-Winters and LSTM as for the multivariate data, SARIMA with exogenous variables, Support Vector Regression and LSTM. After the first execution of each model, hyperparameter tuning was done to conclude on the improvement of the results and the robustness of the models for later application to the framework.
Com o desenvolvimento de países subdesenvolvidos e a digitalização das sociedades, é esperado que o consumo de energia continue a apresentar um crescimento elevado nas próximas décadas. Existindo ainda um grande foco em fontes fósseis para a geração de energia, a implementação de políticas energéticas são cruciais para a mudança gradual para energias renováveis e consequente redução de emissões de CO2. Edifícios são atualmente o sector que mais energia consomem. De forma a contribuir para uma melhor eficiência no consumo de energia foi proposta uma framework, a aplicar em edifícios ou apartamentos, para possibilitar aos utilizadores ter um conhecimento do seu consumo de energia bem como a previsão desse mesmo consumo. Diferentes técnicas de análise de dados para séries temporais foram utilizadas para proporcionar informação ao utilizador sobre o seu consumo de energia bem como a validação de caraterísticas importantes dos dados, nomeadamente a verificação da estacionariedade e a existência da sazonalidade, que terão impacto no modelo de previsão. Para a definição dos modelos preditivos, foi feita uma revisão de literatura sobre modelos utilizados atualmente para previsão do consumo de energia e testados três modelos para os dois tipos de dados, univariados e multivariados. Para os dados univariados os modelos testados foram SARIMA, Holt-Winters e LSTM e para os dados multivariados SARIMA com variáveis exógenas, Support Vector Regression e LSTM. Após a primeira execução de cada modelo, foi feita uma otimização dos modelos para concluir na melhoria dos resultados previstos e na robustez dos modelos para posterior aplicação na framework.
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29

Talmo, Dan. "Energy use and forecasting in Wisconsin manufacturing industries." 1987. http://catalog.hathitrust.org/api/volumes/oclc/16319156.html.

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30

Lin, Po-Heng, and 林柏亨. "Forecasting Energy Consumption using Vector Autoregression and Genetic Planning." Thesis, 2013. http://ndltd.ncl.edu.tw/handle/22425591254582313690.

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碩士
國立交通大學
工業工程與管理系所
101
After three oil crises, the international energy situation is now characterized by volatility, rising energy prices, and heightened societal attention to issues related to energy. Accurate predict is a nation's annual energy consumption would enable help government to develop a policy of energy resources. Most studies constructed energy consumption prediction models for using univariate time series model. However, univariate time series model does not consider other variables such as some economic indice. When energy consumption are affected significently by other variables, the result of univariate time series model will be inaccurate. Because the energy consumption is tied up with the economic growth, this study will focus on predict energy consumption using the economic Indicators. This study utilizes economic indicators, such as population and total exportd and their lag effects as input data, and the energy consumption data as the output variable to build a Vector Autoregression model for predicting energy consumption. In addition, genetic Programming is employed to build a residual prediction model to enhance the forecasting accuracy. Finally, a hybrid energy consumption prediction model is developed by combining the Vetor Autoregression model and residual prediction model. The energy consumption data in Taiwan from 1979~2010 are utilized to demonstrate the effectiveness of the proposed method.
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Chuang, Chao-Bin, and 莊朝斌. "Constructing an Energy Consumption Model using Rolling Gray Forecasting Method." Thesis, 2011. http://ndltd.ncl.edu.tw/handle/12562865610821030510.

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碩士
國立交通大學
工業工程與管理學系
99
Since the Oil crisis in 1973, energy has been an important source of economic development. Therefore, many countries are concerned with energy-related issues and energy consumption becomes an important economic index. However, it is generally difficult to accurately predict energy consumption. Although there are studies relating to energy consumption forecasting based on social-economic indicators such as gross domestic product, population and import–export figures, those studies have certain deficiencies and disadvantages, such as regression models and artificial neural network (ANN)-based models. For example, their predictive accuracy is low when the sample is small. Grey model (GM) only needs at least four data points to construct a reliable and acceptable prediction model, thus GM is appropriate to be utilized to predict future energy consumption. However, GM also has some drawbacks. This study proposed an improve grey model, using a rolling grey model combining a residual modification model to construct an energy consumption forecasting model to enhance the prediction accuracy of GM. A Taiwanese energy consumption dataset is utilized to demonstrate the effectiveness and feasibility of the proposed method.
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Lee, Yi-Shian, and 李宜憲. "Using Grey Theory and Genetic Programming to Construct Forecasting Models-Case Study of Forecasting Energy Consumption." Thesis, 2010. http://ndltd.ncl.edu.tw/handle/40678566385164095054.

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博士
國立交通大學
工業工程與管理學系
99
The issue of energy consumption has become increasingly important due to its impact on the economies of nations. Forecasting energy consumption by conventional statistical methods usually requires assumptions such as normality, independence or large data sets. However, data collected on energy consumption are often very few and do not meet the statistical assumptions. The grey forecasting model, based on grey theory, can be constructed for at least four observations and it does not require any statistical assumptions. Thus, it is suitable to use a grey forecasting model to forecast the energy consumption. In some cases, however, the grey forecasting model may obtain large forecasting errors. To overcome this problem, this dissertation proposes three improved grey forecasting models, which combines grey theory and genetic programming. Finally, two energy consumption data sets from 1990 to 2007 of China and US are used to demonstrate the effectiveness of the proposed forecasting models. The proposed models are also compared with the existing forecasting models of energy consumption. The empirical results indicated that the proposed models can enhance both of the accuracy and the precision of forecasting the energy consumption data.
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Hung, I.-Hsien, and 洪亦賢. "Using Grey Markov and Genetic Programming to Construct Energy Consumption Forecasting Models." Thesis, 2015. http://ndltd.ncl.edu.tw/handle/47300001556696705283.

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碩士
國立交通大學
工業工程與管理系所
103
The rapid growth of technology and population drives the human needs for energy. It influences widely on many national issues, including economic development, environmental protection, etc. However, because of energy resources are not inexhaustible, so the forecasting and management of energy consumption have become an increasingly important issue now and future. The conventional statistical methods are typically utilized to construct the prediction model, but the data collected on energy consumption are often very few, non-linear or do not meet the statistical assumptions, such as normality, homogeneity and independence, resulting poor prediction accuracy of forecasting models. Some studies have proposed GM(1,1) and residual modification GM(1,1) models to predict the energy consumption. The advantages of these GM(1,1) models can be constructed for at least four observations and they do not require any statistical assumptions. However, the energy consumption is influenced by random fluctuations of various factors, such as: economic development, technological breakthroughs, changes in national policy, and GM(1,1) is a smooth function of exponential curve, which has poor prediction accuracy when the data is random. Therefore, the objective of this study is to combined GM(1,1), Markov chains and genetic programming to establish the improved prediction models on energy consumption data. Finally, the energy consumption data set from 1990 to 2005 of US was utilized to demonstrate the effectiveness of the proposed energy consumption forecasting model.
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Ravichandran, S., A. Vijayalakshmi, K. S. Swarup, Haile S. Rajamani, and Prashant Pillai. "Short term energy forecasting techniques for virtual power plants." 2016. http://hdl.handle.net/10454/11101.

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Yes
The advent of smart meter technology has enabled periodic monitoring of consumer energy consumption. Hence, short term energy forecasting is gaining more importance than conventional load forecasting. An Accurate forecasting of energy consumption is indispensable for the proper functioning of a virtual power plant (VPP). This paper focuses on short term energy forecasting in a VPP. The factors that influence energy forecasting in a VPP are identified and an artificial neural network based energy forecasting model is built. The model is tested on Sydney/ New South Wales (NSW) electricity grid. It considers the historical weather data and holidays in Sydney/ NSW and forecasts the energy consumption pattern with sufficient accuracy.
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曾子豪. "Analyzing and Forecasting CO2 Emission, Energy Consumption, and Real GDP in the Top New Renewable Energy Countries." Thesis, 2018. http://ndltd.ncl.edu.tw/handle/79gz9p.

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碩士
國立交通大學
管理科學系所
106
This study explores the causality among energy (Fossil Fuel and new renewable energy), environment (CO2 emission) and economic (real GDP), which is based on three countries without record of use of nuclear energy and with highest share of renewable energy in the world in 1980 to 2016.As the economic rapidly develop, the use and demand of energy annually grows up. But it also causes the damage and pollution to the environment. For the sustainable development of the environment, each government of countries starts to set new energy policy. In this study, it found that the renewable energy couldn’t completely substitute the fossil fuel in the short term because of the economic growth, but each countries make the change of the use of energy in the long term. If it decrease the use of fossil fuel, it can not only reduce the pollution of environment but also reduce the dependence on imported fossil fuel. Moreover, we also make the forecast of our data by using the XGBoost, one of famous machine learning algorithm. The performance of model prediction is well. If we can successfully build the model, the result can serve as a reference for future policy development and suggestion.
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Huang, Chian-Shan, and 黃千珊. "A comparison of seasonal time series models for forecasting the energy consumption in Taiwan." Thesis, 2008. http://ndltd.ncl.edu.tw/handle/26773247449379626795.

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碩士
淡江大學
數學學系碩士班
96
Recently, the energy price keeps increasing.Both the demand and the consumption are on the rise.Due to these scenarios,this essay will try to predict the energy consumption in Taiwan,hoping to get a better grasp of the future trend.We will use the following four models for prediction,and they are Seasonal Autoregressive Integrated Moving Average Models(SARIMA),Regression Models with Time Series Errors (RMTSE),Back-propagation Network(BPN),and hybrid SARIMA and BPN(SARIMABP).The findings discovered that,at that time series of graph the sequence shook obviously uses BPN to be able to obtain a better forecast,otherwise,the graph shook steadily, used SARIMA to be able to obtain a better forecast,and adopt the mixing model to be able to improve the forecast error.
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Greyling, Jean. "Mine energy budget forecasting : the value of statistical models in predicting consumption profiles for management systems / Jean Greyling." Thesis, 2014. http://hdl.handle.net/10394/12240.

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The mining industry in South Africa has long been a crucial contributor to the Gross Domestic Product (GDP) starting in the 18th century. In 2010, the direct contribution towards the GDP from the mining industry was 10% and 19.8% indirect. During the last decade global financial uncertainty resulted in commodity prices hitting record numbers when Gold soared to a high at $1900/ounce in September 2011, and thereafter the dismal decline to a low of $1200/ounce in July 2013. Executives in these markets have reacted strongly to reduce operational costs and focussing on better production efficiencies. One such a cost for mining within South Africa is the Operational Expenditure (OPEX) associated with electrical energy that has steadily grown on the back of higher than inflation rate escalations. Companies from the Energy Intensive User Group (EIUG) witnessed energy unit prices (c/kWh) and their percentage of OPEX grow to 20% from 7% in 2008. The requirement therefore is for more accurate energy budget forecasting models to predict what energy unit price escalations (c/kWh) occur along with the required units (kWh) at mines or new projects and their impact on OPEX. Research on statistical models for energy forecasting within the mining industry indicated that the historical low unit price and its notable insignificant impact on OPEX never required accurate forecasting to be done and thus a lack of available information occurred. AngloGold Ashanti (AGA) however approached Deloittes in 2011 to conclude a study for such a statistical model to forecast energy loads on one of its operations. The model selected for the project was the Monte Carlo analysis and the rationale made sense as research indicated that it had common uses in energy forecasting at process utility level within other industries. For the purpose of evaluation a second regression model was selected as it is well-known within the statistical fraternity and should be able to provide high level comparison to the Monte Carlo model. Finally these were compared to an internal model used within AGA. Investigations into the variables that influence the energy requirement of a typical deep level mine indicated that via a process of statistical elimination tonnes broken and year are the best variables applicable in a mine energy model for conventional mining methods. Mines plan on a tonnage profile over the Life of Mine (LOM) so the variables were known for the given evaluation and were therefore used in both the Monte Carlo Analysis that worked on tonnes and Regression Analysis that worked on years. The models were executed to 2040 and then compared to the mine energy departments’ model in future evaluations along with current actuals as measured on a monthly basis. The best comparison against current actuals came from the mine energy departments’ model with the lowest error percentage at 6% with the Regression model at 11% and the Monte Carlo at 20% for the past 21 months. This, when calculated along with the unit price path studies from the EIUG for different unit cost scenarios gave the Net Present Value (NPV) reduction that each model has due to energy. A financial analysis with the Capital Asset Pricing Model (CAPM) and the Security Market Line (SML) indicated that the required rate of return that investors of AGA shares have is 11.92%. Using this value the NPV analysis showed that the mine energy model has the best or lowest NPV impact and that the regression model was totally out of line with expectations. Investors that provide funding for large capital projects require a higher return as the associated risk with their money increases. The models discussed in this research all work on an extrapolation principle and if investors are satisfied with 6% error for the historical 2 years and not to mention the outlook deviations, then there is significance and a contribution from the work done. This statement is made as no clear evidence of any similar or applicable statistical model could be found in research that pertains to deep level mining. Mining has been taking place since the 18th century, shallow ore resources are depleted and most mining companies would therefore look towards deeper deposits. The research indicates that to some extent there exist the opportunity and some rationale in predicting energy requirements for deep level mining applications. Especially when considering the legislative and operational cost implications for the mining houses within the South African economy and with the requirements from government to ensure sustainable work and job creation from industry in alignment with the National Growth Path (NGP). For this, these models should provide an energy outlook guideline but not exact values, and must be considered along with the impact on financial figures.
MBA, North-West University, Potchefstroom Campus, 2014
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38

Deline, Mary Elizabeth. "Keeping Up With the Joneses: Electricity Consumption, Publicity and Social Network Influence in Milton, Ontario." Thesis, 2010. http://hdl.handle.net/10012/5219.

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Abstract This study used an exploratory research focus to investigate if making electricity consumption public and subject to social norms and networks resulted in consumption decreases for households in Milton, Ontario. In the first phase, Milton Hydro identified customers who fell within an average annual electricity consumption category and these customers were invited to participate by mail. Due to lack of participant uptake, cold-calling, targeting of service and faith groups and commuters, and snowball sampling were employed to obtain a total participant size of 17. The second phase saw participants grouped according to social network type (occupational, faith group, etc) and exposed to approval or disapproval indicators within their group about their daily electricity consumption rates via an on-line ‘energy pool’. There were five main groups: one of neighbours, one of members of a faith group, one of members of a company, one of strangers and one of a control group. Group members saw other members’ indicators with the exception of the control group, whose indicators were privately delivered. All group’s electricity consumption was tracked through daily smart meter readings. Participants also had the option of commenting on each other’s electricity use via an online ‘comment box’. In the third phase participants were asked to participate in a questionnaire to assess: 1) the perceived efficacy of the intervention; 2) perceptions of electricity consumption; and 3) the influence of the group on these perceptions. This sequential methodology was chosen for its ability to “...explain significant (or non-significant) results, outlier results, or surprising results” (Cresswell, 2006, p. 72). The findings of this exploratory research seem to suggest the following: 1) that publicity or group type does not seem to affect electricity consumption in comparative electricity consumption feedback for this study; 2) that participants used injunctive norms to comment on their electricity consumption but directed these comments solely at themselves; and 3) that the stronger the relationships in the group, the more likely participants were to engage with the website through checking it and commenting on it. This study may be useful to those in the fields of: 1) electricity conservation who wish to leverage feedback technologies; 2) social networks who wish to better understand how tie strength interacts with social norms and; 3) those in social marketing who wish to develop norm-based campaigns.
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39

Sánchez-Lugo, Ahira M. "An index to measure the influences of climate on residential natural gas demand." Thesis, 2007. http://hdl.handle.net/10125/20709.

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40

Van, Rooyen Gerhardus Christiaan. "Spesifieke verbruik van steenkool in die Suid-Afrikaanse energie-ekonomie met spesiale verwysing na die invloed van hoër steenkoolpryse." Thesis, 2014. http://hdl.handle.net/10210/12080.

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M.Phil.
Coal is today providing more than seventy-five percent of South Africa's energy requirements and will, to a large extent, remain so in the future. It is thus important to evaluate the adequacy of the country's available coal resources against expected future demand. The main objective of this study, which was done under the supervision of Prof. D. J. Kotze, was therefore to analyse the specific consumption of coal in the various consumption sectors in order to establish historical trends. The specific comsumption of coal is defined as the amount of coal used to produce a unit of final product. The factors attributing to these observed trends were determined and their future role evaluated in order to establish whether it was possible to extrapolate historical trends into the future. By means of curve fitting to the observed data and extrapolation it was possible to obtain future values of specific coal consumption for each of the sectors. These values, together with the production output forecasts for the various sectors were then used to calculate the total coal requirements for three reference years, namely, 1990, 1995 and 2000. The role of coal prices in explaining trends in specific coal consumption of various sectors was also analised separately. Information to conduct the study was obtained mainly from the various coal producers' associations as well as from individual producers and other organizations such as Escom, Sasol and Iscor, the Department of Hineral and Energy Affairs and the Hinerals Bureau. In some instances private firms and producers' associations were also consulted as well as a wide variety of literature on the subject. The principal finding of the study was that coal was substituted or was still being substituted by electricity in most final applications because of the convenience of use. Coal, however, still plays and probably will continue to play an important role in future in basic industries such as the metallurgical industry. Coal prices have not up to now played a very important role in the overall specific consumption of coal which can probably be attributed to the relatively low prices of coal on the inland market. It was also found that it was not desirable to do away with the present system of price control entirely as a certain measure of control was still necessary to safeguard the usuage of coal in certain applications for which there were no other substitutes. It was further concluded that South Africa does not have the vast quantities of coal commonly thought and that measures have to be taken in order to ensure that the country's coal resources are conserved and that optimum use is made of available resources.
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41

Moroke, N. D. "An application of Box-Jenkins transfer function analysis to consumption-income relationship in South Africa / N.D. Moroke." Thesis, 2005. http://hdl.handle.net/10394/11344.

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Using a simple linear regression model for estimation could give misleading results about the relationship between Yt, and Xt, . Possible problems involve (1) feedback from the output series to the inputs, (2) omitted time-lagged input terms, (3) an auto correlated disturbance series and, (4) common autocorrelation patterns shared by Y and X that can produce spurious correlations. The primary aim of this study was therefore to use the Box-Jenkins Transfer Function analysis to fit a model that related petroleum consumption to disposable income> The final Transfer Function Model z1t=)C(1-w1 B)/((1-δ1 B) B^5 Z(t^((x) +(1-θ1 B)at significantly described the data. Forecasts generated from this model show that petroleum consumption will hit a record of up to 4.8636 in 2014 if disposable income is augmented. There is 95% confidence that the forecasted value of petroleum consumption will lie between 4.5276 and 5.1997 in 2014.
Thesis (M. Com. (Statistics) North-West University, Mafikeng Campus, 2005
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42

Arienti, João Henrique Leal. "Time series forecasting applied to an energy management system ‐ A comparison between Deep Learning Models and other Machine Learning Models." Master's thesis, 2020. http://hdl.handle.net/10362/108172.

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Abstract:
Project Work presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced Analytics
A large amount of energy used by the world comes from buildings’ energy consumption. HVAC (Heat, Ventilation, and Air Conditioning) systems are the biggest offenders when it comes to buildings’ energy consumption. It is important to provide environmental comfort in buildings but indoor wellbeing is directly related to an increase in energy consumption. This dilemma creates a huge opportunity for a solution that balances occupant comfort and energy consumption. Within this context, the Ambiosensing project was launched to develop a complete energy management system that differentiates itself from other existing commercial solutions by being an inexpensive and intelligent system. The Ambiosensing project focused on the topic of Time Series Forecasting to achieve the goal of creating predictive models to help the energy management system to anticipate indoor environmental scenarios. A good approach for Time Series Forecasting problems is to apply Machine Learning, more specifically Deep Learning. This work project intends to investigate and develop Deep Learning and other Machine Learning models that can deal with multivariate Time Series Forecasting, to assess how well can a Deep Learning approach perform on a Time Series Forecasting problem, especially, LSTM (Long Short-Term Memory) Recurrent Neural Networks (RNN) and to establish a comparison between Deep Learning and other Machine Learning models like Linear Regression, Decision Trees, Random Forest, Gradient Boosting Machines and others within this context.
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43

Alani, Adeshina Yahaha. "Short-term multiple forecasting of electric energy loads with weather profiles for sustainable demand planning in smart grids for smart homes." Diss., 2018. http://hdl.handle.net/10500/25216.

Full text
Abstract:
Energy consumption in the form of fuel or electricity is ubiquitous globally. Among energy types, electricity is crucial to human life in terms of cooking, warming and cooling of shelters, powering of electronic devices as well as commercial and industrial operations. Therefore, effective prediction of future electricity consumption cannot be underestimated. Notably, repeated imbalance is noticed between the demand and supply of electricity, and this is affected by different weather profiles such as temperature, wind speed, dew point, humidity and pressure of the electricity consumption locations. Effective planning is therefore needed to aid electricity distribution among consumers. Such effective planning is activated by the need to predict future electricity consumption within a short period and the effect of weather variables on the predictions. Although state-of-the-art techniques have been used for such predictions, they still require improvement for the purpose of reducing significant predictive errors when used for short-term load forecasting. This research develops and deploys a near-zero cooperative probabilistic scenario analysis and decision tree (PSA-DT) model to address the lacuna of significant predictive error faced by the state-of-the-art models and to analyse the effect of each weather profile on the cooperative model. The PSA-DT is a machine learning model based on a probabilistic technique in view of the uncertain nature of electricity consumption, complemented by a DT to reinforce the collaboration of the two techniques. Based on detailed experimental analytics on residential, commercial and industrial data loads, the PSA-DT model with weather profiles outperforms the state-of-the-art models in terms of accuracy to a minimal error rate. This implies that its deployment for electricity demand planning will be of great benefit to various smart-grid operators and homes.
School of Computing
M. Sc. (Computer Science)
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