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)

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.

With the growing installation of home photovoltaics, traditional energy trading is evolving from a unidirectional utility-to-consumer model into a more distributed peer-to-peer paradigm. Besides, with the development of building energy management platforms and demand response-enabled smart devices, energy consumption saved, known as negawatt-hours, has also emerged as another commodity that can be exchanged. Users may tune their heating, ventilation, and air conditioning (HVAC) system setpoints to adjust building hourly energy consumption to generate negawatt-hours. Both photovoltaic (PV) energy and negawatt-hours are two major resources of peer-to-peer electricity trading. Blockchain has been touted as an enabler for trustworthy and reliable peer-to-peer trading to facilitate the deployment of such distributed electricity trading through encrypted processes and records. Unfortunately, blockchain cannot fully detect anomalous participant behaviors or malicious inputs to the network. Consequentially, end-user anomaly detection is imperative in enhancing trust in peer-to-peer electricity trading. This dissertation introduces machine learning-based anomaly detection techniques in peer-to-peer PV energy and negawatt-hour trading. This can help predict the next hour's PV energy and negawatt-hours available and flag potential anomalies when submitted bids. As the traditional energy trading market is agnostic to tangible real-world resources, developing, evaluating, and integrating machine learning forecasting-based anomaly detection methods can give users knowledge of reasonable bid offer quantity. Suppose a user intentionally or unintentionally submits extremely high/low bids that do not match their solar panel capability or are not backed by substantial negawatt-hours and PV energy resources. Some anomalies occur because the participant's sensor is suffering from integrity errors. At the same time, some other abnormal offers are maliciously submitted intentionally to benefit attackers themselves from market disruption. In both cases, anomalies should be detected by the algorithm and rejected by the market. Artificial Neural Networks (ANN), Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Units (GRU), and Convolutional Neural Network (CNN) are compared and studied in PV energy and negawatt-hour forecasting. The semi-supervised anomaly detection framework is explained, and its performance is demonstrated. The threshold values of anomaly detection are determined based on the model trained on historical data. Besides ambient weather information, HVAC setpoint and building occupancy are input parameters to predict building hourly energy consumption in negawatt-hour trading. The building model is trained and managed by negawatt-hour aggregators. CO2 monitoring devices are integrated into the cloud-based smart building platform BEMOSS™ to demonstrate occupancy levels, further improving building load forecasting accuracy in negawatt-hour trading. The relationship between building occupancy and CO2 measurement is analyzed. Finally, experiments based on the Hyperledger platform demonstrate blockchain-based peer-to-peer energy trading and how the platform detects anomalies. / Doctor of Philosophy / The modern power grid is transforming from unidirectional to transactive power systems. Distributed peer-to-peer (P2P) energy trading is becoming more and more popular. Rooftop PV energy and negawatt-hours as two main sources of electricity assets are playing important roles in peer-to-peer energy trading. It enables the building owner to join the electricity market as both energy consumer and producer, also named prosumer. While P2P energy trading participants are usually un-informed and do not know how much energy they can generate during the next hour. Thus, a system is needed to guide the participant to submit a reasonable amount of PV energy or negawatt-hours to be supplied. This dissertation develops a machine learning-based anomaly detection model for an energy trading platform to detect the reasonable PV energy and negawatt-hours available for the next hour's electricity trading market. The anomaly detection performance of this framework is analyzed. The building load forecasting model used in negawatt-hour trading also considers the effect of building occupancy level and HVAC setpoint adjustment. Moreover, the implication of CO2 measurement devices to monitor building occupancy levels is demonstrated. Finally, a simple Hyperledger-based electricity trading platform that enables participants to sell photovoltaic solar energy/ negawatt-hours to other participants is simulated to demonstrate the potential benefits of blockchain.

Thesis (D. Tech. (Engineering: Electric)) -- Central University of technology, 2012 / Because power generation relies heavily on electricity demand, consumers are required to wisely manage their loads to consolidate the power utility‟s optimal power generation efforts. Consequently, accurate and reliable electric load forecasting systems are required. Prior to the present situation, there were various forecasting models developed primarily for electric load forecasting. Modelling short term load forecasting using artificial neural networks has recently been proposed by researchers. This project developed a model for short term load forecasting using a neural network. The concept was tested by evaluating the forecasting potential of the basic feedforward and the cascade forward neural network models. The test results showed that the cascade forward model is more efficient for this forecasting investigation. The final model is intended to be a basis for a real forecasting application. The neural model was tested using actual load data of the Bloemfontein reticulation network to predict its load for half an hour in advance. The cascade forward network demonstrates a mean absolute percentage error of less than 5% when tested using four years of utility data. In addition to reporting the summary statistics of the mean absolute percentage error, an alternate method using correlation coefficients for presenting load forecasting performance results are shown. This research proposes that a 6:1:1 cascade forward neural network can be trained with data from a month of a year and forecast the load for the same month of the following year. This research presents a new time series modeling for short term load forecasting, which can model the forecast of the half-hourly loads of weekdays, as well as of weekends and public holidays. Obtained results from extensive testing on the Bloemfontein power system network confirm the validity of the developed forecasting approach. This model can be implemented for on-line testing application to adopt a final view of its usefulness.

Wong, Jefferson See. / Thesis (M.Phil.)--Chinese University of Hong Kong, 2010. / Includes bibliographical references (leaves 143-151). / Abstracts in English and Chinese. / LIST OF FIGURES --- p.i / LIST OF TABLES --- p.iv / Chapter CHAPTER ONE --- INTRODUCTION --- p.1 / Chapter 1.1 --- Introduction --- p.1 / Chapter 1.2 --- Research Background --- p.1 / Chapter 1.3 --- Study Area --- p.8 / Chapter 1.3.1 --- Geographical setting --- p.8 / Chapter 1.3.2 --- Climate --- p.9 / Chapter 1.3.3 --- Water demand and supply in Hong Kong --- p.10 / Chapter 1.4 --- Objectives of the Study --- p.16 / Chapter 1.5 --- Significance of the Study --- p.16 / Chapter 1.6 --- Outline of Thesis --- p.17 / Chapter CHAPTER TWO --- LITERATURE REVIEW --- p.18 / Chapter 2.1 --- Introduction --- p.18 / Chapter 2.2 --- Concept of Urban Water Consumption / Water Use --- p.19 / Chapter 2.3 --- Urban Water Consumption Patterns --- p.19 / Chapter 2.4 --- Factors Influencing Urban Water Consumption --- p.22 / Chapter 2.5 --- Model Formulation of Urban Water Consumption --- p.29 / Chapter 2.6 --- Methods of Forecasting Urban Water Consumption --- p.37 / Chapter 2.7 --- Conclusion --- p.42 / Chapter CHAPTER THREE --- DATA AND METHODOLOGY --- p.44 / Chapter 3.1 --- Introduction --- p.44 / Chapter 3.2 --- Water Consumption and Climatic Data --- p.44 / Chapter 3.3 --- Modeling Framework and Procedure --- p.49 / Chapter 3.4 --- Base Water Use --- p.52 / Chapter 3.4.1 --- Long-term trend --- p.52 / Chapter 3.5 --- Seasonal Water Use --- p.53 / Chapter 3.5.1 --- Seasonal cycle --- p.54 / Chapter 3.5.2 --- Climatic effect --- p.58 / Chapter 3.6 --- Calendrical Water Use --- p.61 / Chapter 3.6.1 --- Day-of-the-week effect --- p.62 / Chapter 3.6.2 --- Holiday effect --- p.63 / Chapter 3.6.3 --- Persistence component --- p.64 / Chapter 3.7 --- Summary --- p.65 / Chapter CHAPTER FOUR --- RESULTS AND DISCUSSION --- p.67 / Chapter 4.1 --- Introduction --- p.67 / Chapter 4.2 --- Model Fitting and Parameterization --- p.68 / Chapter 4.3 --- Long-term Trend in Base Water Use --- p.69 / Chapter 4.4 --- Seasonal Water Use --- p.76 / Chapter 4.4.1 --- Seasonal cycle --- p.76 / Chapter 4.4.2 --- Climatic effect --- p.81 / Chapter 4.5 --- Calendrical Water Use --- p.86 / Chapter 4.5.1 --- Day-of-the-week effect --- p.86 / Chapter 4.5.2 --- Holiday effect --- p.90 / Chapter 4.5.3 --- Persistence component --- p.98 / Chapter 4.6 --- Evaluation of Model Performance --- p.112 / Chapter 4.7 --- Relative Contribution of Various Components of Water Consumption --- p.128 / Chapter CHAPTER FIVE --- CONCLUSION --- p.136 / Chapter 5.1 --- Introduction --- p.136 / Chapter 5.2 --- Summary of Findings --- p.137 / Chapter 5.3 --- Limitations of the Study --- p.141 / Chapter 5.4 --- Recommendations for Future Studies --- p.142 / REFERENCE LIST --- p.143

CAPES / Um dos aspectos fundamentais da inteligência natural é sua aptidão no processamento de informações temporais. O grande desafio proposto é o de desenvolver sistemas inteligentes que mapeiem essa aptidão do comportamento humano. Neste contexto, aportam as Máquinas de Estado Líquido (LSMs), uma arquitetura neural pulsada (meio líquido) que projeta os dados de entrada em um espaço dinâmico de alta dimensão e, por conseguinte, realiza a análise do conjunto de dados de entrada através de uma rede neural clássica (unidade de leitura). Desta maneira, esta tese apresenta uma solução inovadora para a previsão de séries temporais contínuas através das LSMs com mecanismo de reinicialização e entradas analógicas, contemplando a área da demanda de energia elétrica. A metodologia desenvolvida foi aplicada no horizonte de previsão a curto prazo e a longo prazo. Os resultados obtidos são promissores, considerando o alto erro estabelecido para parada do treinamento da unidade de leitura, o baixo número de iterações do treinamento da unidade de leitura e que nenhuma estratégia de ajustamento sazonal, ou pré-processamento, sob os dados de entrada foi realizado. Até o momento, percebe-se que as LSMs têm despontado como uma nova e promissora abordagem dentro do paradigma conexionista, emergente da ciência cognitiva. / Among of several aspects of the natural intelligence is its ability to process temporal information. One of major challenges to be addresses is how to efficiently develop intelligent systems that integrate the complexities of human behavior. In this context, appear the Liquid State Machines (LSMs), a pulsed neural architecture (liquid) that projects the input data in a high-dimensional dynamical space and therefore makes the analysis of input data all through a classical neural network (readout). Thus, this thesis presents an innovative solution for forecasting continuous time series through LSMs with reset mechanism and analog inputs, applied to the electric energy demand. The methodology was applied in the short-term and long-term forecasting of electrical energy demand. Results are promising, considering the high error to stop training the readout, the low number of iterations of training of the readout, and that no strategy of seasonal adjustment or preprocessing of input data was achieved. So far, it can be notice that the LSMs have been studied as a new and promising approach in the Artificial Neural Networks paradigm, emergent from cognitive science.

The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments. / Computing / M. Sc. (Operations Research)

本研究主要應用基於總體經驗模態分解法(EEMD)之倒傳遞類神經網路(BPNN)預測兩種不同的非線性時間序列數據，包括政大逐時用電量以及逐日歷史黃金價格。透過EEMD，這兩種資料會分別被拆解為數條具有不同物理意義的本徵模態函數(IMF)，而這讓我們可以將這些IMF視為各種影響資料的重要因子，並且可將拆解過後的IMF放入倒傳遞類神經網路中做訓練。 另外在本文中，我們也採用移動視窗法作為預測過程中的策略，另外也應用內插法和外插法於逐時用電量的預測。內插法主要是用於補點以及讓我們的數據變平滑，外插法則可以在某個範圍內準確預測後續的趨勢，此兩種方法皆對提升預測準確度占有重要的影響。 利用本文的方法，可在預測的結果上得到不錯的準確性，但為了進一步提升精確度，我們利用多次預測的結果加總平均，然後和只做一次預測的結果比較，結果發現多次加總平均後的精確度的確大幅提升，這是因為倒傳遞類神經網路訓練過程中其目標為尋找最小誤差函數的關係所致。 / In this paper, we applied the Ensemble Empirical Mode Decomposition (EEMD) based Back-propagation Neural Network (BPNN) learning paradigm to two different topics for forecasting: the hourly electricity consumption in NCCU and the historical daily gold price. The two data series are both non-linear and non-stationary. By applying EEMD, they were decomposed into a finite, small number of meaningful Intrinsic Mode Functions (IMFs). Depending on the physical meaning of IMFs, they can be regarded as important variables which are input into BPNN for training. We also use moving-window method in the prediction process. In addition, cubic spline interpolation as well as extrapolation as our strategy is applied to electricity consumption forecasting, these two methods are used for smoothing the data and finding local trend to improve accuracy of results. The prediction results using our methods and strategy resulted in good accuracy. However, for further accuracy, we used the ensemble average method, and compared the results with the data produced without applying the ensemble average method. By using the ensemble average, the outcome was more precise with a smaller error, it results from the procedure of finding minimum error function in the BPNN training.

The forecasting of Eskom transmission electrical network demands is a complex task. The lack of historical data on some of the network components complicates this task even further. In this dissertation a model is suggested which will address all the requirements of the transmission system expansion engineers in terms of future loads and market trends. Suggestions are made with respect to ways of overcoming the lack of historical data, especially on the point loads, which is a key factor in modelling the electrical networks. A brief overview of the transmission electrical network layout is included to provide a better understanding of what is required from such a forecast. Lastly, some theory on multiple regression, neural networks and qualitative forecasting techniques is included, which will be of value for further model developments. / Computing / M. Sc. (Operations Research)

1. Contextual outline of the PhD Research<p><p>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.<p><p>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.<p><p>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.<p><p>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. <p><p>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. <p><p>2. Overview of the approach and its main insights for climate policy<p><p>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. <p><p>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.<p><p>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. <p><p>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. <p><p>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. <p><p>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. <p><p>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.<p><p>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.<p><p>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).<p><p>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. <p><p><p>Mentioned references:<p><p>David, P. A. (1985), Clio and the economics of QWERTY, American Economic Review 75/2: 332–337.<p><p>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. <p><p>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)<p><p>van den Bergh, J.C.J.M. 2007, ‘Evolutionary thinking in environmental economics’, Journal of Evolutionary Economics 17(5): 521-549.<p> / Doctorat en Sciences économiques et de gestion / info:eu-repo/semantics/nonPublished