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Models simulating household energy demand based on different occupant and household types and their behavioral patterns have received increasing attention over the last years due the need to better understand fundamental characteristics that shape the demand side. Most of the models described in the literature are based on Time Use Survey data and Markov chains. Due to the nature of the underlying data and the Markov property, it is not sufficiently possible to consider long-term dependencies over several days in occupant behavior. An accurate mapping of long-term dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supply-side fluctuations of renewable based energy systems. The aim of this study is to bridge the gap between social practice theory, energy related activity modelling and novel machine learning approaches. The weaknesses of existing approaches are addressed by combining time use survey data with mobility data, which provide information about individual mobility behavior over periods of one week. In social practice theory, emphasis is placed on the sequencing and repetition of practices over time. This suggests that practices have a memory. Transformer models based on the attention mechanism and Long short-term memory (LSTM) based neural networks define the state of the art in the field of natural language processing (NLP) and are for the first time introduced in this paper for the generation of weekly activity profiles. In a first step an autoregressive model is presented, which generates synthetic weekly mobility schedules of individual occupants and thereby captures long-term dependencies in mobility behavior. In a second step, an imputation model enriches the weekly mobility schedules with detailed information about energy relevant at home activities. The weekly activity profiles build the basis for multiple use cases one of which is modellin

Energy demand, usually indicated by the amount of energy currently consumed in different sectors, is a key factor in national and international energy and climate change policy. In the energy world, demand has different meanings and associations: it often figures as the logical partner to ‘supply’; it is tied to interpretations of ‘need’; and it is the subject of various forms of intervention, including methods of ‘demand side’ management. In this chapter we distinguish between definitions that (a) interpret demand as the energy required to meet current and future needs and (b) those that take energy demand to be inseparable from the constitution of energy services and social practices. The latter position highlights the ongoing dynamics of demand and the part that policy makers and others play in establishing and changing what people do and the energy requirements that follow.

It has long been recognised that energy is not consumed for its own sake but for the services that it provides. Despite this, the majority of energy policy and research remains firmly focused on energy itself: approaching it as a resource that is measured in units like kWh or cubic meters and that is knowingly and rationally acquired, used and managed. This chapter challenges interpretations of energy as a resource by developing and extending the concept of energy services. This approach generates new ideas and insights for those involved in reducing and managing energy demand.

Over the past decade, there has been increasing interest in using ‘smart’ technologies: to can gather information about a building and its inhabitants, and automatically make decisions about which appliances and services should be powered. Promoting smart homes is frequently seen as a means of reducing energy demand. However, this chapter shows that smart home technologies are also positioned and sold as means of enhancing convenience or creating a cosy atmosphere. In increasing expectations of service, and in relying on energy-intensive infrastructures like network technology and data centres, smart home technologies might not reduce demand at all.

The goal of increasing the energy efficiency of products, buildings and processes has been at the heart of national and international policies from the 1970s onwards. Running against conventional wisdom, this chapter highlights the limitations and the potentially counterproductive side effects of an unthinking pursuit of energy efficiency, whether alone or as part of a broader ‘whole systems’ approach. Two main critiques of energy efficiency are considered. The first focuses on the problem of ‘rebound’, the second on interpretations of normality and service that are unavoidably embedded in measures of efficiency and in related policies. If energy efficiency is to be part of the solution rather than part of the problem, questions about sufficiency need to come first: What is energy used for? How much energy do societies ‘need’? How much consumption is compatible with meeting carbon targets?.

Energy research and energy-related policy making are informed by terms, ideas and stories that reproduce certain ways of thinking about problems and responses. As in other fields, phrases enter common usage, concepts become taken for granted, and shared vocabularies form. Disciplines and approaches build on these foundations, often forgetting that what seems like obvious, or common wisdom has a history: it is not set in stone nor is it uncontested or uncontroversial. The essays included in this collection explore different aspects of what we refer to as ‘fables’ in the energy world.

In the energy field, picking the ‘low hanging fruit’ means taking measures that are simple to implement and that generate quick returns in terms of carbon and energy demand reduction. In taking this term seriously and in taking it apart this chapter shows how the language of low hanging fruit situates and frames both the problem and the politics of energy demand reduction. As described, the phrase reproduces and reinforces a distinctly technocratic approach that bypasses fundamental questions about what energy is used for in society, and how this changes over time. Other metaphors – including references to fungi – could be developed in support of more subtle and more integrated accounts of how energy policies, technologies and patterns of demand affect each other.

The goal of increasing the energy efficiency of products, buildings and processes has been at the heart of national and international policies from the 1970s onwards. Running against conventional wisdom, this chapter highlights the limitations and the potentially counterproductive side effects of an unthinking pursuit of energy efficiency, whether alone or as part of a broader ‘whole systems’ approach. Two main critiques of energy efficiency are considered. The first focuses on the problem of ‘rebound’, the second on interpretations of normality and service that are unavoidably embedded in measures of efficiency and in related policies. If energy efficiency is to be part of the solution rather than part of the problem, questions about sufficiency need to come first: What is energy used for? How much energy do societies ‘need’? How much consumption is compatible with meeting carbon targets?.

In the energy field, picking the ‘low hanging fruit’ means taking measures that are simple to implement and that generate quick returns in terms of carbon and energy demand reduction. In taking this term seriously and in taking it apart this chapter shows how the language of low hanging fruit situates and frames both the problem and the politics of energy demand reduction. As described, the phrase reproduces and reinforces a distinctly technocratic approach that bypasses fundamental questions about what energy is used for in society, and how this changes over time. Other metaphors – including references to fungi – could be developed in support of more subtle and more integrated accounts of how energy policies, technologies and patterns of demand affect each other.

Over the past decade, there has been increasing interest in using ‘smart’ technologies: to can gather information about a building and its inhabitants, and automatically make decisions about which appliances and services should be powered. Promoting smart homes is frequently seen as a means of reducing energy demand. However, this chapter shows that smart home technologies are also positioned and sold as means of enhancing convenience or creating a cosy atmosphere. In increasing expectations of service, and in relying on energy-intensive infrastructures like network technology and data centres, smart home technologies might not reduce demand at all.

It has long been recognised that energy is not consumed for its own sake but for the services that it provides. Despite this, the majority of energy policy and research remains firmly focused on energy itself: approaching it as a resource that is measured in units like kWh or cubic meters and that is knowingly and rationally acquired, used and managed. This chapter challenges interpretations of energy as a resource by developing and extending the concept of energy services. This approach generates new ideas and insights for those involved in reducing and managing energy demand.

Dynamic electricity pricing can produce efficiency gains in the electricity sector and help achieve energy policy goals such as increasing electric system reliability and supporting renewable energy deployment. Retail electric companies can offer dynamic pricing to residential electricity customers via smart meter-enabled tariffs that proxy the cost to procure electricity on the wholesale market. Current investments in the smart metering necessary to implement dynamic tariffs show policy makers’ resolve for enabling responsive demand and realizing its benefits. However, despite these benefits and the potential bill savings these tariffs can offer, adoption among residential customers remains at low levels. Using a choice experiment approach, this paper seeks to determine whether disclosing the environmental and system benefits of dynamic tariffs to residential customers can increase adoption. Although sampling and design issues preclude wide generalization, we found that our environmentally conscious respondents reduced their required discount to switch to dynamic tariffs around 10% in response to higher awareness of environmental and system benefits. The perception that shifting usage is easy to do also had a significant impact, indicating the potential importance of enabling technology. Perhaps the targeted communication strategy employed by this study is one way to increase adoption and achieve policy goals.