• Energy Research
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Decarbonisation plans largely rely on the electrification of energy-intensive sectors such as transport, which has raised both concerns and hopes about the implications for (peak) electricity demand. Particularly so when it comes to the potential impact that private EV charging might have on residential demand patterns. On the one hand, the more pessimistic view suggests that this could substantially increase the demand experienced during peak periods, exacerbating the problems associated with such peaks. On the other hand, the more optimistic view suggests that mass uptake of EVs could offer the opportunity to integrate them as distributed storage units. There is evidence of the fact that synchronisation of practices associated with the use of energy-intensive devices is largely to blame for the occurrence of large peaks in demand; the question of whether this is likely to be the case for EV charging as well remains. This paper adds to the literature on the analysis of the synchronisation of energy-related practices with an in-depth analysis commuting behaviour, using driver commuters as a case study. Cluster analysis is used to identify those commuters with distinctive commuting schedules, and socio-demographic profiling of clusters is carried out with a view to identifying any meaningful correlations that could help target policy interventions. Three characteristic commuting patterns were identified, with clearly distinguishable features in terms of the timing of commuting trips. The analysis of the energy-relevant activities shows that arrival times have a noticeable impact on the scheduling and distribution of periods of engagement in such activities.

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 longterm dependencies in behavior is of increasing importance, e.g. for the determination of flexibility potentials of individual households urgently needed to compensate supplyside 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 modelling consistent electricity, heat and mobility demand profiles of households. The approach developed provides the basis for making high-quality weekly activity data available to the general public without having to carry out complex application procedures.