• Energy Research

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 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.

The EU proposal on liberalisation of the energy market has been widely debated in policy, stakeholder and academic circles both for its content and the potential consequences to the gas and electricity markets. However, little has been said about the empirical evidence produced by the European commission to support this legislative package. No research effort has been devoted to analysing the impact assessment document that accompanies the third legislative package. Since the impact assessment system has been in place, there have been concerns regarding quality and adequateness, especially when considering alternative policy options, including stakeholder opinions and quantifying costs, benefits and risks. This paper analyses how these crucial issues were factored into the impact assessment on the third legislative package. It investigates the interaction between the legislative proposals on energy liberalisation and its Impact Assessment.

Understanding what constitutes peaks and identifying areas of effective load shifting intervention becomes vital to the balancing of demand and supply of electricity. Whilst there is information about the aggregate level of consumption of electricity, little is known about residential peak demand and what levels of flexibility might be available. Specifically, methodologies linking people’s activities and residential electricity load profiles are typically under-investigated. The overall aim of this paper is to introduce methodologies which capture the variation in sequences of activities taking place at times of peak electricity demand. The paper introduces a set of analytical tools which can be deployed when examining time use survey data in energy demand research. It presents the state of the art with modelling load profiles based on time use data and design methodological modifications to improve modelling around peak periods. It is demonstrated how the methodologies presented in the paper can be applied to specific understanding of distributional effects of Time of Use tariffs. The paper discusses issues associated with validation between synthetic data, survey data and electricity metered data and concludes with policy implications and some observations for future research.

Abstract The introduction of Time of Use (ToU) tariffs may affect residential electricity consumers differently depending not only on their financial position but also time availability. The aim of this paper is to identify socio-demographic groups which may be financially advantaged or disadvantaged by the introduction of ToU tariffs. Assuming no behavioural change, we impose ToU tariffs on UK half hourly smart meter data and the synthetic demand profiles for different household composition generated using the 2014–2015 UK Time Use Survey data and optimisation of energy consumption per activity against the smart meter data. The distributional effects of ToU tariffs are obtained for customer segmentation and socio-demographic groups, and presented in terms of peak to off-peak ratios and impacts on the synthetic demand profiles. Findings on the distributional effects of ToU tariffs reveal regional differences (e.g. positive effects for high income groups in London) and household composition similarities (e.g. positive effects for households with children not in the high-income group).

This study presents the findings of applying a Discrete Demand Side Control (DDSC) approach to the space heating of two case study buildings. High and low tolerance scenarios are implemented on the space heating controller to assess the impact of DDSC upon buildings with different thermal capacitances, light-weight and heavy-weight construction. Space heating is provided by an electric heat pump powered from a wind turbine, with a back-up electrical network connection in the event of insufficient wind being available when a demand occurs. Findings highlight that thermal comfort is maintained within an acceptable range while the DDSC controller maintains the demand/supply balance. Whilst it is noted that energy demand increases slightly, as this is mostly supplied from the wind turbine, this is of little significance and hence a reduction in operating costs and carbon emissions is still attained.

Government initiatives in several developed and developing countries to roll-out smart meters call for research on the sustainability impacts of these devices. In principle smart meters bring about higher control over energy theft and lower consumption, but require a high level of engagement by end-users. An alternative consists of load controllers, which control the load according to pre-set parameters. To date, research has focused on the impacts of these two alternatives separately. This study compares the sustainability impacts of smart meters and load controllers in an occupied office building in Italy. The assessment is carried out on three different floors of the same building. Findings show that demand reductions associated with a smart meter device are 5.2% higher than demand reductions associated with the load controller.

A critical element of assessing a building’s suitability for Demand Side Response (DSR) is understanding its turndown potential to ensure that DSR participation will be financially viable. While research has been undertaken on site level DSR estimation methods, there is currently no research that compares the outcomes of these methods. This paper compares four non-domestic energy estimation methods used for understanding the DSR potential of electrical appliances in a building to provide insights about uncertainty levels based on input requirements. Each method is deployed to estimate the DSR potential of HVAC chiller assets at two UK hotels over two years. The results show the methods have a range of error levels from the highest Mean Average Percentage Error (MAPE) of 159% to the lowest MAPE of 39%. The input requirements followed a general trend of more complex informational inputs resulting in lower error values. The outcomes of this research enable users to make informed decisions in selecting DSR estimation methods based on information availability and acceptable estimation error levels.

In current academic, policy and industry debates there is significant emphasis on the importance of enhancing the level of flexibility of electricity demand. Flexibility is considered critical in order to improve balancing with renewables, reduce costs of electricity generation and make the most of smart systems and battery storage. There remain questions around how flexibility is delivered, and which portions of demand will take part in different aspects of flexibility markets. The aim of the paper is to identify activities in the home for which people may either gain or lose following the introduction of Time of Use (ToU) tariffs. It uses 2014–2015 UK Time Use Survey data to cluster households in terms of similarities in activities at peak time and identify households differently affected by ToU tariffs across several socio-demographic parameters (i.e. work status, income, family structure). Findings show that sociodemographic distribution did not demonstrate any significant dominant parameter. Instead, clustering based on similarities in the timing of activities has provided distinctive patterns and can shed light on groups of people who might be either advantaged or disadvantaged from the introduction of ToU tariffs.