| Summary: | In this paper, we present the Home Electricity Data Generator (HEDGE), an open-access tool for the random generation of realistic residential energy data. HEDGE generates realistic daily profiles of residential PV generation, household electric loads, and electric vehicle consumption and at-home availability, based on real-life UK datasets. The lack of usable data is a major hurdle for research on residential distributed energy resources characterisation and coordination, especially when using data-driven methods such as machine learning-based forecasting and reinforcement learning-based control. We fill this gap with the open-access HEDGE tool which generates data sequences of energy data for several days in a way that is consistent for single homes, both in terms of profile magnitude and behavioural clusters. • From raw datasets, pre-processing steps are conducted, including filling in incomplete data sequences, and clustering profiles into behaviour clusters. Transitions between successive behaviour clusters and profiles magnitudes are characterised. • Generative adversarial networks (GANs) are then trained to generate realistic synthetic data representative of each behaviour groups consistent with real-life behavioural and physical patterns. • Using the characterisation of behaviour cluster and profile magnitude transitions, and the GAN-based profiles generator, a Markov chain mechanism can generate realistic energy data for successive days.
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