Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles
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 ava...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2024-06-01
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| Series: | MethodsX |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2215016124000724 |
| _version_ | 1797295763269615616 |
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| author | Flora Charbonnier Thomas Morstyn Malcolm McCulloch |
| author_facet | Flora Charbonnier Thomas Morstyn Malcolm McCulloch |
| author_sort | Flora Charbonnier |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-07T21:53:35Z |
| format | Article |
| id | doaj.art-b4f28cfdbed140a1a9d253ea75d2aa97 |
| institution | Directory Open Access Journal |
| issn | 2215-0161 |
| language | English |
| last_indexed | 2024-03-07T21:53:35Z |
| publishDate | 2024-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | MethodsX |
| spelling | doaj.art-b4f28cfdbed140a1a9d253ea75d2aa972024-02-25T04:35:45ZengElsevierMethodsX2215-01612024-06-0112102618Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profilesFlora Charbonnier0Thomas Morstyn1Malcolm McCulloch2Corresponding author.; Department of Engineering Science, University of Oxford, UKDepartment of Engineering Science, University of Oxford, UKDepartment of Engineering Science, University of Oxford, UKIn 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.http://www.sciencedirect.com/science/article/pii/S2215016124000724DatasetsData-driven methodsOpen accessDemand-side responseDistributed energy resourcesBuildings |
| spellingShingle | Flora Charbonnier Thomas Morstyn Malcolm McCulloch Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles MethodsX Datasets Data-driven methods Open access Demand-side response Distributed energy resources Buildings |
| title | Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles |
| title_full | Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles |
| title_fullStr | Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles |
| title_full_unstemmed | Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles |
| title_short | Home electricity data generator (HEDGE): An open-access tool for the generation of electric vehicle, residential demand, and PV generation profiles |
| title_sort | home electricity data generator hedge an open access tool for the generation of electric vehicle residential demand and pv generation profiles |
| topic | Datasets Data-driven methods Open access Demand-side response Distributed energy resources Buildings |
| url | http://www.sciencedirect.com/science/article/pii/S2215016124000724 |
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