Data-driven modelling of energy demand response behaviour based on a large-scale residential trial
Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing compl...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2021-06-01
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| Series: | Energy and AI |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666546821000252 |
| _version_ | 1818936314141081600 |
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| author | Ioannis Antonopoulos Valentin Robu Benoit Couraud David Flynn |
| author_facet | Ioannis Antonopoulos Valentin Robu Benoit Couraud David Flynn |
| author_sort | Ioannis Antonopoulos |
| collection | DOAJ |
| description | Recent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the electrification of transport networks. Currently, less than 2% of the global potential for demand-side flexibility is currently utilised, but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential. In order to achieve this target, acquiring a better understanding of how residential DR participants respond in DR events is essential – and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge. This study provides an in-depth analysis of how residential customers have responded in incentive-based DR, utilising household-related data from a large-scale, real-world trial: the Smart Grid, Smart City (SGSC) project. Using a number of different machine learning approaches, we model the relationship between a household’s response and household-related features. Moreover, we examine the potential effects of households’ features on the residential response behaviour, and highlight a number of key insights which raise questions about the reported level of consumers’ engagement in DR schemes, and the motivation for different customers’ response level. Finally, we explore the temporal structure of the response – and although we found no supporting evidence of DR responders learning over time for the available data from this trial, the proposed methodologies could be used for longer-term longitudinal DR studies. Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area. |
| first_indexed | 2024-12-20T05:34:05Z |
| format | Article |
| id | doaj.art-d01eb83a30c8455f9c74192fbdd062d0 |
| institution | Directory Open Access Journal |
| issn | 2666-5468 |
| language | English |
| last_indexed | 2024-12-20T05:34:05Z |
| publishDate | 2021-06-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Energy and AI |
| spelling | doaj.art-d01eb83a30c8455f9c74192fbdd062d02022-12-21T19:51:41ZengElsevierEnergy and AI2666-54682021-06-014100071Data-driven modelling of energy demand response behaviour based on a large-scale residential trialIoannis Antonopoulos0Valentin Robu1Benoit Couraud2David Flynn3School of Engineering and Physical Sciences, Earl Mountbatten Building, Heriot-Watt University, EH14 4AS Edinburgh, UK; Corresponding author at: School of Engineering and Physical Sciences, Earl Mountbatten Building, Heriot-Watt University, EH14 4AS Edinburgh, UK.School of Engineering and Physical Sciences, Earl Mountbatten Building, Heriot-Watt University, EH14 4AS Edinburgh, UK; CWI, National Research Center for Mathematics and Computer Science, Amsterdam 1098 XG, the Netherlands; Algorithmics Group, EEMCS, Delft University of Technology, 2628 XE Delft, The NetherlandsSchool of Engineering and Physical Sciences, Earl Mountbatten Building, Heriot-Watt University, EH14 4AS Edinburgh, UKSchool of Engineering and Physical Sciences, Earl Mountbatten Building, Heriot-Watt University, EH14 4AS Edinburgh, UKRecent years have seen an increasing interest in Demand Response (DR), as a means to satisfy the growing flexibility needs of modern power grids. This increased flexibility is required due to the growing proportion of intermittent renewable energy generation into the energy mix, and increasing complexity in demand profiles from the electrification of transport networks. Currently, less than 2% of the global potential for demand-side flexibility is currently utilised, but a more widespread adoption of residential consumers as flexibility resources can lead to substantially higher utilisation of the demand-side flexibility potential. In order to achieve this target, acquiring a better understanding of how residential DR participants respond in DR events is essential – and recent advances in novel machine learning and statistical AI provide promising tools to address this challenge. This study provides an in-depth analysis of how residential customers have responded in incentive-based DR, utilising household-related data from a large-scale, real-world trial: the Smart Grid, Smart City (SGSC) project. Using a number of different machine learning approaches, we model the relationship between a household’s response and household-related features. Moreover, we examine the potential effects of households’ features on the residential response behaviour, and highlight a number of key insights which raise questions about the reported level of consumers’ engagement in DR schemes, and the motivation for different customers’ response level. Finally, we explore the temporal structure of the response – and although we found no supporting evidence of DR responders learning over time for the available data from this trial, the proposed methodologies could be used for longer-term longitudinal DR studies. Our study concludes with a broader discussion of our findings and potential paths for future research in this emerging area.http://www.sciencedirect.com/science/article/pii/S2666546821000252Artificial intelligenceMachine learningArtificial neural networksEnsemble methodsDemand responseResidential response behaviour |
| spellingShingle | Ioannis Antonopoulos Valentin Robu Benoit Couraud David Flynn Data-driven modelling of energy demand response behaviour based on a large-scale residential trial Energy and AI Artificial intelligence Machine learning Artificial neural networks Ensemble methods Demand response Residential response behaviour |
| title | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial |
| title_full | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial |
| title_fullStr | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial |
| title_full_unstemmed | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial |
| title_short | Data-driven modelling of energy demand response behaviour based on a large-scale residential trial |
| title_sort | data driven modelling of energy demand response behaviour based on a large scale residential trial |
| topic | Artificial intelligence Machine learning Artificial neural networks Ensemble methods Demand response Residential response behaviour |
| url | http://www.sciencedirect.com/science/article/pii/S2666546821000252 |
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