Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Data-driven modelling of energy demand response behaviour based on a large-scale residential trial

Antonopoulos, Ioannis (Heriot-Watt University)
Robu, Valentin (TU Delft Algorithmics)
Couraud, Benoit (Heriot-Watt University)
Flynn, David (Heriot-Watt University)

2021

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.

Artificial intelligence
Artificial neural networks
Demand response
Ensemble methods
Machine learning
Power systems
Residential response behaviour

http://resolver.tudelft.nl/uuid:eae17775-a574-4bfa-b83a-db22a4b15f2b

https://doi.org/10.1016/j.egyai.2021.100071

Energy and AI, 4

Institutional Repository

journal article

© 2021 Ioannis Antonopoulos, Valentin Robu, Benoit Couraud, David Flynn