A platform for research: civil engineering, architecture and urbanism
A platform for research: civil engineering, architecture and urbanism
Characterising temporal aspects of residential electricity consumption using statistical learning
Achieving ambitious climate change mitigation targets requires a comprehensive transformation of the energy system. As part of this transformation, early, rapid, and full decarbonisation of the electricity sector is essential. Residential buildings contribute substantially to electricity consumption, so pathways for electricity system decarbonisation similarly depend on accelerated change in the residential sector. Delivering further and faster reductions in emissions from residential buildings requires not only reductions in electricity demand but also more responsive demand. Flexibility in the residential sector can support the supply-side transition to renewable energy while also helping reduce the costs of electricity provision during times of peak demand. Understanding the potential contributions of residential buildings to ambitious mitigation pathways through demand reduction and flexibility requires more detailed characterisations of demand. In particular, it necessitates stronger evidence on the structural and occupant factors that shape electricity demand and its resulting emissions. This thesis contributes to these research areas and is guided by three primary research questions. The first focuses on improving methods for characterising residential electricity consumption, the second considers advancing our knowledge of factors influencing consumption, and the third relates to the policy implications of having deeper insight into the temporal aspects of demand. The three research questions are: • What statistical learning approaches can improve efforts to characterise residential electricity consumption patterns? • What factors explain electricity consumption in residential buildings at annual, daily, and hourly temporal resolutions? • How does a more detailed understanding of the temporal aspects of residential electricity consumption inform energy efficiency and flexibility interventions? This thesis consists of four original research papers, each of which makes methodological, empirical, and policy ...
Characterising temporal aspects of residential electricity consumption using statistical learning
Achieving ambitious climate change mitigation targets requires a comprehensive transformation of the energy system. As part of this transformation, early, rapid, and full decarbonisation of the electricity sector is essential. Residential buildings contribute substantially to electricity consumption, so pathways for electricity system decarbonisation similarly depend on accelerated change in the residential sector. Delivering further and faster reductions in emissions from residential buildings requires not only reductions in electricity demand but also more responsive demand. Flexibility in the residential sector can support the supply-side transition to renewable energy while also helping reduce the costs of electricity provision during times of peak demand. Understanding the potential contributions of residential buildings to ambitious mitigation pathways through demand reduction and flexibility requires more detailed characterisations of demand. In particular, it necessitates stronger evidence on the structural and occupant factors that shape electricity demand and its resulting emissions. This thesis contributes to these research areas and is guided by three primary research questions. The first focuses on improving methods for characterising residential electricity consumption, the second considers advancing our knowledge of factors influencing consumption, and the third relates to the policy implications of having deeper insight into the temporal aspects of demand. The three research questions are: • What statistical learning approaches can improve efforts to characterise residential electricity consumption patterns? • What factors explain electricity consumption in residential buildings at annual, daily, and hourly temporal resolutions? • How does a more detailed understanding of the temporal aspects of residential electricity consumption inform energy efficiency and flexibility interventions? This thesis consists of four original research papers, each of which makes methodological, empirical, and policy ...
Characterising temporal aspects of residential electricity consumption using statistical learning
Satre Meloy, AP (author) / Grünewald, P / Eyre, N
2020-07-02
Theses
Electronic Resource
English
DDC:
690
U.S. Residential Miscellaneous Electric Loads Electricity Consumption
| British Library Conference Proceedings | 2008