Essays on Residential Electricity Consumption Profiles: Weather Effects and Household Behaviour Patterns

The high temporal resolution data created by smart metering, which has now been deployed in many countries, provides an unprecedented opportunity to examine household consumption behaviour in narrow time windows, whereas past studies could only look at monthly or even yearly consumption. However, most studies that have used smart meter data focused either on load management (load forecasting, theft detection, etc.) or linked electricity usage to demographic and/or building characteristics. Few studies have been conducted on the impacts of weather on intraday consumption behaviour. Better appreciation of the influence of weather could improve pricing designs as well as provide better understanding of household behaviour, which could, for example, potentially increase energy efficiency. With knowledge of weather effects on residential consumption, it could also be valuable for utilities to improve grid stability and reduce operation cost. To fill the gap, this dissertation analyses the impact of different weather variables as well as consumption patterns through different tools based on smart metering data. This thesis uses a three article format. Chapter 1 provides a general overview of the literature on smart meters and empirical studies using smart metering data. Chapter 2 presents an econometric analysis of the effect of weather factors in Ireland (such temperature, rainfall and sun duration) at different periods of a day, and contrasts the impacts on consumption for workdays versus weekends versus holidays. Chapter 3 employs machine learning methods – clustering algorithms – to categorise households by their electricity demand response to different weather variables. The results demonstrated that some weather sensitivity patterns are closely associated with household characteristics. In Chapter 4, smart meter data was gathered from a very different location, Chengdu, the capital of Sichuan Province in China, which has more extreme weather and greater variability. Three scenarios are analysed in Chapter 4: (1) weekly consumption profiles in different seasons; (2) festival (major holiday) consumption profiles; and (3) consumption patterns during extreme weather. Finally, the thesis is concluded by Chapter 5, which summarises the main empirical and methodological contributions of the three papers and lays out future work in this area.

Name dc:type.qualificationname

Doctor of Philosophy (PhD)

Level dc:type.qualificationlevel

Doctoral

Grantor dc:publisher.institution

University of Cambridge

Year dc:date.issued

2020

Author dc:creator

• Kang, Jieyi

Advisor dc:contributor.advisor

• Reiner, David

• smart metering
• data mining
• residential electricity
• consumption behaviour
• weather response

Licence

• https://www.repository.cam.ac.uk/bitstreams/b9c2043d-aa93-4451-8524-0ffcbe4f8071/download
• https://www.rioxx.net/licenses/all-rights-reserved/

Language dc:language

eng

DOI dc:identifier.doi

https://doi.org/10.17863/CAM.72083

OAI identifier oai:identifier

oai:www.repository.cam.ac.uk:1810/324633