Understanding how to make demand more flexible in the residential buildings necessitates a more in-depth examination of what electricity is used for in households. We investigate how the scheduling and sequencing of domestic activities are related to electricity consumption at various times of day and we consider how this knowledge can inform efforts to improve demand-side flexibility.
Department: Construction Management & Engineering
Supervised by: M谩t茅 J谩nos L艖rincz
In general, little is known about how the temporal variation of institutional policies (such as work hours) affects the scheduling and sequencing of domestic activities and how this relates to the timing of energy demand. We can see from our daily experiences that "normal" working hours contribute to the creation of peaks in the level of certain activities at specific times of the day. For instance, we all have the notion of "rush hours". When the activities in question are energy-relevant鈥攖hat is, they have a significant impact on observed energy demand loads鈥攖his causes peaks in energy demand. This explains the prevalence of evening peak periods in the United Kingdom, which occur when people return home and begin cooking, watching TV, eating, or socialising. In this research, we explore how the scheduling and sequencing of domestic activities are related to electricity consumption at various times of the day. We analyse a large collection of time-use survey data from United Kingdom (1974, 1983/5 (鈥�1985鈥�), 2000/1 (鈥�2000鈥�) and 2014/5 (鈥�2014鈥�)) using sequence methods and cluster analysis. Historical time-use surveys assist us to comprehend the evolution of electricity-intensive activity sequences. We aim to identify common daily sequence patterns among these diaries, including distinct clusters of work, non-work and leisure time. Understanding the sequences of activities and structure of the daily routines could provide information to smart home automation systems enabling residential demand-side response services.
The student will learn how to do a literature review, how to analyse data with R software, and how to present and write up the results. The archival time use survey data will be analysed first, and followed by sequence analysis and clustering methodology.
The student should have knowledge in statistical programming languages with a keen interest and enthusiasm for the study of residential electricity consumption patterns. Competence with computers and statistics is beneficial but not crucial. Previous experience of handling time-use data and working knowledge in R programming language is welcomed but is not crucial.
The student will gain hands on experience in all stages of the research process from start to finish, including reviewing the literature, sourcing the data, analyzing the data, and presenting and writing up the results. The supervisor will work closely with the student at all stages to help them develop new skills and expertise in each area. If the study results in publishable findings, the student would be involved in the write-up of the results and included as an author on any publications will be included in the results write-up and will be listed as an author on any publications. General transferable abilities to be cultivated include teamwork, computing and analytical skills, report writing and presentation skills.
School of the Built Environment
9am-5pm
Monday 03 July 2023 - Friday 11 August 2023
The deadline to apply for this opportunity is Sunday 7th May 2023. Students should submit their CV and Cover Letter directly to the Project Supervisor (click on supervisor name at the top of the page for email). Successful candidates will be invited for an interview.