Data-Driven Analysis of Time of Day Pricing for Residential Consumers

Time-of-day (or dynamic time-of-use, dToU) pricing is a mechanism by which system operators try to lower stress on the grid in times of high demand. The price for high demand periods is pre-set but the times of day they are applied is dynamic. Data on how residential consumers respond to the pricing scheme can inform more accurate models of consumption to maintain the integrity of the grid while lowering consumers' utility bills and optimizing renewable use. In this thesis, I analyze the data from a time-of-day pricing trial in London to see whether the treatment was effective in lowering consumption. I do this analysis using four different models and compare the accuracy of each and the results; an aggregated linear regression model, a multi linear regression model, an aggregated multi linear regression model, and a random forest regression time series model. I found that the time-of-day pricing during the trial was effective in lowering consumption and costs. A dependence on households' socio-economic status was observed.

Name thesis:degree_name

Master

Department dc:contributor.department

Massachusetts Institute of Technology. Department of Electrical Engineering and Computer Science

Grantor dc:publisher

Massachusetts Institute of Technology

Year dc:date.issued

2022

Author dc:creator

• Nejad, Saba

Advisor dc:contributor.advisor

• Dahleh, Munther A.

Statement dc:rights

• In Copyright - Educational Use Permitted
• Copyright MIT

Licence dc:rights.uri

• http://rightsstatements.org/page/InC-EDU/1.0/

Handle dc:identifier.uri

https://hdl.handle.net/1721.1/144969

OAI identifier oai:identifier

oai:dspace.mit.edu:1721.1/144969