Predicting Baseline for Analysis of Electricity Pricing

Date Published
09/2017
Publication Type
Journal Article
Authors
DOI
10.1504/IJBDI.2018.10008133
Abstract

To understand the impact of new pricing structure on residential electricity demands, we need a baseline model that captures every factor other than the new price. The standard baseline is a randomized control group, however, a good control group is hard to design. This motivates us to develop data-driven approaches. We explored many techniques and designed a strategy, named LTAP, that could predict the hourly usage years ahead. The key challenge in this process is that the daily cycle of electricity demand peaks a few hours after the temperature reaching its peak. Existing methods rely on the lagged variables of recent past usages to enforce this daily cycle. These methods have trouble making predictions years ahead. LTAP avoids this trouble by assuming the daily usage profile is determined by temperature and other factors. In a comparison against a well-designed control group, LTAP is found to produce accurate predictions. 

Notes

This is the preprint version of a paper published in International Journal of Big Data Intelligence. The published version of the article can be found here:

http://www.inderscience.com/info/inarticle.php?artid=88269

doi:10.1504/IJBDI.2018.10008133

Journal
International Journal of Big Data Intelligence (IJBDI)
Volume
Vol. 5
Year of Publication
2017
Issue
No. 1/2, 2018
Pagination
3-20
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