Predicting Baseline for Analysis of Electricity Pricing

23 Pages Posted: 3 May 2016

See all articles by Taehoon Kim

Taehoon Kim

Ulsan National Institute of Science and Technology (UNIST)

Dongeun Lee

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab)

Jaesik Choi

Ulsan National Institute of Science and Technology (UNIST)

Anna Spurlock

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab)

Alexander Sim

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab)

Annika Todd

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab)

Kesheng Wu

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab)

Date Written: March 30, 2016

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 devlop 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.

Keywords: baseline model, residential electricity consumption, outdoor temperature, gradient tree boosting, electricity rate scheme

JEL Classification: D12, C80, D40

Suggested Citation

Kim, Taehoon and Lee, Dongeun and Choi, Jaesik and Spurlock, Anna and Sim, Alexander and Todd, Annika and Wu, Kesheng, Predicting Baseline for Analysis of Electricity Pricing (March 30, 2016). Available at SSRN: https://ssrn.com/abstract=2773991

Taehoon Kim

Ulsan National Institute of Science and Technology (UNIST) ( email )

gil 50
Ulsan, 689-798
Korea, Republic of (South Korea)

Dongeun Lee

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab) ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

Jaesik Choi

Ulsan National Institute of Science and Technology (UNIST) ( email )

gil 50
Ulsan, 689-798
Korea, Republic of (South Korea)

Anna Spurlock

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab) ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

Alexander Sim

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab) ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

Annika Todd

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab) ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

Kesheng Wu (Contact Author)

University of California, Berkeley - Lawrence Berkeley National Laboratory (Berkeley Lab) ( email )

1 Cyclotron Road
Berkeley, CA 94720
United States

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