Support vector regression with asymmetric loss for optimal electric load forecasting

Journal article


Wu, Ryan, Wang, You-Gan, Tian, Yu-Chu, Burrage, Kevin and Cao, Taoyun. (2021). Support vector regression with asymmetric loss for optimal electric load forecasting. Energy. 223, p. Article 119969. https://doi.org/10.1016/j.energy.2021.119969
AuthorsWu, Ryan, Wang, You-Gan, Tian, Yu-Chu, Burrage, Kevin and Cao, Taoyun
Abstract

In energy demand forecasting, the objective function is often symmetric, implying that over-prediction errors and under-prediction errors have the same consequences. In practice, these two types of errors generally incur very different costs. To accommodate this, we propose a machine learning algorithm with a cost-oriented asymmetric loss function in the training procedure. Specifically, we develop a new support vector regression incorporating a linear-linear cost function and the insensitivity parameter for sufficient fitting. The electric load data from the state of New South Wales in Australia is used to show the superiority of our proposed framework. Compared with the basic support vector regression, our new asymmetric support vector regression framework for multi-step load forecasting results in a daily economic cost reduction ranging from 42.19% to 57.39%, depending on the actual cost ratio of the two types of errors.

Keywordsasymmetric loss; cost-orientation; machine learning; statistical modeling; load forecasting
Year2021
JournalEnergy
Journal citation223, p. Article 119969
PublisherElsevier Ltd
ISSN0360-5442
Digital Object Identifier (DOI)https://doi.org/10.1016/j.energy.2021.119969
Scopus EID2-s2.0-85101019931
Open accessPublished as green open access
Page range1-12
FunderAustralian Research Council (ARC)
Guangdong Basic and Applied Basic Research Foundation
Guangdong universities
Author's accepted manuscript
License
File Access Level
Open
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online10 Feb 2021
Publication process dates
Accepted22 Jan 2021
Deposited07 Dec 2022
ARC Funded ResearchThis output has been funded, wholly or partially, under the Australian Research Council Act 2001
Grant IDDP160104292
CE140100049
2020A1515011580
2018GKTSCX010
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