Loading...
Support vector regression with asymmetric loss for optimal electric load forecasting
Wu, Ryan Wang, You-Gan Tian, Yu-Chu Burrage, Kevin Cao, Taoyun
Wu, Ryan
Wang, You-Gan
Tian, Yu-Chu
Burrage, Kevin
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.
Keywords
asymmetric loss, cost-orientation, machine learning, statistical modeling, load forecasting
Date
2021
Type
Journal article
Journal
Energy
Book
Volume
223
Issue
Page Range
1-12
Article Number
Article 119969
ACU Department
Institute for Learning Sciences and Teacher Education (ILSTE)
Faculty of Education and Arts
Faculty of Education and Arts
Relation URI
Source URL
Event URL
Open Access Status
Published as green open access
License
File Access
Controlled
Open
Open