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A Novel Combined Model for Short-Term Electric Load Forecasting Based on Whale Optimization Algorithm.

Authors :
Shang, Zhihao
He, Zhaoshuang
Song, Yanru
Yang, Yi
Li, Lian
Chen, Yanhua
Source :
Neural Processing Letters; Oct2020, Vol. 52 Issue 2, p1207-1232, 26p
Publication Year :
2020

Abstract

Stable electric load forecasting plays a significant role in power system operation and grid management. Improving the accuracy of electric load forecasting is not only a hot topic for energy managers and researchers of the power system, but also a fair challenging and difficult task due to its complex nonlinearity characteristics. This paper proposes a new combination model, which uses the least squares support vector machine, extreme learning machine, and generalized regression neural network to predict the electric load in New South Wales, Australia. In addition, the model employs a heuristic algorithm–whale optimization algorithm to optimize the weight coefficient. To verify the usability and generalization ability of the model, this paper also applies the proposed combined model to electricity price forecasting and compares it with the benchmark method. The experimental results demonstrate that the combined model not only can get accurate results for short-term electric load forecasting, but also achieves fine accuracy for the same period of electricity price forecasting. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
13704621
Volume :
52
Issue :
2
Database :
Complementary Index
Journal :
Neural Processing Letters
Publication Type :
Academic Journal
Accession number :
146150935
Full Text :
https://doi.org/10.1007/s11063-020-10300-0