Day-Ahead Spot Market Price Forecast Based on a Hybrid Extreme Learning Machine Technique: A Case Study in China

Jun Dong, Xihao Dou, Aruhan Bao, Yaoyu Zhang and Dongran Liu
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Jun Dong: Department of Economic Management, North China Electric Power University, Beijing 102206, China
Xihao Dou: Department of Economic Management, North China Electric Power University, Beijing 102206, China
Aruhan Bao: Department of Economic Management, North China Electric Power University, Beijing 102206, China
Yaoyu Zhang: Department of Economic Management, North China Electric Power University, Beijing 102206, China
Dongran Liu: Department of Economic Management, North China Electric Power University, Beijing 102206, China

Sustainability, 2022, vol. 14, issue 13, 1-24

Abstract: With the deepening of China’s electricity spot market construction, spot market price prediction is the basis for making reasonable quotation strategies. This paper proposes a day-ahead spot market price forecast based on a hybrid extreme learning machine technology. Firstly, the trading center’s information is examined using the Spearman correlation coefficient to eliminate characteristics that have a weak link with the price of power. Secondly, a similar day-screening model with weighted grey correlation degree is constructed based on the grey correlation theory (GRA) to exclude superfluous samples. Thirdly, the regularized limit learning machine (RELM) is tuned using the Marine Predators Algorithm (MPA) to increase RELM parameter accuracy. Finally, the proposed forecasting model is applied to the Shanxi spot market, and other forecasting models and error computation methodologies are compared. The results demonstrate that the model suggested in this paper has a specific forecasting effect for power price forecasting technology.

Keywords: electricity market; price prediction; CRITIC; MPA; RELM (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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