Day-Ahead Electricity Price Forecasting Strategy Based on Machine Learning and Optimization Algorithm

被引：0

作者：

Sun, Caixin ^{[1
]}

Pan, Xiaofeng ^{[1
]}

Li, Gang ^{[2
]}

Li, Pengfei ^{[3
]}

Gao, Guoqing ^{[4
]}

Tian, Ye ^{[2
]}

Xu, Gesheng ^{[5
]}

机构：

[1] Huaneng Clean Energy Res Inst, Beijing, Peoples R China

[2] Huaneng Grp Co Ltd, Shanxi Branch, Taiyuan, Peoples R China

[3] Huaneng Shanxi Power Supply Co Ltd, Taiyuan, Peoples R China

[4] Huaneng Shanghai Renewable Corp Co Ltd, Shanghai, Peoples R China

[5] Xian FPA Energy Technol Co Ltd, Xian, Peoples R China

来源：

2022 4TH ASIA ENERGY AND ELECTRICAL ENGINEERING SYMPOSIUM (AEEES 2022) | 2022年

关键词：

system load rate; electricity price forecast; machine learning; optimization algorithm; REGRESSION;

D O I：

10.1109/AEEES54426.2022.9759695

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

Electric power marketization is the product of the continuous development of the current electric power system. Day-ahead electricity price plays an important role in power market, so the prediction of day-ahead electricity price becomes very important. In this paper, the load rate of the system is introduced, and the machine learning algorithm is adopted. In addition, the improvement and expansion of the machine learning algorithm are realized by combining the tuning advantage of the bionic optimization algorithm, and then the accurate prediction of day-ahead electricity price is realized. The results show that this strategy algorithm can greatly improve the accuracy of day-ahead electricity price forecasting.

引用

页码：254 / 259

页数：6

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