Predicting long-term electricity prices using modified support vector regression method

被引:7
作者
Abroun, Mehdi [1 ]
Jahangiri, Alireza [1 ]
Shamim, Ahmad Ghaderi [1 ]
Heidari, Hanif [2 ]
机构
[1] Islamic Azad Univ, Dept Elect Engn, Hamedan Branch, Hamadan, Iran
[2] Damghan Univ, Dept Appl Math, Damghan, Iran
基金
英国科研创新办公室;
关键词
Monthly electricity price; Grey Verhulst method; Support vector regression; Forecasting; Time series; NEURAL-NETWORK; LOAD; CONSUMPTION;
D O I
10.1007/s00202-023-02174-x
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
The energy market operates in a highly deregulated and competitive environment, where electricity price plays a crucial role. Forecasting electricity prices presents a significant challenge due to the influence of complex factors such as weather patterns, fuel costs, and the advancement of renewable energy technologies. This study focuses on monthly electricity prices in four neighboring European countries: Bulgaria, Greece, Hungary, and Romania, which share similar weather conditions and economic characteristics. The research investigates the efficacy of four forecasting methods: Grey Verhulst Model (GVM), Nonlinear Regression, Feedforward Neural Network, and Support Vector Regression (SVR). These methods are applied to both short-term (1 month-ahead) and long-term (up to 7 months) electricity price forecasting in the aforementioned countries. The findings reveal that GVM proves suitable for short-term predictions. However, when it comes to long-term forecasting, SVR accurately captures the trends and turning points in electricity prices, albeit with unsatisfactory error rates. To address this issue, a modified version of SVR, referred to as Modified SVR (MSVR), is proposed to mitigate the errors. The results demonstrate that MSVR is an effective approach for long-term electricity price prediction.
引用
收藏
页码:4103 / 4114
页数:12
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