Month ahead average daily electricity price profile forecasting based on a hybrid nonlinear regression and SVM model:an ERCOT case study

• 作者: Chongqing KANG Haiwang ZHONG Le XIE Qing XIA Ziming MA
• 来源期刊: 现代电力系统与清洁能源学报(英文)
• 学科: 工学
• 关键词: ELECTRICITY PRICE forecasting MONTH AHEAD AVERAGE DAILY ELECTRICITY PRICE profile Nonlinear regression model Support vector machine(SVM) Electric Reliability council of Texas(ERCOT)
• 年，卷（期）: 2018,（2）
• 页码范围: 281-291
• 页数: 11页
• 分类号: TM73

摘要

With the deregulation of the electric power industry, electricity price forecasting plays an increasingly important role in electricity markets, especially for retailors and investment decision making. Month ahead average daily electricity price profile forecasting is proposed for the first time in this paper. A hybrid nonlinear regression and support vector machine(SVM) model is proposed. Offpeak hours, peak hours in peak months and peak hours in off-peak months are distinguished and different methods are designed to improve the forecast accuracy. A nonlinear regression model with deviation compensation is proposed to forecast the prices of off-peak hours and peak hours in off-peak months. SVM is adopted to forecast the prices of peak hours in peak months. Case studies based on data from ERCOT validate the effectiveness of the proposed hybrid method.