Short-Term Load Forecasting Using Radial Basis Function Neural Network

• 作者: Wen-Yeau Chang
• 来源期刊: 电脑和通信(英文)
• 学科: 工学
• 关键词: SHORT-TERM LOAD Forecasting RBF NEURAL NETWORK TAI Power System
• 年，卷（期）: 2015,（11）
• 页码范围: 40-45
• 页数: 6页
• 分类号: TP1

摘要

An accurate short-term forecasting method for load of electric power system can help the electric power system’s operator to reduce the risk of unreliability of electricity supply. This paper proposed a radial basis function (RBF) neural network method to forecast the short-term load of electric power system. To demonstrate the effectiveness of the proposed method, the method is tested on the practical load data information of the Tai power system. The good agreements between the realistic values and forecasting values are obtained;the numerical results show that the proposed forecasting method is accurate and reliable.