基于RBF神经网络的电力系统短期负荷预测  

Short-term Prediction of Power Based on RBF Neural Network

作　　者：张慧斌[1] 王黎冰 

机构地区：[1]忻州师范学院,山西忻州034000 [2]忻州市供电分公司,山西忻州034000

出　　处：《忻州师范学院学报》2009年第5期10-12,共3页Journal of Xinzhou Teachers University

摘　　要：电力负荷预测一直是电力系统的重要研究课题。在考虑天气状况因素的前提下,通过MATLAB软件实验平台,构建了一个RBF神经网络模型,并用历史电力负荷数据进行训练,成功的进行了电力系统的短期负荷预测,预测结果误差比较小,结果令人满意,证明了RBF神经网络在电力短期负荷预测领域的有效性和可行性。Load forecasting of a power system has always been an important research subject. In this paper, the author builds a RBF neural network model based on Matlab considering weather conditions. Applying the RBF neural network model, the author makes short - term load forecasting of a power system by using the load data on record. The error of the prediction is relatively small and the results are satisfactory. That shows the RBF neural network owns the effectiveness and feasibility in the field of short - term load forecasting power.

关 键 词：短期负荷预测 时间序列 RBF神经网络 

分 类 号：TM73[电气工程—电力系统及自动化]