Dynamic Neural Network Based Genetic Algorithm Optimizing for Short Term Load Forecasting

摘要：

Short term load forecasting (STLF) plays a signi- ficant role in the management of power system of countries and regions on the grounds of insufficient electric energy for increased need. This paper presents an approach of back propagation neural network based genetic algorithm (GA) optimizing to develop the accuracy of predictions. With GA’s optimizing and BP neural network’s dynamic feature, the weight optimization is realized by selection, crossing and mutation operations. Using load time series from a practical power system, we tested the performance of BP neural network based genetic algorithm optimizing by comparing its predictions with that of BP network.

关键词： Short term load forecasting genetic algorithm BP neural network

作者: Yan Wang Yuanwei Jing Weilun Zhao Yan-e Mao

作者单位: College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China She College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China Shenyang Institute of Engineering, Shenyang, 110136, China College of Information Science and Engineering, Northeastern University, Shenyang, 110004, China She

会议类型: 国际会议

会议名称: The 22nd China Control and Decision Conference(2010年中国控制与决策会议)

会议地点: 徐州

会议语种:英文

页码: 2701-2704

在线出版日期: 2010-05-26（万方平台首次上网日期，不代表论文的发表时间）