基于遗传算法和BP神经网络的城区中长期电力负荷预测与分析  

Forecasting and analysis on long-term/mid-term electric load of city by GA-BP neural networks

作　　者：程玉桂[1] 黎明[1] 林明玉[1] 

机构地区：[1]南昌航空大学经济管理学院,南昌330063

出　　处：《计算机应用》2010年第1期224-226,共3页journal of Computer Applications

基　　金：国家自然科学基金资助项目(60475002);航空科学基金资助项目(2008ZD5600);江西省教育厅科技项目(GJJ09197)

摘　　要：由于产业结构的调整、居民消费能力消费结构的变化和市场化等因素的影响,城区中长期电力负荷预测具有相当的难度。建立一个基于遗传算法和BP算法相结合的神经网络预测模型,以南昌市为例做实证,并与传统BP神经网络和模拟退火预测结果做对比,验证了该模型的准确性。最后对城区未来十几年的基本用电负荷进行了预测和分析。Due to the industrial structure adjustment, the change of resident consumption ability and pattern of consumption, and market-oriented and so on, long-term/mid-term power load forecasting for urban plans faces considerable difficulties. In the past two years, the methods that combined genetic algorithm and Back Propagation （BP） algorithm have been used for short-term power load forecasting rather than long-term/mid-term power load forecast of electricity. In this paper, a neural network prediction model with combination of genetic algorithm and BP neural network was established; the example in Nanchang was given to validate the accuracy of the algorithm, by comparing with the traditional BP neural network and Simulated Annealing （SA） prediction. Then the basic electricity load of Nanchang in the next dozens of years was forecasted and analyzed.

关 键 词：中长期电力负荷 模拟退火算法 前馈型网络 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]