电力系统及其自动化学报2011，Vol.23Issue(2)：31-37,7.
文化微粒群神经网络在用电量预测中的应用
Application of Cultural Particle Swarm Optimization Neural Network in Electric Load Forecasting
摘要
Abstract
In order to improve the speed and forecasting precision of traditional neural network (NN), a cultural particle swarm optimization neural network (CPSONN) was proposed by integrating culture algorithm (CA)and particle swarm optimization algorithm (PSO) into NN. The proposed model was used to construct a middie-long-term electricity load forecasting model in an area of China. To further optimize the model data input,a rolling time window technique is used to deal with input and output data at the same time. This method synthesizes the global optimization ability of PSO and the evolutionary advantage of CA. Comparing with grey forecasting model and the actual field data, results show that the CPSONN with rolling time window technique is more effective for middle-long-term load forecasting method in this region.关键词
文化算法/微粒群算法/灰色理论/神经网络/滚动时间窗/中长期用电负荷预测Key words
cultural algorithm/ particle swarm optimization algorithm/ grey theory/ neural network； sliding time window/ middle-long-term electric load forecasting分类
信息技术与安全科学引用本文复制引用
陈国初..文化微粒群神经网络在用电量预测中的应用[J].电力系统及其自动化学报,2011,23(2):31-37,7.基金项目
上海市教委科研创新重点项目(09ZZ211) （09ZZ211）
上海市教委重点学科项目(J51901) （J51901）
闵行区-上海电机学院区校合作项目(08Q07) （08Q07）
