中国电力2012，Vol.45Issue(4)：87-91,5.
基于CAPSO-RNN的光伏系统短期发电量预测
Short-term generation forecasting for photovoltaic system based on CAPSO-RNN algorithm
赵杰 1张艳霞1
作者信息
- 1. 天津大学智能电网教育部重点实验室,天津300072
- 折叠
摘要
Abstract
Considering the characteristics of photovoltaic (PV ) systems and various factors which will affect the power generation of PV systems, a short-term generation forecasting model of PV systems was proposed based on recurrent neural network (RNN) and chaos adaptive particle swarm optimization (CAPSO) algorithm. In the model, the weights and thresholds of RNN were optimized by the global optimization ability of CAPSO algorithm in order to avoid the disadvantages of the traditional RNN, such as slow convergence and prone to local minimum. The fuzzy membership function was used to process the data in the temperature evaluation, which can improve generation forecasting precision. Forecasting results show the high accuracy of the proposed model.关键词
光伏系统/发电量预测/混沌自适应粒子群优化算法/反馈型神经网络Key words
photovoltaic system/generation forecasting/chaos adaptive particle swarm optimization algorithm/recurrent neural network分类
信息技术与安全科学引用本文复制引用
赵杰,张艳霞..基于CAPSO-RNN的光伏系统短期发电量预测[J].中国电力,2012,45(4):87-91,5.
