针对光伏系统的发电特性及影响光伏发电的因素,建立基于混沌自适应粒子群优化算法的反馈型神经网络短期发电量预测模型.该预测模型利用混沌自适应粒子群优化算法的全局优化能力初始化反馈性神经网络权值和阈值,可以克服反馈型神经网络收敛速度慢俄且易陷于局部最优等缺点.同时为提高预测精度,采用隶属度函数对温度进行模糊化处理.预测结果表明,建立的预测模型具有较高的精度.%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.
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