改进BP神经网络的光伏系统发电功率预测

为了提高光伏发电功率的预测精度，提出一种改进BP神经网络的光伏发电功率预测模型.首先采用包括室外温度、光照辐射量、风速等作为输入层节点，交流发电功率作为输出节点，引入RMSE作为衡量最优模型指标，确定了隐含层节点数，然后采用BP神经网络对其进行学习，并采用布谷鸟搜索算法对BP神经网络进行优化，最后采用仿真实验对其有效性进行测试.结果表明，改进神经网络提高了光伏发电功率预测精度，具有一定的推广价值.

Prediction of Capacity of Power Generation System Based on Improved BP Neural Network

In order to improve the prediction accuracy of photovoltaic power generation, a prediction model of photovoltaic power generation based on improved BP neural network is proposed. First, such factors as outdoor temperature, light radiation, wind speed and other factors are taken as input layer nodes while AC power is taken as output nodes, RMSE is introduced as indicators to measure the optimal model to determine number of hidden layer nodes, and then BP neural network is used to learn which cuckoo search algorithm is used to optimize BP neural network. Finally, the simulation experiment is used to test its effectiveness. The results show that improved neural network can improve prediction accuracy of photovoltaic power generation, and it has a widespread value.