基于无迹卡尔曼滤波神经网络的光伏发电预测

针对光伏发电系统在不同天气状况下发电功率预测精度不高的问题，在分析传统方法的基础上，提出一种无迹卡尔曼滤波神经网络光伏发电预测方法。该方法利用无迹卡尔曼滤波实时更新神经网络模型的权重，以直流电压和电流作为系统的输入，以有功功率和无功功率作为系统的输出，分别建立两个独立的双输入单输出功率预测模型。实验结果表明：所提出的方法对有功功率和无功功率的预测精度分别为97.3%和94.2%，并且对天气具有良好的鲁棒性。

Photovoltaic power forecasting based on unscented Kalman filtering neural network

As the existing photovoltaic power prediction methods have low robustness under different weather conditions, we proposed a new method for the prediction of photovoltaic power system based on the unscented Kalman filtering (UKF) neural network. The method uses the unscented Kalman filter to update the weight of the neural network model in real time, and establishes two independent dual-input-single-output models with taking DC voltage and current as input and active power and reactive power as output. The experimental results indicate that the proposed UKF neural network model can accurately forecast the photovoltaic power, the best fit of active and reactive power are 97.3% and 94.2% respectively, and the method is robust to weather conditions.