基于BP神经网络的光伏电站短期发电功率预测

Short-term Power Generation Prediction of Photovoltaic Power Plant Based on BP Neural Network

针对光伏电站功率输出的随机性和波动性,文章研究建立了BP神经网络预测模型对某光伏电站发电功率进行预测。将太阳水平总辐射、水平散射辐射、环境温度及环境相对湿度作为模型的输入量,光伏发电功率作为输出量,通过预测模型对光伏电站某一天的发电功率数据进行了预测及误差分析。结果表明,预测结果的趋势曲线与真实曲线基本一致,均方根误差结果为0.176,BP神经网络模型预测精度及准确度较高。

Aiming at the randomness and volatility of the power output of photovoltaic power plants, a BP neural network prediction model is studied and established to predict the power generation of a photovoltaic power plant in this paper. The solar horizontal total radiation, horizontal scattered radiation, ambient temperature and relative humidity are used as the input of the model, and the photovoltaic power generation is used as the output. The power generation data of a day of photovoltaic power plant is predicted and analyzed by the prediction model. The results show that the trend curve of the prediction results is basically consistent with the real curve, and the root mean square error is 0.176.The BP neural network model has high prediction accuracy.