基于神经网络的吕梁市光伏电站发电量预测研究

Power Generation Forecasting of Photovoltaic Power Station in Lvliang City Based on Neural Network

依据吕梁市光伏电站的历史发电数据和历史气象数据,使用BP神经网络建立了光伏电站发电量预测模型。模型一的输入变量为天气类型、最高温度、最低温度和前一日的发电量,模型二的输入变量为天气类型、最高温度、最低温度和相似日的发电量。使用预测模型预测了2021年5月10日至16日连续7天的发电量。其中模型一的平均绝对百分误差为28.89%,模型二的平均绝对百分误差为16.39%。通过对比发现,使用相似日发电量作为神经网络模型的输入变量可显著提高预测精度。

Based on the historical power generation data and historical meteorological data of Lvliang photovoltaic power station,the power generation prediction model of photovoltaic power station is established by using BP neural network.The input variables of model 1 are weather type,maximum temperature,minimum temperature and power generation of the previous day,and the input variables of model 2 are weather type,maximum temperature,minimum temperature and power generation of similar days.The prediction model was used to predict the power generation for 7 consecutive days from May 10 to 16,2021.The average absolute percentage error of model 1 is 28.89%,and the average absolute percentage error of model 2 is 16.39%.Through comparison,it is found that using similar daily power generation as the input variable of neural network model can significantly improve the prediction accuracy.