基于图相似日和PSO-XGBoost的光伏功率预测

Photovoltaic Power Prediction Based on Graph Similarity Day and PSO-XGBoost

为充分利用历史信息,最大限度优化模型效果,提高光伏功率预测精度,提出了基于图相似日和粒子群优化极限梯度提升树(particle swarm optimization-extreme gradient boosting tree,PSO-XGBoost)的光伏功率预测方法。将每天由天气特征组成的向量转换成格拉姆矩阵,以便充分挖掘各个向量之间的关系;然后将拉姆矩阵转换为图像,采用结构相似性算法(structural similarity,SSIM)寻找与待预测日最相似的历史日,并选取历史日的光伏功率、待预测日的辐照度、温度、湿度作为极限梯度提升树的输入变量;为充分发挥模型的预测能力,运用粒子群算法优化极限梯度提升树确定最优的超参数,最终输出各时段的光伏功率预测值。利用光伏电站实际数据进行验证,结果表明:相比于未改进的XGBoost模型,所提方法在晴天下的均方根误差(root mean square error,RMSE)降低了31.6%,平均绝对误差(mean absolute error,MAE)降低了31.6%;在多云天气下的RMSE降低了24.1%,MAE降低了40%;在阴雨天气下的RMSE降低了25%,MAE降低了38.5%,有效地提高了模型的预测精度和泛化能力。

In order to make full use of historical information,maximize the effect of the model,and improve the accuracy of photovoltaic power prediction,we proposed a photovoltaic power prediction method based on graph similarity days and particle swarm optimization-extreme gradient boosting tree(PSO-XGBoost).The daily vector composed of weather features was converted into a Gram matrix to fully explore the relationship between the various vectors,and then the Ram matrix was converted into an image.Moreover,the structural similarity algorithm(SSIM)was adopted to find the best similar historical days,and the photovoltaic power of the historical day,the irradiance,temperature,and humidity of the day to be predicted were selected as the input variables of the extreme gradient boosting tree.In order to give full play to the predictive ability of the model,the particle swarm algorithm was adopted to optimize the extreme gradient boosting tree to determine optimal hyperparameters,and the predicted value of photovoltaic power was finally output in each period.The actual data of photovoltaic power plants were used for verification.The results show that,compared with the unimproved XGBoost model,the method proposed in this paper can be employed to reduce the root mean square error(RMSE)by 31.6%and the mean absolute error(MAE)by 31.6%in sunny days;the RMSE is reduced by 24.1%and the MAE by 40%in cloudy weather;RMSE is reduced by 25%and MAE is reduced by 38.5%under rainy weather,which effectively improves the prediction accuracy and generalization ability of the model.