基于Spearman-GCN-GRU模型的超短期多区域电力负荷预测

Ultra-short-term Multi-region Power Load Forecasting Based on Spearman-GCN-GRU Model

为提升多区域电力负荷的预测精度,聚焦于多区域电力数据的时空相关性分析,提出了一种基于Spearman-GCN-GRU的超短期多区域电力负荷预测模型。该模型通过Spearman相关系数分析不同区域电力负荷的时空相关性,构建Spearman邻接矩阵并输入图卷积神经网络(graph convolutional network,GCN)和门控循环单元(gated recurrent unit,GRU)提取数据中的空间特征和时序特征,最后由多层感知机(multilayer perceptron,MLP)解码输出预测结果。与基于距离邻接矩阵的模型进行对比,验证了SpearmanGCN-GRU模型的可行性。在模型的预测精度上,与传统统计模型和神经网络模型相比,Spearman-GCNGRU模型在通用的评价指标中均取得最优结果。就均方根误差(root mean square error,RMSE)而言,SpearmanGCN-GRU模型与神经网络模型GRU、GCN和深度神经网络(deep neural network,DNN)相比,RMSE指标分别下降了13.90%、11.66%和8.36%,验证了模型具有更好的预测效果。

To improve the prediction accuracy of multi-region power load,an ultra-short-term multi-region power load forecasting model based on Spearman-GCN-GRU is proposed with focus on the spatial-temporal correlation analysis of multi-region power data.Firstly,the Spearman correlation coefficient is used to analyze the spatial-temporal correlation of power load in different regions and construct the Spearman adjacency matrix.And then,the graph convolutional network(GCN)and gated recurrent unit(GRU)are used to respectively extract the spatial and temporal features from the data.Finally,the multilayer perceptron(MLP)is used to decode and output the prediction results.Through comparison with the distance adjacency matrix-based models,the Spearman-GCN-GRU model is proved to be feasible.In terms of prediction accuracy,the Spearman-GCN-GRU model are optimal in common evaluation indexes compared with traditional statistical models and neural network models.Specifically,in terms of the root mean square error(RMSE),the Spearman-GCN-GRU model exhibits a respective decrease of 13.90%,11.66%,and 8.36%compared to the GRU,GCN and deep neural network(DNN)models,demonstrating its superior predictive performance.