作者:王 清，陈祉如，李贵民，荆 臻，张 志，王平欣，崔 琦
Authors:WANG Qing,CHEN Zhiru,LI Guimin,JING Zhen,ZHANG Zhi,WANG Pingxin,CUI Qi摘要:针对台区短期负荷预测精度不高的问题 ,提出了一种基于变分模态分解(variational mode decomposi- tion ,VMD) 的时间卷积网络(temporal convolutional network,TCN)短期负荷预测算法 。其利用 VMD 对负荷数据进行分解 ,得到规律性更强的子序列 ,并采用最大信息系数(maximal information coefficient,MIC)选出与负荷相关性强的 天气因素 ,与历史负荷和分解的子序列形成新的负荷数据集 ,采用 TCN 模型完成低压台区短期负荷预测 。并对 TCN、LSTM、GRU 预测算法进行对比分析 。仿真结果表明 ,VMD-TCN 的预测效果最好 ,MAPE 和 RMSE 分别为 1. 65% ,15. 05kW,表明了采用该算法可以实现对台区负荷进行精准的短期预测,以便于台区的调度管理、优化运行以及节能减排,同时采用了另一种数据集对算法进行了验证 ,结果表明 VMD-TCN 的预测结果仍是最好的。
Abstract:Aiming at the low accuracy of short-term load forecasting in substation area, a temporal convolutional network short-term load forecasting algorithm based on variational mode decomposition is proposed in this paper. It uses VMD to decompose load data to get a more regular subsequence, and uses the maximum information coefficient to select weather factors strongly correlated with load, and forms a new load data set with the historical load and the subsequence of decomposition, using TCN model to complete short-term load forecasting in low-voltage substation areas. The prediction algorithms of TCN, LSTM and GRU are compared and analyzed. The simulation results show that the forecasting effect of VMD-TCN is the best, MAPE and RMSE are 1. 65% and 15. 05kW, respectively, indicating that the algorithm can be used to achieve accurate short-term forecasting of the station load, so as to facilitate the dispatch management, optimization operation, energy saving and emission reduction of the station. At the same time, another dataset was used to validate the algorithm, and the results showed that the forecasting results of VMD-TCN were still the best.

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