基于EEMD-AE-LSTM的生活用电短期负荷预测

SHORT-TERM LOAD FORECASTING OF DOMESTIC ELECTRICITY BASED ON EEMD-AE-LSTM

生活用电负荷随机性高,使用单一的预测模型进行预测会造成预测结果精度不高并且预测时间比较长。建立集合经验模态分解(EEMD)-自动编码器(AE)-长短期记忆网络(LSTM)的组合预测模型用来预测生活用电短期负荷。采用EEMD算法将负荷数据分解为有限个本征模态分量(IMF)和一个残差分量,与自动编码器训练得到的特征序列组合,并建立LSTM模型预测线性加权产生最终预测结果。实验结果表明,相对于其他模型,EEMD-AE-LSTM模型的预测精度更高,是一种较为有效的生活用电短期负荷预测方法。

The domestic power load has high randomness,and using a single forecasting model to forecast will result in low accuracy and long prediction time.A combined forecasting model of ensemble empirical mode decomposition(EEMD)-Automatic encoder(AE)-long short-term memory network(LSTM)is established for short-term load forecasting of domestic power consumption.The EEMD algorithm was used to decompose the load data into a finite number of intrinsic mode components(IMF)and a residual component,and then combined with the feature sequences trained by the automatic encoder,and the LSTM model was established to predict the linear weighting to generate the final prediction results.The experimental results show that EEMD-AE-LSTM model is more accurate than other models,and it is an effective short-term load forecasting method for domestic power.