基于VMD-LSTM-LightGBM的多特征短期电力负荷预测

Multi-Featured Short-Term Power Load Forecasting Based on VMD-LSTM-LightGBM

针对目前多特征电力负荷预测精度不准的问题,为充分挖掘电力负荷数据中的时序信息、天气信息等特征信息,提出了一种基于变分模态分解(variational mode decomposition,VMD)的长短期记忆(long short-term memory,LSTM)神经网络与轻量级梯度提升机(light gradient boosting machine,LightGBM)预测模型,优化负荷数据非线性、非平稳、长记忆等问题,解决了多特征预测提取特征信息差的问题。该方法首先用VMD分解代表不同尺度的特征模态分量,降低了原始序列的不平稳度,同时分解的残差量代表负荷数据强非线性部分,通过特征性强的算法进行预测,将各模态分量通过LSTM的单特征预测,再将各个分量加入多特征利用LightGBM进行负荷预测。通过与目前多特征电力负荷预测模型进行对比实验,平均绝对误差(mean absolute error,MAE)值仅为其23%~73%,平均绝对百分比误差(mean absolute percentage error,MAPE)值能达到0.37%,具有更好的预测精度。

Arming at the current inaccurate problem of multi-feature power load prediction accuracy,in order to fully exploit the feature information such as time-series information and weather information in power load data,a variational mode decomposition(VMD)-long short-term memory(LSTM)neural network-light gradient boosting machine(LightGBM)based prediction model is proposed to optimize the problems of non-linearity,non-stationary and long memory of load data,and solve the problem of poor extraction of feature information for multi-feature prediction.The method first decomposes the characteristic mode components representing different scales with VMD,which reduces instability of the original sequence,while the residual amount of the decomposition represents the strongly nonlinear part of the load data,which is predicted by the characteristic strong algorithm.Each modal component is predicted by the single feature of LSTM,and then each component is added to the multi-feature using LightGBM for load prediction.Experimental comparison with current multi-characteristic power load forecasting models through example analysis is conducted,the proposed method MAE value is only 23%~73%,and MAPE value can reach 0.37%,with better predic⁃tion accuracy.