基于EMD-SLSTM的家庭短期负荷预测

针对非平稳的家庭短期负荷数据,直接套用预测模型难以挖掘出更深层次的时序特征。提出一种经验模式分解(Empirical Mode Decomposition, EMD)和堆栈式长短期记忆(Stack Long Short-term Memory, SLSTM)的组合算法应用于家庭短期负荷预测。首先分析了SLSTM和EMD原理,提出EMD-SLSTM组合预测模型。将负荷数据通过EMD算法进行分解,然后将分解后的分量数据分别转化为三维数据样本。通过设计SLSTM网络架构及其参数,对归一化的分量数据和原始数据分别进行预测建模及其重构。为显示算法预测性能,实验对比了支持向量回归、人工神经网络、深度神经网络、梯度提升回归等模型在两种情景下的性能,采用MAPE和RMSE性能度量进行验证。实验结果表明EMD-SLSTM更能有效地表达出家庭短期负荷的时序关系,具有更高的预测精度。

Short-term household load forecasting based on EMD-SLSTM

For non-stationary short-term household load data, it is difficult to mine deeper temporal characteristics by directly applying the prediction model. A combination of Empirical Mode Decomposition (EMD) and Stack Long Short-Term Memory (SLSTM) algorithm is proposed for short-term household load forecasting. Firstly, the principle of EMD and SLSTM is analyzed and the EMD-SLSTM combined prediction model is proposed. Then, the load data is decomposed by the EMD algorithm and the decomposed component data is respectively converted into three-dimensional data. By designing the network architecture of SLSTM and its parameters, the normalized component data and original data are separately predicted and reconstructed. In order to show the performance of the algorithm, the performance of the support vector regression, artificial neural network, deep neural network, gradient boosting regression is compared and verified by MAPE and RMSE performance metrics in two scenarios. The results show that EMD-SLSTM can more effectively express the time series relationship of short-term household load and has higher prediction accuracy. This work is supported by Youth Science Foundation (No. 51607179).