基于二次EEMD的工业电能需量多步预测

电力大用户最大需量控制是降低电网峰值负荷、节约用户电费成本的重要技术手段.面向强波动性和冲击性工业电能需量控制,研究了超短期需量负荷的多步预测问题.基于集成经验模态分解(EE-MD)方法,通过二次分解有效分离时间序列中不同频率的信号,采用长短期记忆网络(LSTM)对各信号子序列进行独立预测,最后组合预测结果.实验结果表明,本方法能很好的预测工业需量负荷变化,M A PE/MAE/NRMSE精度指标基本控制在2％ 以内,明显优于多种现行主流时序预测模型和最新文献方法,且消除了多步预测的传递误差,预测模型精度和稳定性满足需量控制要求.

Multi-step Forecasting of Industrial Electrical Power Demand Based on Twice Ensemble Empirical Mode Decomposition

The maximum demand control of large power users is an important technical means to reduce the peak load of power grid and save the cost of power users. Aiming to control the industrial power demand characterized by strong fluctuation and impact, this paper studies the multi-step forecasting problem of ultra-short term demand load. Based on the integrated empirical mode decomposition method, the signals with different frequencies are effectively separated by twice decomposition. Then, the long short memory neural network is used to independently predict different signal subsequences, and finally the subsequence prediction results are combined. The experimental results show that the proposed method can well predict the industrial demand load, and the indices of prediction accuracy, such as MAPE, MAE, and NRMSE, are all controlled within 2%, and are significantly better than several classical time series prediction model, as well as the latest literature algorithms. The transfer error is also eliminated in the method, which represents good prediction accuracy and stability to meet the demand of demand control.