基于优化的VMD-mRMR-LSTM模型的短期负荷预测  

Short-term load forecasting based on an optimized VMD-m RMR-LSTM model

作　　者：胡威 张新燕[1] 李振恩 李青[1] 王衡 HU Wei;ZHANG Xinyan;LI Zhen'en;LI Qing;WANG Heng(Xinjiang University,Urumqi 830047,China;Xinjiang Institute of Technology,Akesu 843000,China)

机构地区：[1]新疆大学,新疆乌鲁木齐830047 [2]新疆理工学院,新疆阿克苏843000

出　　处：《电力系统保护与控制》2022年第1期88-97,共10页Power System Protection and Control

基　　金：国家自然科学基金项目资助(51667018,52067020);新疆自然科学基金项目资助(2021D01C044)。

摘　　要：随着智能电网技术的发展和电力市场的推进,用电模式的复杂性逐渐凸显,对短期负荷预测的精度和稳定性提出了更高的要求。针对传统负荷预测方法缺少对时序数据相关性、特征值的全面考虑等问题,提出一种基于优化的变分模态分解、最小冗余最大相关性与长短期记忆神经网络的组合预测模型。首先,将波动性高的负荷序列分解为一组相对平稳的模态分量,其中利用麻雀智能算法优化VMD的关键参量。其次,利用m RMR方法分析各模态分量与预测模型输入特征元素间的相关性,获取各预测模型的最优输入特征集,并在分析负荷影响因子中考虑实时电价。最后,采用不同结构参数的LSTM方法对各分量分别预测,将预测结果叠加得到最终的预测值。以澳大利亚的实际运行数据做算例分析,与常规负荷预测方法进行对比,验证了该方法的有效性。With the development of smart grid technology and the advance of the power market, the complexity of power consumption patterns has gradually become prominent. This then makes higher demands on the accuracy and stability of short-term load forecasting. Given the lack of comprehensive consideration of time series data correlation and eigenvalues in traditional load forecasting methods, a combined forecasting model based on optimization of variational mode decomposition(OVMD), minimal redundancy maximal relevance(m RMR) and a long short-term memory neural network(LSTM) is proposed. First, the load sequence with high fluctuation is decomposed into a group of relatively stable modal components, in which the parameter of VMD is optimized by a sparrow intelligent algorithm. Secondly, the m RMR method is used to analyze the correlation between each modal component and the input feature set elements of the prediction model, obtain the optimal input feature set of each prediction model, and introduce the real-time electricity price into the load impact factor analysis. Finally, the LSTM method with different structural parameters is used to predict each component separately, and the predicted results are superimposed to obtain the final predicted value. An example is given to analyze the actual operational data of Australia, and compared with the conventional load forecasting method, the validity of the method is verified.

关 键 词：负荷预测 变分模态分解 最小冗余最大相关性 长短期记忆神经网络 实时电价 

分 类 号：TM715[电气工程—电力系统及自动化]