基于改进PSO优化LSTM网络的典型用电负荷模式识别

Typical power load mode recognition based on IPSO optimization and LSTM network

新能源发电的推广和使用加剧了用电高峰期电网供需矛盾,对电力用户的负荷模式进行识别可以为负荷参与调峰决策提供支持。为提高用电负荷模式辨识准确率,提出一种基于改进粒子群(IPSO)算法优化长短期记忆(LSTM)神经网络的用电负荷模式识别模型。通过引入多样化初始参数、动态非线性权重和淘汰机制等措施,改善了粒子群算法的寻优能力,实现对LSTM的关键参数寻优,确定LSTM神经网络的最优参数组合。实验结果表明,该方法可以有效提高模型的准确率,同时节省模型的训练时间。

Promotion and use of new energy power generation has aggravated the contradiction between supply and demand of power grid during peak hours.Identification of load patterns of power users can provide support for load participation in peak regulation decisions.In order to improve the accuracy of power load pattern recognition,a power load pattern recognition model based on improved particle swarm optimization(IPSO)algorithm to optimize long short-term memory(LSTM)neural network is proposed.By introducing diversified initial pa⁃rameters,dynamic nonlinear weights and elimination mechanism,the optimization ability of PSO algorithm is improved,the key parame⁃ters of LSTM are optimized,and optimal parameter combination of LSTM neural network is determined.Experimental results show that this method can effectively improve the accuracy of the model and save the training time of the model.