基于双向长短时记忆网络的母线负荷分解方法

The bus load decomposition method based on bi-directional long short-term memory model

目前在负荷分解领域的研究多以家庭住宅的总负荷分解为电器级别的负荷为主,对于中高电压等级的母线负荷分解研究较少,为解决这一问题,提出基于双向长短时记忆网络(Bi-LSTM)的中高电压等级母线负荷分解算法。首先在长短时记忆(LSTM)的基础上构建了Bi-LSTM;其次以母线负荷和其对应的外部信息源(如日期类型、天气等)作为Bi-LSTM的输入量,母线负荷的各下属建筑负荷作为输出量,对Bi-LSTM进行训练;最后以网络分解的母线负荷构成值与实际值间的平均相对误差作为评价指标。实验结果表明该方法可有效对构成成分未知的母线负荷进行分解。

The current research work is mainly based on the decomposition of the total load of the family house into the electrical level load,and less research on the bus load of the high voltage level.To solve this problem,ia bus load composition decomposition algorithm based on bi-directional long short-term memory(Bi-LSTM)is proposed.Firstly,Bi-LSTM is constructed on the basis of LSTM.Secondly,bus load and its corresponding external information sources(such as date type,weather,et al)are used as input of Bi-LSTM after training.Finally,taking the mean relative error between the predicted value and the target value of the building power load decomposed by the network as the evaluation index.The experimental results show that the method can effectively identify the bus load with unknown components.Compared with the traditional recurrent neural network and long-term and short-term memory network,the proposed algorithm has better identification ability.