基于GA-BiLSTM网络的建筑负荷分解方法研究

Research on Building Load Disaggregation Method Based on GA-BiLSTM Network

提出一种基于遗传算法和双向长短时记忆网络的非侵入式负荷分解方法,通过卷积神经网络对总能耗波形进行特征提取,借助双向长短时记忆网络的双向传播特性对能耗波形特征进行训练,引入遗传算法对网络的超参数进行优化,以提高网络性能。利用UK-DALE数据集对所提方法进行对比验证,证明了本文方法对建筑分项能耗预测的有效性和准确性,以及在波动能耗跟踪上的优良性能。

A non-intrusiveload disaggregation method based on genetic algorithm and bidirectional longshort-termnmemory network is proposed.The feature extraction of total energy consumption waveform is carried out by use of convolutional neural network,and the energy consumption waveform featuresare trainedaccording to the bidirectional propagation characteristics of bidirectional long short-term memory(LSTM)network;genetic algorithm is introduced to optimize the hyperparameters of the network,so as to improve network performance.The comparison and validation are carried out for the proposed method by use of the UK-DALE dataset,and thus the effectiveness and accuracy of the proposed method in predicting the energy consumptionofbuilding sub-items,as well as their excellent performancein tracking fluctuating energy consumption are demonstrated.