A Self-Attention-based power data energy consumption prediction algorithm Dual-channel LSTM
Electricity energy demand increases volatility with unpredictable events.In order to achieve ac-curate prediction of building energy consumption,this paper proposes a Dual-channel LSTM for power data energy consumption prediction algorithm.The algorithm framework is mainly divided into three modules.Firstly,the power data with missing values in the time series is preprocessed by the T-LSTM module,so that the time series can be arranged regularly.The Dual-channel attention feature module is then input,which introduces a self-attention mechanism to guide the weight assignment.Finally,the prediction module combined with LSTM and FCN outputs the result.The algorithm is evaluated on the AEP and IHEPC public power datasets.The loss function MAE/MSE of Dual-channel is only 0.12/0.06 and 0.06/0.04 respec-tively.Through ablation experiments,it is found that the introduction of T-LSTM has a decisive impact on the performance of the algorithm,and the introduction of self-attention mechanism plays an auxiliary role in the algorithm.The proposed method improves the prediction accuracy and robustness.
power supplyenergy consumption predictionattentionLSTMtime series data
NETL
NSTL
万方数据
