Distribution Network Line Loss Estimation Based On Deep Learning

As the demand for reliable and efficient power distribution continues to increase,the accurate prediction of line losses in power systems has become more crucial than ever.High line losses not only lead to significant economic losses but also endanger the power grid’s stability and safety.Traditional machine learning models have limitations in capturing the complex relationships between variables and often yield unsatisfactory results in predicting line losses.Therefore,this research proposes three deep learning models for line loss prediction:Long Short-Term Memory(LSTM),Encoder-Decoder-Attention LSTM(EDA-LSTM),and Hybrid Encoder-DecoderAttention LSTM MLP(HEDA-LSTM-MLP).The main goal of this study is to assess the effectiveness of these models for predicting line losses accurately.First,a LSTM model is constructed,and its performance is evaluated.Then,the proposed EDA-LSTM and HEDA-MLP models,which incorporate the attention mechanism and MLP layers,respectively,are developed and compared to the LSTM model.The results show that the attention mechanism can capture the relevant information in the input sequence effectively and enhance prediction accuracy.At the same time,the use of MLP layers enhance the model’s ability to handle complex nonlinear relationships and The MLP layers improve the model’s ability to handle complex nonlinear relationships and outperform the LSTM model.The contribution of this study is threefold:(1)it proposes and compares three deep learning models for line loss prediction,(2)it shows that,the accuracy can be improved by using the attention mechanism and MLP layers,and(3)it provides a useful reference for researchers and engineers in the power industry.The study’s findings may assist power system operators and planners to make decisions to reduce line losses and boost the electric grid’s overall efficiency and reliability.