Research On Short-Term Load Forecasting Method Of Distribution Network Based On Deep Learning

With the national strategic goal of "carbon neutrality,emission peak" proposed,the power system will be involved in a new round of reform,industrial upgrading requires distribution network to achieve reasonable distribution of electricity and reduced energy consumption.Furthermore,a higher standard is proposed for load forecasting of distribution network.Accurate power load forecasting will provide great help for production,transmission and distribution of electric energy.Under the background that traditional prediction methods cannot meet the requirements of precision and intelligence of new distribution network,deep learning provide a new idea for load forecasting of distribution network.After summarizing the existing methods of short-term load forecasting,the characteristics of power consumption of distribution network users are analyzed.Combined with the deep learning technology which repeatedly climbing peaks,a method of short-term load forecasting of distribution network based on deep learning is proposed.The specific research contents are as follows:Firstly,aim at the characteristics of power consumption of distribution network users,SelfOrganizing Map(SOM)neural network is selected as the principal part of user load clustering model.The real load data collected in distribution network is used for simulation analysis to obtain user clustering results and clustering centers,which provides a basis for choosing the structure of prediction model.The correctness of SOM neural network is verified by the actual information of distribution network users and comparing with other clustering models.Secondly,a combination model of short-term load forecasting based on Bird Swarm Algorithm(BSA)is proposed.The prediction results of Deep Neural Network(DNN)and Long Short-Term Memory(LSTM)neural network are weighted and averaged by BSA,which provides the preliminary prediction results.Through example analysis,it is found that the user load preliminary prediction method based on the combined model of BSA proposed in this paper can effectively improve the prediction accuracy,compared with DNN,LSTM neural network and other single prediction models and Particle Swarm optimization-Elman(PSO-Elman)neural network,Wavelet Transform-Back Propagation(WT-BP)neural network and other combined models.This method has higher accuracy and stronger generalization ability,and is suitable for short-term load forecasting of distribution network.Finally,the short-term load forecasting compensation algorithm based on the average error of historic data of the same type of users is used to modify the preliminary prediction results.The simulation results of real data show that this combination model with structure of "Clustering-Forecasting-Compensation" is more outstanding in performance compared with a number of existing forecasting models.It can reduce the effects of different power characteristics to a certain extent,improve forecasting accuracy within a certain scope,and have the important value of engineering application.