Design And Implementation Of Rural Electricity Load Forecasting System Based On Hybrid Neural Network

A stable power supply is conducive to the process of rural construction and provides a strong guarantee for rural revitalization.Electricity load is an important reference content in the power supply plan,and at the same time,the level of electricity load reflects the construction effect of rural revitalization.In order to quantitatively analyze the rural development level and provide data support for the reasonable formulation of electricity consumption plan,this thesis takes the load data of various industries in Pingshan County,Shijiazhuang City as the starting point,and visualizes the electricity load data of various industries in Pingshan County from multiple angles by building a hybrid neural network model to deeply explore the hidden information behind the electricity load data.The specific research contents are as follows.(1)Firstly,the similarity,periodicity and continuity characteristics of the electric load data of each industry in Pingshan County are analyzed.Secondly,the raw data are smoothed according to the characteristics of the electric load data.Finally,the meteorological factors that have a greater impact on the electric load are screened out using gray correlation analysis and participate in the load prediction experiments to assist in improving the model prediction accuracy.(2)To address the problems of poor feature acquisition capability and low prediction accuracy of current power load forecasting models,a short-term power load model with SSA-optimized hybrid RNN is proposed.Firstly,the model uses CNN and Bi LSTM to build a dual-channel structure to acquire local feature information and global temporal feature information of power load data respectively.Secondly,the LSTM with Attention is used to learn the hidden load transformation law in the feature information.Finally,the sparrow search algorithm(SSA)is used to select the hyperparameters of the prediction model adaptively,so as to improve the load prediction of the model Accuracy.(3)According to the demand analysis of power system in Pingshan County,the rural power load forecasting system based on hybrid neural network is built.Firstly,the demand analysis and functional module designed for the system.Secondly,six system functional modules of user management,data management,data pre-processing,load prediction,power index and visualization analysis are designed and implemented.Among them,the load prediction module is built based on the SSA optimized hybrid RNN power load prediction model built by Tensor Flow2.0 framework.Finally,the graphical display of load forecast results for each industry and the trend of electricity and power index changes will provide data support for reasonable analysis of the development of rural revitalization in Pingshan County.In summary,the SSA-optimized hybrid RNN short-term electric load forecasting model proposed in this thesis can capture the hidden feature information in the electric load data more comprehensively,and therefore has a high accuracy of load forecasting.the built electric load forecasting system can accurately forecast the load data of multiple industries in Pingshan County,and at the same time,display the load data of each industry in the form of multiple types of graphs and charts,reflecting the construction effect of rural revitalization from multiple perspectives,and providing scientific and effective data reference for making power supply plan and rural revitalization decision analysis.