Research And Forecasting Of Electricity Market In China On Linear Regression Model Based On The Development Of The Power Transmission And Distribution

Electric power market is not only the carrier of a relationship of commodityexchange, but also an important component of the socialist market economy, thenational economy is an important guarantee for the smooth development. Research onthe relationship between supply electricity markets have also become a focus ofcurrent research direction and target countries, the price and the use of transmissionand distribution of electricity transmission and distribution has become a major issueof people’s livelihood.This paper contrast and analysis the current situation in the field of developmentof domestic electricity market, electricity transmission and distribution selected forthe study focused on China, describes the development process of China’s pricemanagement system, a comprehensive introduction to the development of internal andexternal factors to consider price. Then introduces two kinds of transmission anddistribution price control mechanism, emphasized the significance of theimplementation of the pricing ladder. Secondly, according to the characteristics of thepower load forecast and the basic theory of the more popular time series, gray neuralnetwork model for prediction and forecasting techniques to predict the threetechniques for system analysis and research. Finally, the paper proved a integralpredict process about transmission and distribution.Due to the development of transmission and distribution have some key sectorsof the national economy with a linear relationship, so this is an entry point to a linearregression model using linear regression and multiple linear regression correlationstudy of the electric power industry. By linear regression model, Hebei Province, anarea for research and forecasting electricity consumption and usage, to provide thenecessary reference for the enterprise’s economic choices. This paper aims to verifythe validity of case studies and feasibility of this method in the field of electricitytransmission and distribution prediction.