Power demand forecast method based on vector autoregression model

Abstract

The invention relates to a power demand forecast method based on a vector autoregression model. Step one, the economic data and the power demand data of a region (country, province or city) to be forecasted are collected; step two, the data are compiled and cleaned and stored in a database; step three, the time period required to be forecasted and the minimum time interval of the data are definedand logarithmic processing is performed on the corresponding data; step four, stationarity testing is performed on the four columns of processed data (fixed asset investment, total retail sales of consumer goods, total net export trade and the power demand); step five, an equation based on the vector autoregression model is constructed; step six, the lag order of the model is determined by using the AIC Akaike information criterion and the SIC Schwarz criterion; and step seven, the vector autoregression model is corrected and the power demand is forecasted by using the vector autoregression model. The method has the characteristics of high generalization capability and high forecast accuracy.

Description

technical field [0001] The invention relates to a method for predicting electric power demand based on a vector autoregressive model, specifically carrying out electric demand forecasting through the vector autoregressive model in econometrics. Background technique [0002] Traditional With the increasing development of the power system, electricity has become the most important secondary energy in industrial production and residents' lives. As a commodity, electric energy has certain particularities compared with other commodities. Its biggest feature is that it cannot be stored, that is, the production, transportation, distribution and consumption of electric energy are completed at the same time. Therefore, the available power generation capacity in the power system should meet the demand of the load in the system under normal conditions. When the power generation capacity is insufficient, necessary measures should be taken, such as increasing generator sets, reducing lo...

AI Technical Summary

Problems solved by technology

These methods often use the historical data of electricity consumption to construct corresponding forecasting functions, and seldom describe and quantify other indicators that may affect electricity demand in the region, such as related economic indicators. At the same time, the analysis of the correlation between economy and electricity is not comprehensive enough. , leading to a lack of accuracy in the predictive model

Images