Monthly electricity consumption prediction method comprehensively considering multiple economic factors

Abstract

The invention discloses a monthly power consumption prediction method that comprehensively considers various economic factors, adopts the X-12-ARIMA model to decompose the monthly power and various economic factors into seasons; uses stepwise regression analysis to study the economic quantities and power consumption The correlation degree and regression model of electricity consumption were used to obtain preliminary prediction results; the annual power consumption forecast was carried out by using polynomial fitting, and the existing monthly power consumption forecast results were adjusted; the autoregressive integral sliding average Seasonal forecast corrections are carried out in each month to obtain a monthly electricity forecast model with good accuracy. Using the above technical solution, after seasonal decomposition of monthly electricity and economic volume, not only can use periodicity for forecasting, but also can effectively reduce the impact of volatility on regression analysis fitting accuracy and forecasting accuracy, and obtain good forecasting results.

Description

technical field [0001] The invention belongs to the technical field of electricity market demand forecasting, and in particular relates to a monthly electricity consumption forecasting method that comprehensively considers various economic factors. Background technique [0002] With the continuous development of the economy, the demand for electricity in social production and life continues to increase, and electricity consumption has gradually become an important indicator of the level of social and economic development. At the same time, power industry reform policies such as the separation of power plants from power grids, grid bidding, and liberalization of electricity sales have prompted power companies to have higher requirements for their own economic benefits. For power supply enterprises and electricity retail enterprises, the quality and accuracy of monthly electricity forecasts have a very important impact on the reasonable arrangement of production, procurement a...

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Problems solved by technology

[0004] Most of the current power market demand forecasting focuses on the forecasting of power load. Considering that the load is affected by seasonal factors, mainstream research is based on meteorological indicators and uses various algorithms to carry out power load forecasting; and for the research on power consumption forecasting, Little consideration is given to seasonal factors, and little consideration is given to the volatility and periodicity between monthly electricity and economic volume, resulting in a large error between the forecasted results of electricity consumption and the actual electricity consumption

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