Temperature/ economic growth factor considered monthly total electricity consumption predication method

Abstract

The invention discloses a temperature / economic growth factor considered monthly total electricity consumption predication method. The method comprises the steps of creating a linear regression technique for the monthly total electricity consumption, the current average temperature and the economic growth index; predicating monthly total electricity consumption, including historic total electricity consumption of the same month as the predicated month and monthly average temperature data; expressing the economic growth factor through the time trend term; building a time trend term included predication model; acquiring the historic above-scale industrial added value growth rate data of the same month as the predicated month and obtaining the economic growth index; building an economic growth index included predication model; calculating the total electricity consumption of the predicated month by two equation methods of the predicated models. The method has the advantages that the temperature / economic growth factor considered monthly total electricity consumption predication models are utilized to obtain the predicated monthly total electricity consumption, which is beneficial for power planning and the preparation of production schedule and monthly production planning of a power grid.

Description

technical field [0001] The invention relates to the technical field of forecasting and analyzing methods for power consumption demand of power grids, in particular to two monthly unified power consumption forecasting methods considering temperature and economic growth factors. Background technique [0002] Power consumption forecasting is an important daily work of relevant departments of the power system. Power demand forecasting is of great significance to the work of the power sector and related economic and energy departments. Unified power consumption is one of the important indicators of power demand forecasting. Accurate forecasting of power consumption It is helpful to arrange power production plan, and can also provide a basis for power grid planning and design. [0003] At present, within the power industry, the commonly used forecasting methods for unified electricity consumption mainly include trend extrapolation, total regression, ARIMA, growth rate extrapol...

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Problems solved by technology

[0008] Combining the above practical schemes, when we use the trend extrapolation method, linear regression method and gray model method to predict electricity consumption for a certain period of time, we conduct a comparative analysis of the three prediction methods, and the results can be concluded that different prediction methods have different prediction accuracy , where the accuracy of the gray model forecasting method is relatively high, and most of the above-mentioned studies are based on annual data for annual forecasting. Among them, the trend extrapolation method, ARIMA method, gray forecasting method and growth rate extrapolation method are all based on past and present developments. A class of methods for inferring trends in the future can only reflect the changing trend of the monthly unified electricity consumption itself, and cannot reflect and predict the impact of economic development on it. The linear regression method only considers the impact of yearly changes on electricity consumption, so all Unable to accurately predict future monthly unified electricity consumption

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