Temperature index construction method for monthly power consumption prediction model

Abstract

The invention discloses a temperature index construction method for a monthly power consumption prediction model. The method comprises the following steps of setting an air temperature threshold for distinguishing a power consumption load starting range, wherein a high-temperature threshold interval is [T<H>-t, T<H>+t], a high-temperature threshold is upt, a low-temperature threshold interval is [T<L>-t, T<L>+t], and a low-temperature threshold is dpt; performing grid searching in the high-temperature threshold interval and the low-temperature threshold interval according to a determined step length in order to determine all grid points, wherein the grid points are taken as a primary point set; obtaining a high-temperature index of an ith month according to the determined high-temperature threshold interval and all threshold grid points in the high-temperature interval, and obtaining a low-temperature index through similar processing; performing weighted synthesis according to weights of sub-regions within a whole region to obtain a whole-region air temperature index; and using all the high-temperature / low-temperature indexes formed according to the grid points as input data of the power consumption prediction model, predicting power consumption according to the power consumption prediction model, and obtaining a prediction error of the model. According to the method, a comprehensive index reflecting a whole-region air temperature is constructed, and a step-like influence of power consumption, an accumulative effect caused by high and low temperatures, and a regional difference reflecting an air temperature influence are depicted, so that the prediction ability of the model is enhanced.

Description

technical field [0001] The invention relates to the field of electricity consumption prediction, in particular to a method for constructing a temperature index for a monthly electricity consumption prediction model. Background technique [0002] Existing electricity forecasting models include GM gray forecasting model, ARIMA model, regression model and so on. [0003] 1. GM gray prediction model [0004] Based on the trend of the electricity consumption data itself, the gray system is generated through the accumulation of actual data, and after obtaining a curve with strong regularity, the exponential curve is used to fit the generated model, and then the data obtained by using the generated model is obtained through cumulative and inverse operations - cumulative and subtractive generation The reduction model, the reduction model is used as the prediction model. [0005] 2. Electrical elasticity coefficient [0006] A coefficient calculation method, which calculates the e...

AI Technical Summary

Problems solved by technology

Existing temperature data and forecasting methods cannot capture this cumulative feature

Finally, the impact of temperature on electricity consumption also varies with the degree of regional economic and social development. Direct use of temperature data cannot describe regional differences in climate and electricity consumption.

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