Electric power demand prediction method based on S-shaped model

Abstract

The present invention relates to a power demand prediction method based on an "S"-shaped model. According to the "S"-shaped physical model of per capita power consumption and per capita GDP, an "S"-shaped theoretical model based on per capita power consumption and per capita GDP is constructed, and then the Based on the "S"-shaped theoretical model of per capita electricity consumption and per capita GDP, with per capita GDP as the independent variable, using trend analysis and hyperbolic tangent mathematical methods, combined with historical data from different countries or regions, the power demand forecasting equation is established to achieve Forecast the medium and long-term electricity demand of the country or region. The power demand prediction method based on the "S" shape model provided by the present invention is guided by the basic laws of per capita power consumption, fundamentally solves the problem of large prediction deviations that were common in medium and long-term power demand forecasts in the past, and improves the medium and long-term power consumption. Demand forecast reliability and confidence. The invention also relates to a method for constructing a forecast equation and a method for forecasting power demand in various countries.

Description

technical field [0001] The present invention relates to a quantitative power demand forecasting method, in particular to a method based on the "S" shape theoretical model between per capita GDP and per capita power consumption, using trend analysis and hyperbolic tangent function mathematical methods to construct its forecasting equation and Forecasting methods based on forecasting equations. Background technique [0002] Electricity is the energy source powered by electrical energy, and is the most commonly used secondary energy source. Electricity has become the basis of economic growth and is widely used in production and living fields. Power demand forecasting is based on all relevant historical data of power consumption, economy, society, weather, etc., to explore the changing law of historical data of power demand, to seek the internal relationship between power demand and various related factors, so as to predict future power demand. scientific predictions. [0003...

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Problems solved by technology

The unit consumption method requires a lot of detailed statistical work, and it is difficult to accurately calculate the unit consumption of the product in actual work, and the workload is too large

(2) The power elasticity coefficient method is based on the growth rate of GDP combined with the power elasticity coefficient to obtain the total power consumption at the end of the planning period. In the forecasting process, the forecaster mainly relies on the forecaster to make judgmental forecasts based on his own and other people's experience and relevant statistical data. , the influence of human subjectivity is relatively large, so the accuracy of its prediction results is difficult to guarantee

However, this method does not include determining the relationship between secondary energy consumption and GDP, that is, it does not construct an "S"-shaped physical model, theoretical model, and mathematical model between per capita power consumption and per capita GDP.

Moreover, this method does not construct an "S"-shaped theoretical model between per capita energy consumption and per capita GDP, and the parameters of its mathematical model are completely inconsistent with those of the "S"-shaped mathematical model between per capita power consumption and per capita GDP. Forecast does not apply

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