Long-term power load prediction method based on residual correction grey prediction model

Abstract

The invention discloses a long-term power load prediction method based on a residual correction grey prediction model, which is applied to the technical field of power system load prediction, and aimsto solve the problems of enhanced volatility along with load change and poor fitting and prediction effects of an existing grey prediction model. Under the condition that the power load sequence of the previous n years is acquired, the power load value of the (n + 1) th year is predicted by using a linear time-varying parameter discrete grey TDGM (1, 1) model, then an original prediction model iscorrected by using a Fourier series residual correction method, and finally, a corrected analog value and a corrected predicted value are obtained; the corrected TDGM (1, 1) model has higher fittingand prediction precision, and the adaptability and flexibility of the grey prediction model are improved.

Description

technical field

[0001] The invention belongs to the field of power system load forecasting, in particular to a long-term power load forecasting technology. Background technique

[0002] Long-term power load forecasting is the basis of power grid planning. Accurate load forecasting plays an extremely important role in making new plans for power plants, determining the size of installed capacity, and ensuring the safety and stable operation of the power grid. Generally speaking, the change of long-term power load has an increasing trend year by year, and the gray forecasting model can better fit the long-term power consumption in exponential form, so the gray forecasting method is an effective method for predicting long-term power load; but the long-term power The change of load also has a certain degree of randomness and volatility, and the electricity does not increase year by year according to the absolute exponential law. If the gray forecasting model is not corrected and ...

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