Urban electricity consumption prediction method based on GM(1,1) model and grey Verhulst model

Abstract

The invention relates to a urban electricity consumption prediction method based on a GM(1,1) model and a grey Verhulst model. The method includes the following steps: firstly performing cos(x) conversion on historical electricity consumption data to enhance the smoothness of a data sequence so as to increase the prediction precision of the GM(1,1); then inputting the data which is obtained by the cos(x) conversion to the GM(1,1) model, obtaining a predicted electricity consumption data sequence; and eventually, inspecting the precision of the electricity consumption data sequence, if the a prediction value does not exceed the precision requirement, outputting the electricity consumption data sequence; if the prediction value exceeds the precision requirement, in combination with the characteristics of the changes of a residual sequence and the advantages of the grey Verhulst model, establishing a grey Verhulst model, performing residual correction on the residual sequence of low or lower precision, and until the prediction value of the electricity consumption meets the precision requirement, outputting the corrected electricity consumption data sequence. Compared with prior art, according to the invention, the method is advantaged by being capable of better predicting urban annual electricity consumption and higher prediction precision.

Description

technical field [0001] The invention relates to a method for predicting urban electricity consumption, in particular to a method for predicting urban electricity consumption based on a GM (1,1) model and a gray Verhulst model. Background technique [0002] Urban electricity consumption forecasting is one of the most important basic tasks in the power system. It is of great significance to energy planning, power system operation and control, and economic development strategy research. Experts and scholars at home and abroad have done a lot of theoretical and methodological research work on this, and have proposed a variety of electricity consumption forecasting methods, such as: time series method, elastic coefficient method, artificial neural network method, etc. [0003] The consumption of electricity consumption is affected by many factors such as economic development, industrial structure, residents' income level, climate, and national policies. The research is suitable ...

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Problems solved by technology

[0004] However, since the GM(1,1) model is an exponential function, it is more suitable for the situation where the power consumption increases slowly, but the actual power consumption is difficult to change strictly according to the exponential law, resulting in low prediction accuracy

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