LSSVM annual electricity consumption prediction method based on ant lion optimization

Abstract

The present invention relates to a kind of LSSVM annual power consumption prediction method based on antlion optimization, the annual power consumption prediction method comprises the following steps: determine the input variable of least square support vector machine (Least Square Support Vector Machines, LSSVM) prediction model; Initialize the antlion optimization algorithm; calculate the fitness value of the initial antlion to obtain the initial elite antlion; update the position of the ant, calculate the fitness value of the current antlion, and compare it with the fitness value of the corresponding antlion to determine whether to update the antlion Lion position; compare the fitness value of the antlion after the updated position with the fitness value of the previous elite antlion one by one, keep the antlion corresponding to the smaller fitness value, and obtain the elite antlion of this iteration; judge whether The maximum number of iterations is reached, if yes, then output the location of the elite antlion and the corresponding predicted value of annual electricity consumption, if no, continue to iterate. Compared with the prior art, the present invention has the advantages of higher prediction accuracy and higher prediction efficiency.

Description

technical field [0001] The invention relates to the technical field of annual power consumption forecasting, in particular to an LSSVM annual power consumption forecasting method based on antlion optimization. Background technique [0002] Annual electricity consumption forecast is very important to the planning, operation and maintenance of the power system, and it can also reflect the economic development of a country or region to a certain extent. Accurate annual electricity consumption forecasts can provide valuable references for power system operators and economic managers. Therefore, forecasting the power load is one of the most important basic tasks in the power system, and it is of great significance to energy planning, power system operation and control, and economic development strategy research. Forecasting methods include: regression analysis, analogy, elastic coefficient method, time series method, neural network method, etc. [0003] In the modern economy an...

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Problems solved by technology

[0003] In the modern economy and society, there is an extremely close relationship between electricity consumption and the economy, society, population and ecological environment, that is, the electricity consumption system is a complex system affected by many factors, and conventional mathematical methods are used to establish predictions. model, not only the workload is heavy, but also the prediction accuracy is difficult to guarantee

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