Weather index and weighted LS-SVM-based power system short-term load prediction method

Abstract

The invention discloses a weather index and weighted LS-SVM-based power system short-term load prediction method. The method comprises the following steps of S1, obtaining a sample of original data; S2, according to the original data, calculating a comprehensive weather index; S3, performing data preprocessing on date type data and the comprehensive weather index; S4, according to an obtained dimensionless load characteristic quantity, performing gray correlation analysis between the dimensionless load characteristic quantity and a power system load, and calculating a characteristic quantity weight through a correlation degree obtained by the gray correlation analysis; and S5, building a weather index and weighted LS-SVM-based power system short-term load prediction model, performing parameter optimization by adopting a fruit fry optimization algorithm, and obtaining power system load prediction data of a to-be-predicted day through model output. The method has very good global optimization performance and few adjustment parameters, difficultly falls into local minimum and can effectively improve the power system short-term load prediction precision.

Description

technical field [0001] The invention relates to the field of power system engineering, in particular to a short-term load forecasting method of a power system based on a meteorological index-based weighted LS-SVM. Background technique [0002] With the deepening of my country's new power industry system reform, power-related enterprises have gradually entered the market, and the short-term load forecasting of the power system is of great significance to the regulation of the power grid system and the operation of the power market. However, the power load is affected by many factors such as temperature, historical load, and power consumption date, and has strong uncertainty and nonlinear characteristics, which increases the difficulty of load forecasting and results in low accuracy of short-term load forecasting results. At present, short-term load forecasting methods mainly include regression analysis, time series method, artificial neural network method, etc. The advantages...

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Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a short-term load forecasting method of power system based on met

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