Short-term wind speed prediction method and system based on improved whale algorithm optimized ELM

Abstract

The invention discloses a short-term wind speed prediction method and system based on an improved whale algorithm optimized ELM. The method comprises the steps: (1) obtaining the time series of various historical meteorological data of a wind power plant within a preset time range, and carrying out the preprocessing of the data; (2) analyzing the influence of each collected meteorological factor on the wind speed, calculating the weight of the characteristic quantity through the correlation degree obtained by grey correlation analysis, and taking the characteristic quantity with high correlation degree as input; (3) determining a network structure of the extreme learning machine and selecting an activation function; (4) adding chaos initialization and hill-climbing local search into the basic whale optimization algorithm, and adding inertia weight for improvement; and (5) establishing an extreme learning machine algorithm model based on improved whale algorithm optimization. According to the method, the technical problem that the wind driven generator cannot generate power according to the ideal wind power curve due to the uncertainty of the wind speed is solved, so that the technical effect of improving the short-term wind speed accurate prediction precision is achieved, and the utilization of wind energy resources by a wind power plant is improved.

Description

technical field [0001] The invention belongs to the field of wind power prediction, in particular to a short-term wind speed prediction method and system based on an improved whale algorithm to optimize ELM. Background technique [0002] With the rapid consumption of fossil energy, human beings are facing the double crisis of energy depletion and environmental degradation. Therefore, in recent years, clean and renewable energy has received extensive attention and development worldwide. Use of renewable energy. Wind energy is the main part of clean and renewable energy, and improving the utilization rate of wind energy is of great significance to the current society. [0003] However, wind energy has great randomness and volatility, and the ability to accurately predict wind speed is very important for the development of the wind power industry. Traditional wind speed prediction methods mainly include physical methods and statistical methods. The physical method is to comb...

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Problems solved by technology

[0004] Purpose of the invention: The present invention provides a short-term wind speed prediction method and system based on the improved whale algorithm to optimize ELM, which solves the technical problem that the wind generator cannot generate electricity according to the ideal wind power curve due to the uncertainty of stroke in the prior art, and achieves The technical effect of improving the accuracy of short-term wind speed prediction

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