Short-term electric load prediction method based on improved genetic algorithm for optimizing extreme learning machine

Abstract

The invention discloses a short-term electric load prediction method based on improved genetic algorithm for optimizing extreme learning machine. A hill climbing method is used to perform preferentialselection again in the progeny population, an initial individual is selected, another individual in a close area is select, their fitness values are compared, and one individual which has good fitness values is leaved. If the initial individual is replaced or a better individual cannot be found in several iterations, iteration is stopped, the search direction of the genetic algorithm through thehill climbing method is optimized, obtaining an optimal weight value and a threshold value, a network optimization prediction model are obtained, a network optimization prediction model is obtained, the network optimization prediction model and prediction results of BP network and the extreme learning machine are comparative analyzed, including selection of input and output of the prediction network model, algorithm of improved genetic algorithm for optimizing extreme learning machine, and analysis of prediction results. The short-term electric load prediction method based on improved geneticalgorithm for optimizing extreme learning machine has faster training speed and more accurate prediction results, and is suitable for modern short-term electric load prediction with plenty of influence factors and huge data volume.

Description

Technical field: [0001] The invention relates to a short-term power load forecasting method based on an improved genetic algorithm to optimize an extreme learning machine, which is an important content in the economic dispatch of a power system and an important module of an energy management system (EMS). Background technique: [0002] The traditional extreme learning machine (Extreme Learning Machine, ELM) randomly generates input layer weights and hidden layer thresholds, which makes the network unstable and prone to overfitting, which affects the generalization performance of the network. Traditional Genetic Algorithm (GA) has limited space search capability, and it is easy to converge to a local optimal solution, resulting in premature problems. Invention content: [0003] The technical problem to be solved by the present invention is to provide a short-term power load forecasting method based on the improved genetic algorithm to optimize the extreme learning machine f...

AI Technical Summary

Problems solved by technology

[0003] The technical problem to be solved by the present invention is to provide a short-term power load forecasting method based on the improved genetic algorithm to optimize the extreme learning machine for the problem that the input layer weights and hidden layer thresholds randomly generated by ELM lead to network instability. The improved genetic algorithm, that is, the improved genetic algorithm (IGA) optimizes the weights and thresholds of the ELM, thereby improving network performance and improving the accuracy of network short-term load prediction; applying the improved genetic algorithm to optimize the extreme learning machine (IGA-ELM) for short-term load Forecasting can speed up the learning speed of the forecasting network, enhance the stability of the forecasting network, and improve the prediction accuracy of the forecasting network

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