Neural network short-term power load prediction method based on squirrel weed hybrid algorithm

Abstract

The invention provides a neural network short-term power load prediction method based on a squirrel and weed hybrid algorithm. The method comprises: forming a sample data set by historical power loads, meteorological factors and date types before the day to be predicted, conducting principal component analysis on meteorological factor data through SPSS software factor analysis, and extracting principal components to replace original meteorological factor variables to form a new sample data set; taking the normalized historical power load data as an output sample, and taking meteorological factors and date types as input samples; optimizing the weight and threshold of the BP neural network by applying a squirrel and weed hybrid algorithm to construct an SSIWO-BP neural network prediction model; and inputting the date type to be predicted and the meteorological factor data into the SSIWO-BP neural network prediction model to predict the power load value. According to the method, the global convergence of the squirrel weed hybrid algorithm and the stability in a high-dimensional space are considered, BP neural network parameters are optimized, the generalization ability of the neuralnetwork is enhanced, and the prediction precision of the model is improved.

Description

technical field [0001] The invention relates to the technical field of short-term power load forecasting, in particular to a neural network short-term power load forecasting method based on a squirrel-weed hybrid algorithm. Background technique [0002] In the power system, short-term power load forecasting is an important means for the safe operation of the power grid and the saving of operating costs. With the continuous improvement of the economic development level of modern society, the power load has also been showing a growing trend. However, there are many factors that affect the power load, such as economic factors, meteorological factors, date type factors, geographical factors, seasonal factors and so on. The biggest problem in power load forecasting is the establishment of a forecasting model. Power load forecasting is actually modeling through existing historical data and other data that affect power load factors, and regression fits a mapping between input and ...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the problems of slow convergence speed and low precision of prediction samples in the case of high dimensions, this paper proposes an improved squirrel algorithm to optimize network parameters. This optimization algorithm has a good effect in solving high-dimensional optimization problems. A large number of high-dimensional sample data problems, using the improved squirrel algorithm to optimize the neural network to achieve good accuracy in predicting power load

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