Power load probabilistic prediction method based on chaotic population algorithm and Bayesian network

Abstract

The invention discloses a power load probability prediction method based on a chaotic population algorithm and a Bayesian network, and the method comprises the steps: 1, obtaining the actual data of an air temperature, relative humidity, wind power and power load time sequence, carrying out the preprocessing of each column of data, and dividing a training set and test set data; 2, performing wavelet threshold de-noising processing on the original data of the power load, and restoring real information of a time sequence of the power load; 3, constructing a Bayesian network model to obtain an initial prediction interval; 4, calculating an interval change amplitude range, and obtaining an optimal interval change amplitude by applying a chaotic population algorithm; 5, chaotic search is adopted in the neighborhood of the optimal interval change amplitude, and a final prediction interval is obtained. The uncertainty of the power load can be measured by constructing the prediction interval,so that an effective reference can be provided for the optimized operation of the power system.

Description

technical field [0001] The invention relates to the field of power consumption, and mainly relates to a power load forecasting model method based on wavelet threshold denoising, Bayesian network and chaotic crowd search algorithm. Background technique [0002] In recent years, the growth of consumption and the continuous improvement of production capacity have led to an increasing demand for electricity. Among them, the ever-increasing information flow and data flow are important components of the power system. By analyzing the characteristic data and power data, the online power load prediction is helpful to the stable operation of the power grid system and the status evaluation of hardware equipment. At the same time, due to the high proportion of electric energy in the use of various energy sources, the management and scheduling of electric energy has become extremely important. By making accurate and reliable predictions of power loads, power consumption can be saved to...

AI Technical Summary

Problems solved by technology

However, due to the characteristics of the load data itself and the inherent defects of the model, the traditional deterministic point forecasting method has problems such as point forecasting errors that cannot be eliminated, uncertainty in the results cannot be measured, and the fluctuation range of the power load cannot be given.

[0005] In the current methods of probabilistic forecasting of electric load, most of the models are optimized by evolutionary algorithms. However, although the traditional evolutionary algorithms have a strong global search ability and can effectively improve the convergence speed of the model, they generally have a slow convergence speed in the later stage. The model is prone to prematurity, and it is easy to fall into the defect of local optimal solution

In power load forecasting, the algorithm will run for a long time and fail to meet the time availability requirements; the prediction accuracy is not high and cannot meet the accuracy and availability requirements

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