Probabilistic load prediction system and method based on Gaussian process quantile regression model

Abstract

The invention discloses a probabilistic load prediction system and method based on a Gaussian process quantile regression model. The method comprises the following steps: inputting a sample data set through an input unit; dividing the sample data set into a training set and a test set, and performing normalization processing on historical power load data and temperature data of the training set; performing correlation factor analysis on the historical power load data and the temperature data of the training set to construct a feature vector; inputting the feature vectors into a Gaussian process quantile regression prediction model; setting a quantile change interval of the model, wherein the model outputs a plurality of conditional quantile load prediction results; inputting the conditional quantile load prediction result into a kernel density estimation function for kernel density estimation to obtain a load probability density function; and calculating a load confidence interval anda load prediction estimation value through a load probability density function. According to the method, the confidence interval and probability density of power load prediction can be effectively constructed, and the accuracy of power load prediction is improved.

Description

technical field [0001] The invention relates to the technical field of short-term power load forecasting, in particular to a probabilistic load forecasting system and method based on a Gaussian process quantile regression model. Background technique [0002] The power load forecasting problem aims to predict the power demand of a single or multiple transmission lines on the power network. According to the forecast time span, it can be divided into: short-term forecast (a few minutes to a week), medium-term forecast (a month to a quarter), Long-term forecasts (more than one year). Under the current technical conditions, it is difficult to effectively store electric energy in large-scale electric storage devices. Therefore, under the condition of meeting the power supply demand, reducing the remaining power generation as much as possible is an effective way to reduce costs and improve the efficiency of electric energy use. With the grid-connected power generation of new energ...

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Problems solved by technology

[0005] The main purpose of the present invention is to provide a probabilistic load forecasting system and method based on the Gaussian process quantile regression model, aiming to solve the problem that various existing load probability density based forecasting methods cannot construct accurate confidence intervals for electric load forecasting and Technical Problems with Probability Density

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