Short-term power load probability prediction method based on CNN and quantile regression

Abstract

The invention discloses a short-term power load probability prediction method based on CNN and quantile regression, and the method comprises the following steps: 1, collecting power load data, and determining a key influence factor according to the correlation between the power load data and an external influence factor; 2, preprocessing the data, and segmenting the input data into training set data and test set data; 3, performing short-term load probability density prediction model training based on the convolutional neural network and quantile regression by using the training set data in the step 2 to obtain a trained short-term load probability density prediction model based on the convolutional neural network and quantile regression; 4, inputting test data into the trained QRCNN modelto obtain predicted values under different quantiles; and 5, taking predicted values under different quantiles as input, and carrying out load probability density prediction by using a kernel densityestimation method under different confidence coefficients to obtain a prediction interval and a probability density curve.

Description

technical field [0001] The invention relates to the technical field of short-term power load forecasting, in particular to a short-term load probability forecasting method based on convolutional neural network and quantile regression. Background technique [0002] Electric energy is an indispensable part of daily life and industrial production. Due to the real-time nature of electric energy and the nature of being difficult to store in large quantities, reasonable prediction of electric load becomes a necessary prerequisite for maintaining the stable operation of the electric power system. problems faced by the system. [0003] According to previous studies, deterministic load forecasting methods can be broadly classified into two categories: statistical models and machine learning models. Statistical models mainly include autoregressive moving average (ARMA) model, exponential smoothing (ES) model, multiple linear regression (MLR) model and semiparametric additive model. ...

AI Technical Summary

Problems solved by technology

However, since the traditional point forecasting method provides a single forecast estimate and cannot accurately measure the high uncertainty of the power load, more and more scholars have switched their research fields from deterministic forecasting to interval forecasting

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