Power load probability density prediction method based on fuzzy support vector quantile regression

Abstract

The invention discloses a power load probability density prediction method based on fuzzy support vector quantile regression. According to the method, maximum day load data and average temperature data before a prediction day are collected; a train set and a test set are established through adoption of history data; lagrangian multipliers and support vector subscripts of a fuzzy support vector quantile regression model are obtained through utilization of the train set; the fuzzy support vector quantile regression prediction model is established according to obtained model parameter values; the test set is substituted into the model to obtain prediction values; and probability density prediction of the maximum day load is realized through utilization of the obtained prediction values under different quantiles and through application of kernel density estimation. According to the method, prediction errors can be effectively reduced, the power load prediction precision is improved, the good prediction effect is obtained, and the relatively reliable basis is provided for a power system scheduling department to adjust power consumption plans and optimize generator set contribution.

Description

technical field [0001] The invention belongs to the field of power load prediction combining statistical methods and intelligent calculations, and mainly relates to a power load probability density prediction method based on fuzzy support vector quantile regression. Background technique [0002] Power system load forecasting is based on the historical data of power load, economy, society, weather, etc., to explore the influence of the change law of the historical data of power load on the future load, and to seek the internal relationship between the power load and various related factors, so as to predict the future. Scientific prediction of power load. Accurate power system load forecasting is of great significance for power system dispatching, power consumption, planning, making power purchase plans, and arranging operation modes. This also requires power system researchers to propose more effective methods to improve prediction accuracy. [0003] In recent years, with ...

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Problems solved by technology

How these factors affect load accuracy is uncertain or ambiguous

However, traditional power load forecasting methods do not preprocess these uncertain factors.

That is to say, the historical data used in the prediction are all definite values, but the formation of these historical data has certain accidental factors, ignoring the uncertainty of historical data;

[0006] (2) The traditional power load forecasting method only gives point forecasting results or interval forecasting results, which cannot accurately describe the volatility of power loads, nor does it give the probability density distribution of the obtained forecasting results

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