Short-term load prediction method based on multi-granularity characteristics and XGBoost model

Abstract

The invention relates to a short-term load prediction method based on multi-granularity characteristics and an XGBoost model. The short-term load prediction method comprises the following steps: acquiring historical short-term load data of a to-be-predicted regional power system; analyzing fluctuation influence factors of the historical short-term load data to obtain date granularity information and meteorological granularity information; calculating the correlation between the multi-dimensional granularity of the date granularity information and the meteorological granularity information and the short-term load by using the Pearson correlation coefficient; selecting a feature combination with high correlation according to the correlation; and predicting the short-term load of the screened feature combinations with high correlation through an XGboost model. According to the method, the Pearson correlation coefficient is used for selecting the characteristics with high multi-granularity correlation as the input, the complexity of the model is reduced, and the XGBoost is used as the prediction model, so that the problem of large-scale data classification can be solved, and the method has the advantages of high accuracy, low possibility of over-fitting and high expandability.

Description

technical field [0001] The present application relates to the technical field of load forecasting, in particular to a short-term load forecasting method based on multi-granularity features and an XGBoost model. Background technique [0002] Short-term load forecasting takes the daily load curve as the forecasting object and is an important part of load forecasting. Short-term load forecasting methods are mainly divided into two categories: traditional forecasting methods and intelligent forecasting methods. Among them, traditional forecasting methods include time series, regression analysis, exponential smoothing, etc., and their models are simple, requiring high regularity of load data, poor adaptability to short-term load forecasting under the influence of multiple factors, and often low in accuracy. [0003] Intelligent forecasting methods include neural networks, support vector machines, deep learning, etc., which have become mainstream forecasting methods. Compared wit...

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Problems solved by technology

[0006] This application provides a short-term load forecasting method based on multi-granularity f

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