Short-term electricity price prediction method and device based on xgboost algorithm

Abstract

The invention provides a short-term electricity price prediction method and device based on an xgboost algorithm, terminal equipment and a storage medium. The method comprises the steps: obtaining training sample data according to a preset feature selection rule; carrying out normalization preprocessing on the training sample data; inputting the training sample data subjected to normalization preprocessing into a pre-constructed xgboost model for training to obtain an xgboost electricity price prediction model; obtaining prediction day feature data according to the feature selection rule, andinputting the prediction day feature data into the xgboost electricity price prediction model; and performing reverse normalization processing on the output value of the xgboost electricity price prediction model to obtain a predicted daily electricity price prediction value. According to the method, the own characteristics of the spot market clearing price data are considered, the key influence factors are screened out to construct the feature vectors, and the prediction model is constructed based on the xgboost algorithm to perform short-term electricity price prediction, so that the electricity price prediction precision is effectively improved.

Description

technical field [0001] The present invention relates to the technical field of electric power market, in particular to a short-term electricity price prediction method, device, terminal equipment and readable storage medium based on xgboost algorithm. Background technique [0002] One of the core tasks of electricity marketization is to build a spot market, which mainly includes the day-ahead, intraday and real-time markets. The market-clearing price is an actual reflection of the supply-demand relationship in the spot market and directly affects the market profits of market players. Therefore, how to accurately and effectively Predicting the clearing price of the electricity spot market is of great significance for market players to make decision-making plans and grasp market rules. [0003] The existing electricity price forecasting methods and theories can be mainly divided into three categories: market simulation methods, statistical methods and methods based on data min...

AI Technical Summary

Problems solved by technology

[0005] The vast majority of data mining methods are machine learning or deep learning methods. The existing electricity price prediction methods mostly use the traditional GBRT algorithm. Since the traditional GBRT algorithm only uses the first-order derivative of the Taylor expansion, the prediction accuracy of the prediction model Low, it is difficult to be suitable for predicting the clearing price of the spot market with strong nonlinearity

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