Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model

Shuang Zeng, Chang Liu, Heng Zhang (), Baoqun Zhang and Yutong Zhao
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Shuang Zeng: State Grid Beijing Electric Power Company, Beijing 100071, China
Chang Liu: State Grid Beijing Electric Power Company, Beijing 100071, China
Heng Zhang: State Grid Beijing Electric Power Company, Beijing 100071, China
Baoqun Zhang: State Grid Beijing Electric Power Company, Beijing 100071, China
Yutong Zhao: State Grid Beijing Electric Power Company, Beijing 100071, China

Energies, 2025, vol. 18, issue 2, 1-15

Abstract: To tackle the challenges of limited accuracy and poor generalization in short-term load forecasting under complex nonlinear conditions, this study introduces a Prophet–BO–XGBoost-based forecasting framework. This approach employs the XGBoost model to interpret the nonlinear relationships between features and loads and integrates the Prophet model for label prediction from a time-series viewpoint. Given that hyperparameters substantially impact XGBoost’s performance, this study leverages Bayesian optimization (BO) to refine these parameters. Using a Gaussian process-based surrogate model and an acquisition function aimed at expected improvement, this framework optimizes hyperparameter settings to enhance model adaptability and precision. Through a regional case study, this method demonstrated improved predictive accuracy and operational efficiency, highlighting its advantages in both runtime and performance.

Keywords: short-term load forecasting; Prophet; Bayesian optimization; XGBoost; hyperparameter (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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