Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model
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 fea...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
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MDPI AG
2025-01-01
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| Series: | Energies |
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| Online Access: | https://www.mdpi.com/1996-1073/18/2/227 |
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| _version_ | 1832588615729807360 |
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| author | Shuang Zeng Chang Liu Heng Zhang Baoqun Zhang Yutong Zhao |
| author_facet | Shuang Zeng Chang Liu Heng Zhang Baoqun Zhang Yutong Zhao |
| author_sort | Shuang Zeng |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-3d641c400fc44c0398c1391584385f42 |
| institution | Kabale University |
| issn | 1996-1073 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Energies |
| spelling | doaj-art-3d641c400fc44c0398c1391584385f422025-01-24T13:30:42ZengMDPI AGEnergies1996-10732025-01-0118222710.3390/en18020227Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost ModelShuang Zeng0Chang Liu1Heng Zhang2Baoqun Zhang3Yutong Zhao4State Grid Beijing Electric Power Company, Beijing 100071, ChinaState Grid Beijing Electric Power Company, Beijing 100071, ChinaState Grid Beijing Electric Power Company, Beijing 100071, ChinaState Grid Beijing Electric Power Company, Beijing 100071, ChinaState Grid Beijing Electric Power Company, Beijing 100071, ChinaTo 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.https://www.mdpi.com/1996-1073/18/2/227short-term load forecastingProphetBayesian optimizationXGBoosthyperparameter |
| spellingShingle | Shuang Zeng Chang Liu Heng Zhang Baoqun Zhang Yutong Zhao Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model Energies short-term load forecasting Prophet Bayesian optimization XGBoost hyperparameter |
| title | Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model |
| title_full | Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model |
| title_fullStr | Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model |
| title_full_unstemmed | Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model |
| title_short | Short-Term Load Forecasting in Power Systems Based on the Prophet–BO–XGBoost Model |
| title_sort | short term load forecasting in power systems based on the prophet bo xgboost model |
| topic | short-term load forecasting Prophet Bayesian optimization XGBoost hyperparameter |
| url | https://www.mdpi.com/1996-1073/18/2/227 |
| work_keys_str_mv | AT shuangzeng shorttermloadforecastinginpowersystemsbasedontheprophetboxgboostmodel AT changliu shorttermloadforecastinginpowersystemsbasedontheprophetboxgboostmodel AT hengzhang shorttermloadforecastinginpowersystemsbasedontheprophetboxgboostmodel AT baoqunzhang shorttermloadforecastinginpowersystemsbasedontheprophetboxgboostmodel AT yutongzhao shorttermloadforecastinginpowersystemsbasedontheprophetboxgboostmodel |