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An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting

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  1. Zhao, Yongning & Zhao, Yuan & Liao, Haohan & Pan, Shiji & Zheng, Yingying, 2025. "Interpreting LASSO regression model by feature space matching analysis for spatio-temporal correlation based wind power forecasting," Applied Energy, Elsevier, vol. 380(C).
  2. Paweł Piotrowski & Marcin Kopyt, 2024. "Short-Term Forecasts of Energy Generation in a Solar Power Plant Using Various Machine Learning Models, along with Ensemble and Hybrid Methods," Energies, MDPI, vol. 17(17), pages 1-24, August.
  3. Han, Tian & Li, Ruimeng & Wang, Xiao & Wang, Ying & Chen, Kang & Peng, Huaiwu & Gao, Zhenxin & Wang, Nannan & Peng, Qinke, 2024. "Intra-hour solar irradiance forecasting using topology data analysis and physics-driven deep learning," Renewable Energy, Elsevier, vol. 224(C).
  4. Yu, Yulong & Lv, Shuangyu & Wang, Qiuyu & Xian, Lei & Chen, Lei & Tao, Wen-Quan, 2024. "A two-stage framework for quantifying the impact of operating parameters and optimizing power density and oxygen distribution quality of PEMFC," Renewable Energy, Elsevier, vol. 236(C).
  5. Huang, Congzhi & Yang, Mengyuan, 2023. "Memory long and short term time series network for ultra-short-term photovoltaic power forecasting," Energy, Elsevier, vol. 279(C).
  6. Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.
  7. Hui Wang & Su Yan & Danyang Ju & Nan Ma & Jun Fang & Song Wang & Haijun Li & Tianyu Zhang & Yipeng Xie & Jun Wang, 2023. "Short-Term Photovoltaic Power Forecasting Based on a Feature Rise-Dimensional Two-Layer Ensemble Learning Model," Sustainability, MDPI, vol. 15(21), pages 1-26, November.
  8. Xing, Zhikai & He, Yigang, 2023. "Multi-modal multi-step wind power forecasting based on stacking deep learning model," Renewable Energy, Elsevier, vol. 215(C).
  9. Aras, Serkan & Hanifi Van, M., 2022. "An interpretable forecasting framework for energy consumption and CO2 emissions," Applied Energy, Elsevier, vol. 328(C).
  10. Yang, Mao & Jiang, Yue & Zhang, Wei & Li, Yi & Su, Xin, 2024. "Short-term interval prediction strategy of photovoltaic power based on meteorological reconstruction with spatiotemporal correlation and multi-factor interval constraints," Renewable Energy, Elsevier, vol. 237(PC).
  11. Ali, Aliyuda & Aliyuda, Kachalla & Elmitwally, Nouh & Muhammad Bello, Abdulwahab, 2022. "Towards more accurate and explainable supervised learning-based prediction of deliverability for underground natural gas storage," Applied Energy, Elsevier, vol. 327(C).
  12. Cao, Yisheng & Liu, Gang & Luo, Donghua & Bavirisetti, Durga Prasad & Xiao, Gang, 2023. "Multi-timescale photovoltaic power forecasting using an improved Stacking ensemble algorithm based LSTM-Informer model," Energy, Elsevier, vol. 283(C).
  13. Eduardo Gomes & Augusto Esteves & Hugo Morais & Lucas Pereira, 2025. "Leveraging Explainable Artificial Intelligence in Solar Photovoltaic Mappings: Model Explanations and Feature Selection," Energies, MDPI, vol. 18(5), pages 1-17, March.
  14. Nhat-Duc Hoang & Quoc-Lam Nguyen, 2025. "Geospatial Analysis and Machine Learning Framework for Urban Heat Island Intensity Prediction: Natural Gradient Boosting and Deep Neural Network Regressors with Multisource Remote Sensing Data," Sustainability, MDPI, vol. 17(10), pages 1-26, May.
  15. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2023. "Assessment of solar radiation resource and photovoltaic power potential across China based on optimized interpretable machine learning model and GIS-based approaches," Applied Energy, Elsevier, vol. 339(C).
  16. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
  17. Tahir, Muhammad Faizan & Yousaf, Muhammad Zain & Tzes, Anthony & El Moursi, Mohamed Shawky & El-Fouly, Tarek H.M., 2024. "Enhanced solar photovoltaic power prediction using diverse machine learning algorithms with hyperparameter optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 200(C).
  18. Henni, Sarah & Becker, Jonas & Staudt, Philipp & vom Scheidt, Frederik & Weinhardt, Christof, 2022. "Industrial peak shaving with battery storage using a probabilistic forecasting approach: Economic evaluation of risk attitude," Applied Energy, Elsevier, vol. 327(C).
  19. Zhu, Jianhua & He, Yaoyao, 2025. "A novel hybrid model based on evolving multi-quantile long and short-term memory neural network for ultra-short-term probabilistic forecasting of photovoltaic power," Applied Energy, Elsevier, vol. 377(PC).
  20. Lin, Huapeng & Gao, Liyuan & Cui, Mingtao & Liu, Hengchao & Li, Chunyang & Yu, Miao, 2025. "Short-term distributed photovoltaic power prediction based on temporal self-attention mechanism and advanced signal decomposition techniques with feature fusion," Energy, Elsevier, vol. 315(C).
  21. Leal, Jairon Isaias & Pitombeira-Neto, Anselmo Ramalho & Bueno, André Valente & Costa Rocha, Paulo Alexandre & de Andrade, Carla Freitas, 2025. "Probabilistic wind speed forecasting via Bayesian DLMs and its application in green hydrogen production," Applied Energy, Elsevier, vol. 382(C).
  22. Sahar Zargarzadeh & Aditya Ramnarayan & Felipe de Castro & Michael Ohadi, 2024. "ML-Enabled Solar PV Electricity Generation Projection for a Large Academic Campus to Reduce Onsite CO 2 Emissions," Energies, MDPI, vol. 17(23), pages 1-29, December.
  23. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
  24. Wen, Shuang & Lin, Ni & Huang, Shengxu & Wang, Zhenpo & Zhang, Zhaosheng, 2023. "Lithium battery health state assessment based on vehicle-to-grid (V2G) real-world data and natural gradient boosting model," Energy, Elsevier, vol. 284(C).
  25. Hiroki Yamamoto & Junji Kondoh & Daisuke Kodaira, 2022. "Assessing the Impact of Features on Probabilistic Modeling of Photovoltaic Power Generation," Energies, MDPI, vol. 15(15), pages 1-17, July.
  26. Qiao, Qingyao & Eskandari, Hamidreza & Saadatmand, Hassan & Sahraei, Mohammad Ali, 2024. "An interpretable multi-stage forecasting framework for energy consumption and CO2 emissions for the transportation sector," Energy, Elsevier, vol. 286(C).
  27. Song, Zhe & Cao, Sunliang & Yang, Hongxing, 2024. "An interpretable framework for modeling global solar radiation using tree-based ensemble machine learning and Shapley additive explanations methods," Applied Energy, Elsevier, vol. 364(C).
  28. Xinghua Wang & Zilv Li & Chenyang Fu & Xixian Liu & Weikang Yang & Xiangyuan Huang & Longfa Yang & Jianhui Wu & Zhuoli Zhao, 2024. "Short-Term Photovoltaic Power Probabilistic Forecasting Based on Temporal Decomposition and Vine Copula," Sustainability, MDPI, vol. 16(19), pages 1-25, September.
  29. Franko Pandžić & Tomislav Capuder, 2023. "Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources," Energies, MDPI, vol. 17(1), pages 1-19, December.
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