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Ensemble of relevance vector machines and boosted trees for electricity price forecasting

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  1. Özen, Kadir & Yıldırım, Dilem, 2021. "Application of bagging in day-ahead electricity price forecasting and factor augmentation," Energy Economics, Elsevier, vol. 103(C).
  2. Beltrán, Sergio & Castro, Alain & Irizar, Ion & Naveran, Gorka & Yeregui, Imanol, 2022. "Framework for collaborative intelligence in forecasting day-ahead electricity price," Applied Energy, Elsevier, vol. 306(PA).
  3. Jasiński, Tomasz, 2020. "Use of new variables based on air temperature for forecasting day-ahead spot electricity prices using deep neural networks: A new approach," Energy, Elsevier, vol. 213(C).
  4. Meng, Anbo & Wang, Peng & Zhai, Guangsong & Zeng, Cong & Chen, Shun & Yang, Xiaoyi & Yin, Hao, 2022. "Electricity price forecasting with high penetration of renewable energy using attention-based LSTM network trained by crisscross optimization," Energy, Elsevier, vol. 254(PA).
  5. Faramarz Saghi & Mustafa Jahangoshai Rezaee, 2023. "Integrating Wavelet Decomposition and Fuzzy Transformation for Improving the Accuracy of Forecasting Crude Oil Price," Computational Economics, Springer;Society for Computational Economics, vol. 61(2), pages 559-591, February.
  6. Di Yang & Weixin Luan & Jun Yang & Bing Xue & Xiaoling Zhang & Hui Wang & Feng Pian, 2022. "The contribution of data-driven poverty alleviation funds in achieving mid-21st-Century multidimensional poverty alleviation planning," Humanities and Social Sciences Communications, Palgrave Macmillan, vol. 9(1), pages 1-9, December.
  7. Shi, Yong & Ren, Xinyue & Guo, Kun & Zhou, Yi & Wang, Jun, 2020. "Research on the economic development pattern of Chinese counties based on electricity consumption," Energy Policy, Elsevier, vol. 147(C).
  8. Loizidis, Stylianos & Kyprianou, Andreas & Georghiou, George E., 2024. "Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets," Applied Energy, Elsevier, vol. 363(C).
  9. Lu, Renzhi & Bai, Ruichang & Huang, Yuan & Li, Yuting & Jiang, Junhui & Ding, Yuemin, 2021. "Data-driven real-time price-based demand response for industrial facilities energy management," Applied Energy, Elsevier, vol. 283(C).
  10. Haider Ali & Faheem Aslam & Paulo Ferreira, 2021. "Modeling Dynamic Multifractal Efficiency of US Electricity Market," Energies, MDPI, vol. 14(19), pages 1-16, September.
  11. Matheus Henrique Dal Molin Ribeiro & Stéfano Frizzo Stefenon & José Donizetti de Lima & Ademir Nied & Viviana Cocco Mariani & Leandro dos Santos Coelho, 2020. "Electricity Price Forecasting Based on Self-Adaptive Decomposition and Heterogeneous Ensemble Learning," Energies, MDPI, vol. 13(19), pages 1-22, October.
  12. Alice La Fata & Giulio Caprara & Riccardo Barilli & Renato Procopio, 2025. "A Classification Algorithm for Revenue Range Estimation in Ancillary Service Markets," Energies, MDPI, vol. 18(19), pages 1-20, October.
  13. Kadir Özen & Dilem Yıldırım, 2021. "Application of Bagging in Day-Ahead Electricity Price Forecasting and Factor Augmentation," ERC Working Papers 2101, ERC - Economic Research Center, Middle East Technical University, revised Apr 2021.
  14. Bhatia, Kushagra & Mittal, Rajat & Varanasi, Jyothi & Tripathi, M.M., 2021. "An ensemble approach for electricity price forecasting in markets with renewable energy resources," Utilities Policy, Elsevier, vol. 70(C).
  15. Lu, Hongfang & Ma, Xin & Huang, Kun & Azimi, Mohammadamin, 2020. "Prediction of offshore wind farm power using a novel two-stage model combining kernel-based nonlinear extension of the Arps decline model with a multi-objective grey wolf optimizer," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  16. Heydari, Azim & Majidi Nezhad, Meysam & Pirshayan, Elmira & Astiaso Garcia, Davide & Keynia, Farshid & De Santoli, Livio, 2020. "Short-term electricity price and load forecasting in isolated power grids based on composite neural network and gravitational search optimization algorithm," Applied Energy, Elsevier, vol. 277(C).
  17. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 267-286.
  18. Daniel Manfre Jaimes & Manuel Zamudio López & Hamidreza Zareipour & Mike Quashie, 2023. "A Hybrid Model for Multi-Day-Ahead Electricity Price Forecasting considering Price Spikes," Forecasting, MDPI, vol. 5(3), pages 1-23, July.
  19. Hui Huang & Qiliang Zhu & Xueling Zhu & Jinhua Zhang, 2023. "An Adaptive, Data-Driven Stacking Ensemble Learning Framework for the Short-Term Forecasting of Renewable Energy Generation," Energies, MDPI, vol. 16(4), pages 1-20, February.
  20. Ibrahim, Muhammad Sohail & Dong, Wei & Yang, Qiang, 2020. "Machine learning driven smart electric power systems: Current trends and new perspectives," Applied Energy, Elsevier, vol. 272(C).
  21. Meira, Erick & Cyrino Oliveira, Fernando Luiz & de Menezes, Lilian M., 2022. "Forecasting natural gas consumption using Bagging and modified regularization techniques," Energy Economics, Elsevier, vol. 106(C).
  22. Christos Hadjichristofi & Spyridon Diochnos & Kyriakos Andresakis & Vassilios Vescoukis, 2024. "Using Time-Series Databases for Energy Data Infrastructures," Energies, MDPI, vol. 17(21), pages 1-23, November.
  23. Zhichao Zhang & Xue Li & Yanling Zhao & Zhaogong Zhang & Bin Li, 2025. "Prediction Accuracy of Stackelberg Game Model of Electricity Price in Smart Grid Power Market Environment," Energies, MDPI, vol. 18(3), pages 1-16, January.
  24. Huang, Siwan & Shi, Jianheng & Wang, Baoyue & An, Na & Li, Li & Hou, Xuebing & Wang, Chunsen & Zhang, Xiandong & Wang, Kai & Li, Huilin & Zhang, Sui & Zhong, Ming, 2024. "A hybrid framework for day-ahead electricity spot-price forecasting: A case study in China," Applied Energy, Elsevier, vol. 373(C).
  25. Zeng, Sheng & Su, Bin & Zhang, Minglong & Gao, Yuan & Liu, Jun & Luo, Song & Tao, Qingmei, 2021. "Analysis and forecast of China's energy consumption structure," Energy Policy, Elsevier, vol. 159(C).
  26. Shimomura, Mizue & Keeley, Alexander Ryota & Matsumoto, Ken'ichi & Tanaka, Kenta & Managi, Shunsuke, 2024. "Beyond the merit order effect: Impact of the rapid expansion of renewable energy on electricity market price," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  27. Wang, Zicheng & Gao, Ruobin & Wang, Piao & Chen, Huayou, 2023. "A new perspective on air quality index time series forecasting: A ternary interval decomposition ensemble learning paradigm," Technological Forecasting and Social Change, Elsevier, vol. 191(C).
  28. He Jiang & Sheng Pan & Yao Dong & Jianzhou Wang, 2024. "Probabilistic electricity price forecasting based on penalized temporal fusion transformer," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(5), pages 1465-1491, August.
  29. Elmore, Clay T. & Dowling, Alexander W., 2021. "Learning spatiotemporal dynamics in wholesale energy markets with dynamic mode decomposition," Energy, Elsevier, vol. 232(C).
  30. Yang, Haolin & Schell, Kristen R., 2021. "Real-time electricity price forecasting of wind farms with deep neural network transfer learning and hybrid datasets," Applied Energy, Elsevier, vol. 299(C).
  31. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  32. Singh, Priyanka & Kottath, Rahul, 2022. "Influencer-defaulter mutation-based optimization algorithms for predicting electricity prices," Utilities Policy, Elsevier, vol. 79(C).
  33. Chai, Shanglei & Li, Qiang & Abedin, Mohammad Zoynul & Lucey, Brian M., 2024. "Forecasting electricity prices from the state-of-the-art modeling technology and the price determinant perspectives," Research in International Business and Finance, Elsevier, vol. 67(PA).
  34. Sai, Wei & Pan, Zehua & Liu, Siyu & Jiao, Zhenjun & Zhong, Zheng & Miao, Bin & Chan, Siew Hwa, 2023. "Event-driven forecasting of wholesale electricity price and frequency regulation price using machine learning algorithms," Applied Energy, Elsevier, vol. 352(C).
  35. Lan, Tian & Huang, Lianzhong & Ma, Ranqi & Wang, Kai & Ruan, Zhang & Wu, Jianyi & Li, Xiaowu & Chen, Li, 2025. "A robust method of dual adaptive prediction for ship fuel consumption based on polymorphic particle swarm algorithm driven," Applied Energy, Elsevier, vol. 379(C).
  36. Shao, Zhen & Yang, Yudie & Zheng, Qingru & Zhou, Kaile & Liu, Chen & Yang, Shanlin, 2022. "A pattern classification methodology for interval forecasts of short-term electricity prices based on hybrid deep neural networks: A comparative analysis," Applied Energy, Elsevier, vol. 327(C).
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