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Probabilistic load forecasting via Quantile Regression Averaging on sister forecasts

Citations

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Cited by:

  1. Wang, Yi & Von Krannichfeldt, Leandro & Zufferey, Thierry & Toubeau, Jean-François, 2021. "Short-term nodal voltage forecasting for power distribution grids: An ensemble learning approach," Applied Energy, Elsevier, vol. 304(C).
  2. Niematallah Elamin & Mototsugu Fukushige, 2016. "A Quantile Regression Model for Electricity Peak Demand Forecasting: An Approach to Avoiding Power Blackouts," Discussion Papers in Economics and Business 16-22, Osaka University, Graduate School of Economics.
  3. Nowotarski, Jakub & Weron, Rafał, 2018. "Recent advances in electricity price forecasting: A review of probabilistic forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1548-1568.
  4. Wang, Pu & Liu, Bidong & Hong, Tao, 2016. "Electric load forecasting with recency effect: A big data approach," International Journal of Forecasting, Elsevier, vol. 32(3), pages 585-597.
  5. Caston Sigauke & Murendeni Maurel Nemukula & Daniel Maposa, 2018. "Probabilistic Hourly Load Forecasting Using Additive Quantile Regression Models," Energies, MDPI, vol. 11(9), pages 1-21, August.
  6. Grzegorz Marcjasz & Bartosz Uniejewski & Rafal Weron, 2017. "Importance of the long-term seasonal component in day-ahead electricity price forecasting revisited: Neural network models," HSC Research Reports HSC/17/03, Hugo Steinhaus Center, Wroclaw University of Science and Technology.
  7. 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.
  8. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(C).
  9. Huosong Xia & Xiaoyu Hou & Justin Zuopeng Zhang & Mohammad Zoynul Abedin, 2025. "A new probability forecasting model for cotton yarn futures price volatility with explainable AI and big data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(1), pages 112-135, January.
  10. Niematallah Elamin & Mototsugu Fukushige, 2018. "Quantile Regression Model for Peak Load Demand Forecasting with Approximation by Triangular Distribution to Avoid Blackouts," International Journal of Energy Economics and Policy, International Journal of Energy Economics and Policy, vol. 8(5), pages 119-124.
  11. Pu, Chuanqing & Fan, Feilong & Tai, Nengling & Liu, Songyuan & Yu, Jinming, 2026. "A hybrid strategy for probabilistic forecasting and trading of aggregated wind-solar power: Design and analysis in HEFTCom2024," International Journal of Forecasting, Elsevier, vol. 42(3), pages 736-751.
  12. 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).
  13. Grzegorz Marcjasz & Tomasz Serafin & Rafał Weron, 2018. "Selection of Calibration Windows for Day-Ahead Electricity Price Forecasting," Energies, MDPI, vol. 11(9), pages 1-20, September.
  14. Xu, Xiuqin & Chen, Ying & Goude, Yannig & Yao, Qiwei, 2021. "Day-ahead probabilistic forecasting for French half-hourly electricity loads and quantiles for curve-to-curve regression," Applied Energy, Elsevier, vol. 301(C).
  15. Yang, Yandong & Li, Shufang & Li, Wenqi & Qu, Meijun, 2018. "Power load probability density forecasting using Gaussian process quantile regression," Applied Energy, Elsevier, vol. 213(C), pages 499-509.
  16. Uniejewski, Bartosz & Marcjasz, Grzegorz & Weron, Rafał, 2019. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting: Part II — Probabilistic forecasting," Energy Economics, Elsevier, vol. 79(C), pages 171-182.
  17. Yoo, Yeuntae & Jung, Seungmin, 2025. "Correlation-segregated joint pdf for stochastic assessment of synthetic inertia in wind turbines," Renewable Energy, Elsevier, vol. 253(C).
  18. Nowotarski, Jakub & Liu, Bidong & Weron, Rafał & Hong, Tao, 2016. "Improving short term load forecast accuracy via combining sister forecasts," Energy, Elsevier, vol. 98(C), pages 40-49.
  19. Qin, Dalin & Wu, Xian & Sun, Dayan & Liang, Zhifeng & Zhang, Ning, 2025. "Load forecasting under distribution shift: An online quantile ensembling approach," Applied Energy, Elsevier, vol. 401(PC).
  20. Liu, Tianhong & Qi, Shengli & Qiao, Xianzhu & Liu, Sixing, 2024. "A hybrid short-term wind power point-interval prediction model based on combination of improved preprocessing methods and entropy weighted GRU quantile regression network," Energy, Elsevier, vol. 288(C).
  21. Huangjie Gong & Rosemary E. Alden & Aron Patrick & Dan M. Ionel, 2022. "Forecast of Community Total Electric Load and HVAC Component Disaggregation through a New LSTM-Based Method," Energies, MDPI, vol. 15(9), pages 1-17, April.
  22. Zhang, Wenjie & Quan, Hao & Srinivasan, Dipti, 2018. "Parallel and reliable probabilistic load forecasting via quantile regression forest and quantile determination," Energy, Elsevier, vol. 160(C), pages 810-819.
  23. Sepehr Moalem & Roya M. Ahari & Ghazanfar Shahgholian & Majid Moazzami & Seyed Mohammad Kazemi, 2022. "Long-Term Electricity Demand Forecasting in the Steel Complex Micro-Grid Electricity Supply Chain—A Coupled Approach," Energies, MDPI, vol. 15(21), pages 1-17, October.
  24. Hong, Tao & Fan, Shu, 2016. "Probabilistic electric load forecasting: A tutorial review," International Journal of Forecasting, Elsevier, vol. 32(3), pages 914-938.
  25. Nowotarski, Jakub & Weron, Rafał, 2016. "On the importance of the long-term seasonal component in day-ahead electricity price forecasting," Energy Economics, Elsevier, vol. 57(C), pages 228-235.
  26. Liu, Tianhao & Li, Fangning & Zhang, Dongdong & Shan, Linke & Zhu, Hongyu & Du, Pengcheng & Jiang, Meihui & Goh, Hui Hwang & Kurniawan, Tonni Agustiono & Huang, Chao & Kong, Fannie, 2026. "Intelligent load forecasting technologies for diverse scenarios in the new power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 226(PD).
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