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N-BEATS neural network for mid-term electricity load forecasting

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  1. Zhong, Zhiming & Fan, Neng & Wu, Lei, 2024. "Multistage Stochastic optimization for mid-term integrated generation and maintenance scheduling of cascaded hydroelectric system with renewable energy uncertainty," European Journal of Operational Research, Elsevier, vol. 318(1), pages 179-199.
  2. Wang, Chuang & Zhao, Haishen & Liu, Yang & Fan, Guojin, 2024. "Minute-level ultra-short-term power load forecasting based on time series data features," Applied Energy, Elsevier, vol. 372(C).
  3. Yang, Shubo & Jahanger, Atif & Awan, Ashar, 2024. "Temperature variation and urban electricity consumption in China: Implications for demand management and planning," Utilities Policy, Elsevier, vol. 90(C).
  4. Adinkrah, Julius & Kemausuor, Francis & Tutu Tchao, Eric & Nunoo-Mensah, Henry & Agbemenu, Andrew Selasi & Adu-Poku, Akwasi & Kponyo, Jerry John, 2025. "Artificial intelligence-based strategies for sustainable energy planning and electricity demand estimation: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 210(C).
  5. He, Yaoyao & Cao, Chaojin & Wang, Shuo & Fu, Hong, 2022. "Nonparametric probabilistic load forecasting based on quantile combination in electrical power systems," Applied Energy, Elsevier, vol. 322(C).
  6. Wen, Qianyun & Liu, Yang, 2025. "Feature engineering and selection for prosumer electricity consumption and production forecasting: A comprehensive framework," Applied Energy, Elsevier, vol. 381(C).
  7. Aoqi Xu & Man-Wen Tian & Behnam Firouzi & Khalid A. Alattas & Ardashir Mohammadzadeh & Ebrahim Ghaderpour, 2022. "A New Deep Learning Restricted Boltzmann Machine for Energy Consumption Forecasting," Sustainability, MDPI, vol. 14(16), pages 1-12, August.
  8. Gao, Yuan & Hu, Zehuan & Yamate, Shun & Otomo, Junichiro & Chen, Wei-An & Liu, Mingzhe & Xu, Tingting & Ruan, Yingjun & Shang, Juan, 2025. "Unlocking predictive insights and interpretability in deep reinforcement learning for Building-Integrated Photovoltaic and Battery (BIPVB) systems," Applied Energy, Elsevier, vol. 384(C).
  9. Pedro M. R. Bento & Jose A. N. Pombo & Maria R. A. Calado & Silvio J. P. S. Mariano, 2021. "Stacking Ensemble Methodology Using Deep Learning and ARIMA Models for Short-Term Load Forecasting," Energies, MDPI, vol. 14(21), pages 1-21, November.
  10. Jiang, Yuqi & Gao, Tianlu & Dai, Yuxin & Si, Ruiqi & Hao, Jun & Zhang, Jun & Gao, David Wenzhong, 2022. "Very short-term residential load forecasting based on deep-autoformer," Applied Energy, Elsevier, vol. 328(C).
  11. Monika Zimmermann & Florian Ziel, 2024. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Papers 2405.17070, arXiv.org, revised Feb 2025.
  12. Eli Hadad & Sohail Hodarkar & Beakal Lemeneh & Dennis Shasha, 2024. "Machine Learning-Enhanced Pairs Trading," Forecasting, MDPI, vol. 6(2), pages 1-22, June.
  13. Paweł Pijarski & Adrian Belowski, 2024. "Application of Methods Based on Artificial Intelligence and Optimisation in Power Engineering—Introduction to the Special Issue," Energies, MDPI, vol. 17(2), pages 1-42, January.
  14. Moreno, Sinvaldo Rodrigues & Seman, Laio Oriel & Stefenon, Stefano Frizzo & Coelho, Leandro dos Santos & Mariani, Viviana Cocco, 2024. "Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition," Energy, Elsevier, vol. 292(C).
  15. Guo, Xiaopeng & Dong, Yining & Ren, Dongfang, 2023. "CO2 emission reduction effect of photovoltaic industry through 2060 in China," Energy, Elsevier, vol. 269(C).
  16. Gao, Tian & Niu, Dongxiao & Ji, Zhengsen & Sun, Lijie, 2022. "Mid-term electricity demand forecasting using improved variational mode decomposition and extreme learning machine optimized by sparrow search algorithm," Energy, Elsevier, vol. 261(PB).
  17. Zimmermann, Monika & Ziel, Florian, 2025. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Applied Energy, Elsevier, vol. 388(C).
  18. Zhu, Jizhong & Dong, Hanjiang & Zheng, Weiye & Li, Shenglin & Huang, Yanting & Xi, Lei, 2022. "Review and prospect of data-driven techniques for load forecasting in integrated energy systems," Applied Energy, Elsevier, vol. 321(C).
  19. Srivastava, Mahima & Tiwari, Prashant Kumar, 2024. "A profit driven optimal scheduling of virtual power plants for peak load demand in competitive electricity markets with machine learning based forecasted generations," Energy, Elsevier, vol. 310(C).
  20. Marcjasz, Grzegorz & Narajewski, Michał & Weron, Rafał & Ziel, Florian, 2023. "Distributional neural networks for electricity price forecasting," Energy Economics, Elsevier, vol. 125(C).
  21. Zhu, Yunlong & Dong, Zhe & Cheng, Zhonghua & Huang, Xiaojin & Dong, Yujie & Zhang, Zuoyi, 2023. "Neural network extended state-observer for energy system monitoring," Energy, Elsevier, vol. 263(PA).
  22. Ruixiang Zhang & Ziyu Zhu & Meng Yuan & Yihan Guo & Jie Song & Xuanxuan Shi & Yu Wang & Yaojie Sun, 2023. "Regional Residential Short-Term Load-Interval Forecasting Based on SSA-LSTM and Load Consumption Consistency Analysis," Energies, MDPI, vol. 16(24), pages 1-17, December.
  23. Agakishiev, Ilyas & Härdle, Wolfgang Karl & Kopa, Milos & Kozmik, Karel & Petukhina, Alla, 2025. "Multivariate probabilistic forecasting of electricity prices with trading applications," Energy Economics, Elsevier, vol. 141(C).
  24. Wang, Junjie & Huang, Wenyu & Ding, Yuanping & Dang, Yaoguo & Ye, Li, 2025. "Forecasting the electric power load based on a novel prediction model coupled with accumulative time-delay effects and periodic fluctuation characteristics," Energy, Elsevier, vol. 317(C).
  25. Bashiri Behmiri, Niaz & Fezzi, Carlo & Ravazzolo, Francesco, 2023. "Incorporating air temperature into mid-term electricity load forecasting models using time-series regressions and neural networks," Energy, Elsevier, vol. 278(C).
  26. Katarzyna Maciejowska & Bartosz Uniejewski & Rafa{l} Weron, 2022. "Forecasting Electricity Prices," Papers 2204.11735, arXiv.org.
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