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National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?

Citations

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Cited by:

  1. Gong, Jianqiang & Qu, Zhiguo & Zhu, Zhenle & Xu, Hongtao & Yang, Qiguo, 2025. "Ensemble models of TCN-LSTM-LightGBM based on ensemble learning methods for short-term electrical load forecasting," Energy, Elsevier, vol. 318(C).
  2. 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).
  3. Joohyun Jang & Woonyoung Jeong & Sangmin Kim & Byeongcheon Lee & Miyoung Lee & Jihoon Moon, 2023. "RAID: Robust and Interpretable Daily Peak Load Forecasting via Multiple Deep Neural Networks and Shapley Values," Sustainability, MDPI, vol. 15(8), pages 1-27, April.
  4. Altan Unlu & Malaquias Peña, 2025. "Comparative Analysis of Hybrid Deep Learning Models for Electricity Load Forecasting During Extreme Weather," Energies, MDPI, vol. 18(12), pages 1-27, June.
  5. Jonathan Berrisch & Micha{l} Narajewski & Florian Ziel, 2022. "High-Resolution Peak Demand Estimation Using Generalized Additive Models and Deep Neural Networks," Papers 2203.03342, arXiv.org, revised Nov 2022.
  6. Zhang, Zhonglian & Yang, Xiaohui & Yang, Li & Wang, Zhaojun & Huang, Zezhong & Wang, Xiaopeng & Mei, Linghao, 2023. "Optimal configuration of double carbon energy system considering climate change," Energy, Elsevier, vol. 283(C).
  7. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
  8. Elise Viadere, 2025. "Promoting energy-sharing communities: Why and how? Lessons from a Belgian pilot project," ULB Institutional Repository 2013/389043, ULB -- Universite Libre de Bruxelles.
  9. Agbessi Akuété Pierre & Salami Adekunlé Akim & Agbosse Kodjovi Semenyo & Birregah Babiga, 2023. "Peak Electrical Energy Consumption Prediction by ARIMA, LSTM, GRU, ARIMA-LSTM and ARIMA-GRU Approaches," Energies, MDPI, vol. 16(12), pages 1-12, June.
  10. Flygare, Carl & Wallberg, Alexander & Jonasson, Erik & Castellucci, Valeria & Waters, Rafael, 2024. "Correlation as a method to assess electricity users’ contributions to grid peak loads: A case study," Energy, Elsevier, vol. 288(C).
  11. Bingjie Jin & Guihua Zeng & Zhilin Lu & Hongqiao Peng & Shuxin Luo & Xinhe Yang & Haojun Zhu & Mingbo Liu, 2022. "Hybrid LSTM–BPNN-to-BPNN Model Considering Multi-Source Information for Forecasting Medium- and Long-Term Electricity Peak Load," Energies, MDPI, vol. 15(20), pages 1-20, October.
  12. Lai, Changzhi & Wang, Yu & Fan, Kai & Cai, Qilin & Ye, Qing & Pang, Haoqiang & Wu, Xi, 2022. "An improved forecasting model of short-term electric load of papermaking enterprises for production line optimization," Energy, Elsevier, vol. 245(C).
  13. Lee, Juyong & Cho, Youngsang, 2022. "Determinants of reserve margin volatility: A new approach toward managing energy supply and demand," Energy, Elsevier, vol. 252(C).
  14. Elise Viadere, 2024. "Promoting Energy-sharing Communities: why and how? Lessons from a Belgian Pilot Project," Working Papers ECARES 2024-22, ULB -- Universite Libre de Bruxelles.
  15. Yang, Weijia & Sparrow, Sarah N. & Wallom, David C.H., 2024. "A comparative climate-resilient energy design: Wildfire Resilient Load Forecasting Model using multi-factor deep learning methods," Applied Energy, Elsevier, vol. 368(C).
  16. Dong, Shengming & Liu, Tong & Hu, Xiaowei & Zhang, Chen & Hu, Pengli & Zhuang, Wenhui & Liu, Qiyou, 2025. "Investigation on the long short-term memory-based models for rural heating load prediction in Northeast China," Energy, Elsevier, vol. 318(C).
  17. Wang, Lei & Wang, Xinyu & Zhao, Zhongchao, 2024. "Mid-term electricity demand forecasting using improved multi-mode reconstruction and particle swarm-enhanced support vector regression," Energy, Elsevier, vol. 304(C).
  18. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
  19. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Forecasting Long-Term Electricity Consumption in Saudi Arabia Based on Statistical and Machine Learning Algorithms to Enhance Electric Power Supply Management," Energies, MDPI, vol. 16(4), pages 1-28, February.
  20. Marwa Salah EIDin Fahmy & Farhan Ahmed & Farah Durani & Štefan Bojnec & Mona Mohamed Ghareeb, 2023. "Predicting Electricity Consumption in the Kingdom of Saudi Arabia," Energies, MDPI, vol. 16(1), pages 1-20, January.
  21. Shi, Jiaqi & Li, Chenxi & Yan, Xiaohe, 2023. "Artificial intelligence for load forecasting: A stacking learning approach based on ensemble diversity regularization," Energy, Elsevier, vol. 262(PB).
  22. Viadere, Elise, 2025. "Promoting energy-sharing communities: Why and how? Lessons from a Belgian pilot project," Energy Policy, Elsevier, vol. 198(C).
  23. 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.
  24. Sharma, Abhishek & Jain, Sachin Kumar, 2022. "A novel seasonal segmentation approach for day-ahead load forecasting," Energy, Elsevier, vol. 257(C).
  25. Nahid Sultana & S. M. Zakir Hossain & Salma Hamad Almuhaini & Dilek Düştegör, 2022. "Bayesian Optimization Algorithm-Based Statistical and Machine Learning Approaches for Forecasting Short-Term Electricity Demand," Energies, MDPI, vol. 15(9), pages 1-26, May.
  26. Saâdaoui, Foued & Ben Jabeur, Sami, 2023. "Analyzing the influence of geopolitical risks on European power prices using a multiresolution causal neural network," Energy Economics, Elsevier, vol. 124(C).
  27. AL-Alimi, Dalal & AlRassas, Ayman Mutahar & Al-qaness, Mohammed A.A. & Cai, Zhihua & Aseeri, Ahmad O. & Abd Elaziz, Mohamed & Ewees, Ahmed A., 2023. "TLIA: Time-series forecasting model using long short-term memory integrated with artificial neural networks for volatile energy markets," Applied Energy, Elsevier, vol. 343(C).
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