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How to model European electricity load profiles using artificial neural networks

Citations

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Cited by:

  1. Dampeyrou, Charles & Goichon, Antoine & Ghienne, Martin & Tschannen, Valentin & Schaack, Sofiane, 2024. "Unsupervised separation of the thermosensitive contribution in the power consumption at a country scale," Applied Energy, Elsevier, vol. 363(C).
  2. Paweł Piotrowski & Dariusz Baczyński & Marcin Kopyt, 2022. "Medium-Term Forecasts of Load Profiles in Polish Power System including E-Mobility Development," Energies, MDPI, vol. 15(15), pages 1-27, August.
  3. Salman, Muhammad, 2025. "Germany’s energy security strategy in times of turmoil: The role of AI-driven energy systems and environmental policy in the Russian gas exit," Energy Policy, Elsevier, vol. 205(C).
  4. Dariusz Kurz & Agata Nowak, 2023. "Analysis of the Impact of the Level of Self-Consumption of Electricity from a Prosumer Photovoltaic Installation on Its Profitability under Different Energy Billing Scenarios in Poland," Energies, MDPI, vol. 16(2), pages 1-40, January.
  5. 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.
  6. Thangjam Aditya & Sanjita Jaipuria & Pradeep Kumar Dadabada, 2025. "A Review of Methods for Long‐Term Electric Load Forecasting," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 44(4), pages 1403-1423, July.
  7. Ergun Yukseltan & Esra Agca Aktunc & Ayse H. Bilge & Ahmet Yucekaya, 2024. "An Overview of Electricity Consumption in Europe: Models for Prediction of the Electricity Usage for Heating and Cooling," International Journal of Energy Economics and Policy, Econjournals, vol. 14(2), pages 96-111, March.
  8. Prajowal Manandhar & Hasan Rafiq & Edwin Rodriguez-Ubinas & Themis Palpanas, 2024. "New Forecasting Metrics Evaluated in Prophet, Random Forest, and Long Short-Term Memory Models for Load Forecasting," Energies, MDPI, vol. 17(23), pages 1-30, December.
  9. Gülay Yıldız Doğan & Aslı Aksoy & Nursel Öztürk, 2024. "A Hybrid Deep Learning Model to Estimate the Future Electricity Demand of Sustainable Cities," Sustainability, MDPI, vol. 16(15), pages 1-16, July.
  10. Satgé, Valentin & Gabriel, Sophie & Clastres, Cédric, 2025. "Sensitivity analysis of load profiles: Implications for resource adequacy in future power system," Energy, Elsevier, vol. 335(C).
  11. Zimmermann, Monika & Ziel, Florian, 2025. "Efficient mid-term forecasting of hourly electricity load using generalized additive models," Applied Energy, Elsevier, vol. 388(C).
  12. Shafie Bahman & Hamidreza Zareipour, 2025. "Long-Term Multi-Resolution Probabilistic Load Forecasting Using Temporal Hierarchies," Energies, MDPI, vol. 18(11), pages 1-30, June.
  13. Elahi, Ehsan & Zhang, Zhixin & Khalid, Zainab & Xu, Haiyun, 2022. "Application of an artificial neural network to optimise energy inputs: An energy- and cost-saving strategy for commercial poultry farms," Energy, Elsevier, vol. 244(PB).
  14. Ferdaus, Md Meftahul & Dam, Tanmoy & Anavatti, Sreenatha & Das, Sarobi, 2024. "Digital technologies for a net-zero energy future: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 202(C).
  15. Lyu, Wenjing & Liu, Jin, 2021. "Artificial Intelligence and emerging digital technologies in the energy sector," Applied Energy, Elsevier, vol. 303(C).
  16. Leonard Burg & Gonca Gürses-Tran & Reinhard Madlener & Antonello Monti, 2021. "Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels," Energies, MDPI, vol. 14(21), pages 1-16, November.
  17. Thangjam, Aditya & Jaipuria, Sanjita & Dadabada, Pradeep Kumar, 2023. "Time-Varying approaches for Long-Term Electric Load Forecasting under economic shocks," Applied Energy, Elsevier, vol. 333(C).
  18. Hasith Jayasinghe & Kosala Gunawardane & Robert Nicholson, 2025. "Applications of Electrical Load Modelling in Digital Twins of Power Systems," Energies, MDPI, vol. 18(4), pages 1-25, February.
  19. Nolting, Lars & Praktiknjo, Aaron, 2022. "The complexity dilemma – Insights from security of electricity supply assessments," Energy, Elsevier, vol. 241(C).
  20. Wang, Jianzhou & Zhang, Linyue & Li, Zhiwu, 2022. "Interval forecasting system for electricity load based on data pre-processing strategy and multi-objective optimization algorithm," Applied Energy, Elsevier, vol. 305(C).
  21. Michael Meiser & Ingo Zinnikus, 2024. "A Survey on the Use of Synthetic Data for Enhancing Key Aspects of Trustworthy AI in the Energy Domain: Challenges and Opportunities," Energies, MDPI, vol. 17(9), pages 1-29, April.
  22. Marlon Schlemminger & Raphael Niepelt & Rolf Brendel, 2021. "A Cross-Country Model for End-Use Specific Aggregated Household Load Profiles," Energies, MDPI, vol. 14(8), pages 1-24, April.
  23. Qiao, Sen & Chang, Yuan & Yang, Meng & Dang, Yi Jing, 2025. "Motivation or resistance: A multidimensional analysis of quantile network spillovers between smart grids and carbon markets from a digital technology perspective," Technology in Society, Elsevier, vol. 83(C).
  24. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
  25. Mayer, Martin János & Biró, Bence & Szücs, Botond & Aszódi, Attila, 2023. "Probabilistic modeling of future electricity systems with high renewable energy penetration using machine learning," Applied Energy, Elsevier, vol. 336(C).
  26. 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).
  27. Monika Zimmermann & Florian Ziel, 2024. "Spatial Weather, Socio-Economic and Political Risks in Probabilistic Load Forecasting," Papers 2408.00507, arXiv.org, revised Dec 2024.
  28. Tillmanns, M. & Schöttler, J. & Praktiknjo, A., 2026. "A review of probabilistic resource adequacy assessments in power systems: Methods, applications, and future challenges," Energy Policy, Elsevier, vol. 209(PA).
  29. Dengyong Zhang & Haixin Tong & Feng Li & Lingyun Xiang & Xiangling Ding, 2020. "An Ultra-Short-Term Electrical Load Forecasting Method Based on Temperature-Factor-Weight and LSTM Model," Energies, MDPI, vol. 13(18), pages 1-14, September.
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