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Multiscale stochastic prediction of electricity demand in smart grids using Bayesian networks

Citations

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Cited by:

  1. Luca Di Persio & Mohammed Alruqimi & Matteo Garbelli, 2024. "Stochastic Approaches to Energy Markets: From Stochastic Differential Equations to Mean Field Games and Neural Network Modeling," Energies, MDPI, vol. 17(23), pages 1-46, December.
  2. Liyang Tang, 2020. "Application of Nonlinear Autoregressive with Exogenous Input (NARX) neural network in macroeconomic forecasting, national goal setting and global competitiveness assessment," Papers 2005.08735, arXiv.org.
  3. Zhu, Jianhua & Peng, Yan & Gong, Zhuping & Sun, Yanming & Lai, Chaoan & Wang, Qing & Zhu, Xiaojun & Gan, Zhongxue, 2019. "Dynamic analysis of SNG and PNG supply: The stability and robustness view #," Energy, Elsevier, vol. 185(C), pages 717-729.
  4. Su, Huai & Zhang, Jinjun & Zio, Enrico & Yang, Nan & Li, Xueyi & Zhang, Zongjie, 2018. "An integrated systemic method for supply reliability assessment of natural gas pipeline networks," Applied Energy, Elsevier, vol. 209(C), pages 489-501.
  5. Qasem Abu Al-Haija & Abdallah A. Smadi & Mohammed F. Allehyani, 2021. "Meticulously Intelligent Identification System for Smart Grid Network Stability to Optimize Risk Management," Energies, MDPI, vol. 14(21), pages 1-19, October.
  6. Pedro Antonio Martín-Cervantes & Parisa Ziarati & Pablo de Frutos Madrazo & Iza Gigauri, 2025. "Digital Marketing as a Driver of Change Towards the Circular Economy," Sustainability, MDPI, vol. 17(11), pages 1-24, June.
  7. Arman Fathollahi, 2025. "Machine Learning and Artificial Intelligence Techniques in Smart Grids Stability Analysis: A Review," Energies, MDPI, vol. 18(13), pages 1-33, June.
  8. Kelk, Rainer & Podofillini, Luca & Dang, Vinh N. & Panos, Evangelos, 2025. "Explorative application of discrete Bayesian networks as surrogate models for energy systems analysis," Applied Energy, Elsevier, vol. 394(C).
  9. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  10. Li, Pei-Hao & Zamanipour, Behzad & Keppo, Ilkka, 2024. "Revealing technological entanglements in uncertain decarbonisation pathways using bayesian networks," Energy Policy, Elsevier, vol. 193(C).
  11. Ferrari, Lorenzo & Esposito, Fabio & Becciani, Michele & Ferrara, Giovanni & Magnani, Sandro & Andreini, Mirko & Bellissima, Alessandro & Cantù, Matteo & Petretto, Giacomo & Pentolini, Massimo, 2017. "Development of an optimization algorithm for the energy management of an industrial Smart User," Applied Energy, Elsevier, vol. 208(C), pages 1468-1486.
  12. Zheng, Zhuang & Shafique, Muhammad & Luo, Xiaowei & Wang, Shengwei, 2024. "A systematic review towards integrative energy management of smart grids and urban energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  13. Obara, Shin'ya & Hamanaka, Ryo & El-Sayed, Abeer Galal, 2019. "Design methods for microgrids to address seasonal energy availability – A case study of proposed Showa Antarctic Station retrofits," Applied Energy, Elsevier, vol. 236(C), pages 711-727.
  14. Liu, Chao & Akintayo, Adedotun & Jiang, Zhanhong & Henze, Gregor P. & Sarkar, Soumik, 2018. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network," Applied Energy, Elsevier, vol. 211(C), pages 1106-1122.
  15. 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).
  16. Hassam Ishfaq & Sania Kanwal & Sadeed Anwar & Mubarak Abdussalam & Waqas Amin, 2025. "Enhancing Smart Grid Security and Efficiency: AI, Energy Routing, and T&D Innovations (A Review)," Energies, MDPI, vol. 18(17), pages 1-77, September.
  17. Zhiyong Li & Wenbin Wu & Yang Si & Xiaotao Chen, 2023. "Optimal Siting and Sizing of Hydrogen Production Modules in Distribution Networks with Photovoltaic Uncertainties," Energies, MDPI, vol. 16(22), pages 1-15, November.
  18. Maticka, Martin J. & Mahmoud, Thair S., 2025. "Bayesian Belief Networks: Redefining wholesale electricity price modelling in high penetration non-firm renewable generation power systems," Renewable Energy, Elsevier, vol. 239(C).
  19. Akash Mahajan & Srijita Das & Wencong Su & Van-Hai Bui, 2024. "Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands," Sustainability, MDPI, vol. 16(22), pages 1-21, November.
  20. Peplinski, McKenna & Dilkina, Bistra & Chen, Mo & Silva, Sam J. & Ban-Weiss, George A. & Sanders, Kelly T., 2024. "A machine learning framework to estimate residential electricity demand based on smart meter electricity, climate, building characteristics, and socioeconomic datasets," Applied Energy, Elsevier, vol. 357(C).
  21. Chemweno, Peter & Pintelon, Liliane & Muchiri, Peter Nganga & Van Horenbeek, Adriaan, 2018. "Risk assessment methodologies in maintenance decision making: A review of dependability modelling approaches," Reliability Engineering and System Safety, Elsevier, vol. 173(C), pages 64-77.
  22. Ngai, Eric W.T. & Wu, Yuanyuan, 2022. "Machine learning in marketing: A literature review, conceptual framework, and research agenda," Journal of Business Research, Elsevier, vol. 145(C), pages 35-48.
  23. Giancarlo Aquila & Lucas Barros Scianni Morais & Victor Augusto Durães de Faria & José Wanderley Marangon Lima & Luana Medeiros Marangon Lima & Anderson Rodrigo de Queiroz, 2023. "An Overview of Short-Term Load Forecasting for Electricity Systems Operational Planning: Machine Learning Methods and the Brazilian Experience," Energies, MDPI, vol. 16(21), pages 1-35, November.
  24. Hamed, Mohammad M. & Ali, Hesham & Abdelal, Qasem, 2022. "Forecasting annual electric power consumption using a random parameters model with heterogeneity in means and variances," Energy, Elsevier, vol. 255(C).
  25. 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.
  26. Gerossier, Alexis & Barbier, Thibaut & Girard, Robin, 2017. "A novel method for decomposing electricity feeder load into elementary profiles from customer information," Applied Energy, Elsevier, vol. 203(C), pages 752-760.
  27. Khan, Waqas & Liao, Juo Yu & Walker, Shalika & Zeiler, Wim, 2022. "Impact assessment of varied data granularities from commercial buildings on exploration and learning mechanism," Applied Energy, Elsevier, vol. 319(C).
  28. Guo, Yabin & Wang, Jiangyu & Chen, Huanxin & Li, Guannan & Liu, Jiangyan & Xu, Chengliang & Huang, Ronggeng & Huang, Yao, 2018. "Machine learning-based thermal response time ahead energy demand prediction for building heating systems," Applied Energy, Elsevier, vol. 221(C), pages 16-27.
  29. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
  30. He, Yaoyao & Qin, Yang & Wang, Shuo & Wang, Xu & Wang, Chao, 2019. "Electricity consumption probability density forecasting method based on LASSO-Quantile Regression Neural Network," Applied Energy, Elsevier, vol. 233, pages 565-575.
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