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Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

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  1. Xie, Jiahang & Yang, Rufan & Hui, Shu-Yuen Ron & Nguyen, Hung D., 2024. "Dual Digital Twin: Cloud–edge collaboration with Lyapunov-based incremental learning in EV batteries," Applied Energy, Elsevier, vol. 355(C).
  2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  3. Ge Zhang & Songyang Zhu & Xiaoqing Bai, 2022. "Federated Learning-Based Multi-Energy Load Forecasting Method Using CNN-Attention-LSTM Model," Sustainability, MDPI, vol. 14(19), pages 1-14, October.
  4. Athanasiadis, C.L. & Papadopoulos, T.A. & Kryonidis, G.C. & Doukas, D.I., 2024. "A review of distribution network applications based on smart meter data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 191(C).
  5. Zhang, Le & Zhu, Jizhong & Zhang, Di & Liu, Yun, 2023. "An incremental photovoltaic power prediction method considering concept drift and privacy protection," Applied Energy, Elsevier, vol. 351(C).
  6. Wadim Strielkowski & Andrey Vlasov & Kirill Selivanov & Konstantin Muraviev & Vadim Shakhnov, 2023. "Prospects and Challenges of the Machine Learning and Data-Driven Methods for the Predictive Analysis of Power Systems: A Review," Energies, MDPI, vol. 16(10), pages 1-31, May.
  7. Dai, Xiaoran & Liu, Guo-Ping & Hu, Wenshan, 2023. "An online-learning-enabled self-attention-based model for ultra-short-term wind power forecasting," Energy, Elsevier, vol. 272(C).
  8. Yundong Gu & Dongfen Ma & Jiawei Cui & Zhenhua Li & Yaqi Chen, 2022. "Variable-Weighted Ensemble Forecasting of Short-Term Power Load Based on Factor Space Theory," Annals of Data Science, Springer, vol. 9(3), pages 485-501, June.
  9. Raiden Skala & Mohamed Ahmed T. A. Elgalhud & Katarina Grolinger & Syed Mir, 2023. "Interval Load Forecasting for Individual Households in the Presence of Electric Vehicle Charging," Energies, MDPI, vol. 16(10), pages 1-21, May.
  10. Alexandra L’Heureux & Katarina Grolinger & Miriam A. M. Capretz, 2022. "Transformer-Based Model for Electrical Load Forecasting," Energies, MDPI, vol. 15(14), pages 1-23, July.
  11. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
  12. Jonathan Gumz & Diego Castro Fettermann & Enzo Morosini Frazzon & Mirko Kück, 2022. "Using Industry 4.0’s Big Data and IoT to Perform Feature-Based and Past Data-Based Energy Consumption Predictions," Sustainability, MDPI, vol. 14(20), pages 1-34, October.
  13. Kong, Xiangyu & Wang, Zhengtao & Liu, Chao & Zhang, Delong & Gao, Hongchao, 2023. "Refined peak shaving potential assessment and differentiated decision-making method for user load in virtual power plants," Applied Energy, Elsevier, vol. 334(C).
  14. Ping Ma & Shuhui Cui & Mingshuai Chen & Shengzhe Zhou & Kai Wang, 2023. "Review of Family-Level Short-Term Load Forecasting and Its Application in Household Energy Management System," Energies, MDPI, vol. 16(15), pages 1-17, August.
  15. Aydin Zaboli & Swetha Rani Kasimalla & Kuchan Park & Younggi Hong & Junho Hong, 2024. "A Comprehensive Review of Behind-the-Meter Distributed Energy Resources Load Forecasting: Models, Challenges, and Emerging Technologies," Energies, MDPI, vol. 17(11), pages 1-27, May.
  16. Chen, Siliang & Ge, Wei & Liang, Xinbin & Jin, Xinqiao & Du, Zhimin, 2024. "Lifelong learning with deep conditional generative replay for dynamic and adaptive modeling towards net zero emissions target in building energy system," Applied Energy, Elsevier, vol. 353(PB).
  17. Fachrizal Aksan & Vishnu Suresh & Przemysław Janik & Tomasz Sikorski, 2023. "Load Forecasting for the Laser Metal Processing Industry Using VMD and Hybrid Deep Learning Models," Energies, MDPI, vol. 16(14), pages 1-24, July.
  18. Deyslen Mariano-Hernández & Luis Hernández-Callejo & Martín Solís & Angel Zorita-Lamadrid & Oscar Duque-Pérez & Luis Gonzalez-Morales & Felix Santos García & Alvaro Jaramillo-Duque & Adalberto Ospino-, 2022. "Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings," Sustainability, MDPI, vol. 14(10), pages 1-14, May.
  19. Elizabeth Michael, Neethu & Hasan, Shazia & Al-Durra, Ahmed & Mishra, Manohar, 2022. "Short-term solar irradiance forecasting based on a novel Bayesian optimized deep Long Short-Term Memory neural network," Applied Energy, Elsevier, vol. 324(C).
  20. Salahuddin Khan, 2023. "Short-Term Electricity Load Forecasting Using a New Intelligence-Based Application," Sustainability, MDPI, vol. 15(16), pages 1-12, August.
  21. 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).
  22. Ahajjam, Mohamed Aymane & Bonilla Licea, Daniel & Ghogho, Mounir & Kobbane, Abdellatif, 2022. "Experimental investigation of variational mode decomposition and deep learning for short-term multi-horizon residential electric load forecasting," Applied Energy, Elsevier, vol. 326(C).
  23. Rizeakos, V. & Bachoumis, A. & Andriopoulos, N. & Birbas, M. & Birbas, A., 2023. "Deep learning-based application for fault location identification and type classification in active distribution grids," Applied Energy, Elsevier, vol. 338(C).
  24. Yousaf Murtaza Rind & Muhammad Haseeb Raza & Muhammad Zubair & Muhammad Qasim Mehmood & Yehia Massoud, 2023. "Smart Energy Meters for Smart Grids, an Internet of Things Perspective," Energies, MDPI, vol. 16(4), pages 1-35, February.
  25. Lu, Renzhi & Bai, Ruichang & Ding, Yuemin & Wei, Min & Jiang, Junhui & Sun, Mingyang & Xiao, Feng & Zhang, Hai-Tao, 2021. "A hybrid deep learning-based online energy management scheme for industrial microgrid," Applied Energy, Elsevier, vol. 304(C).
  26. Graça Gomes, J. & Xu, H.J. & Yang, Q. & Zhao, C.Y., 2021. "An optimization study on a typical renewable microgrid energy system with energy storage," Energy, Elsevier, vol. 234(C).
  27. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  28. Yongjie Yang & Yulong Li & Yan Cai & Hui Tang & Peng Xu, 2024. "Data-Driven Golden Jackal Optimization–Long Short-Term Memory Short-Term Energy-Consumption Prediction and Optimization System," Energies, MDPI, vol. 17(15), pages 1-20, July.
  29. Islam, Md. Zahidul & Lin, Yuzhang & Vokkarane, Vinod M. & Yu, Nanpeng, 2023. "Robust learning-based real-time load estimation using sparsely deployed smart meters with high reporting rates," Applied Energy, Elsevier, vol. 352(C).
  30. Fanidhar Dewangan & Almoataz Y. Abdelaziz & Monalisa Biswal, 2023. "Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review," Energies, MDPI, vol. 16(3), pages 1-55, January.
  31. Jujie Wang & Zhenzhen Zhuang, 2023. "A novel cluster based multi-index nonlinear ensemble framework for carbon price forecasting," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 25(7), pages 6225-6247, July.
  32. 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).
  33. Fachrizal Aksan & Yang Li & Vishnu Suresh & Przemysław Janik, 2023. "Multistep Forecasting of Power Flow Based on LSTM Autoencoder: A Study Case in Regional Grid Cluster Proposal," Energies, MDPI, vol. 16(13), pages 1-20, June.
  34. Fujimoto, Yu & Fujita, Megumi & Hayashi, Yasuhiro, 2021. "Deep reservoir architecture for short-term residential load forecasting: An online learning scheme for edge computing," Applied Energy, Elsevier, vol. 298(C).
  35. Stefan Ungureanu & Vasile Topa & Andrei Cristinel Cziker, 2021. "Analysis for Non-Residential Short-Term Load Forecasting Using Machine Learning and Statistical Methods with Financial Impact on the Power Market," Energies, MDPI, vol. 14(21), pages 1-26, October.
  36. Qin Chen & Komla Agbenyo Folly, 2022. "Application of Artificial Intelligence for EV Charging and Discharging Scheduling and Dynamic Pricing: A Review," Energies, MDPI, vol. 16(1), pages 1-26, December.
  37. Diogo M. F. Izidio & Paulo S. G. de Mattos Neto & Luciano Barbosa & João F. L. de Oliveira & Manoel Henrique da Nóbrega Marinho & Guilherme Ferretti Rissi, 2021. "Evolutionary Hybrid System for Energy Consumption Forecasting for Smart Meters," Energies, MDPI, vol. 14(7), pages 1-19, March.
  38. Türkoğlu, A. Selim & Erkmen, Burcu & Eren, Yavuz & Erdinç, Ozan & Küçükdemiral, İbrahim, 2024. "Integrated Approaches in Resilient Hierarchical Load Forecasting via TCN and Optimal Valley Filling Based Demand Response Application," Applied Energy, Elsevier, vol. 360(C).
  39. Luo, Xing & Zhang, Dongxiao, 2023. "A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs," Energy, Elsevier, vol. 268(C).
  40. Eren, Yavuz & Küçükdemiral, İbrahim, 2024. "A comprehensive review on deep learning approaches for short-term load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
  41. Antić, Tomislav & Capuder, Tomislav, 2024. "A geographic information system-based modelling, analysing and visualising of low voltage networks: The potential of demand time-shifting in the power quality improvement," Applied Energy, Elsevier, vol. 353(PA).
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