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Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

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  1. Razzano, Giuseppe & Brandi, Silvio & Piscitelli, Marco Savino & Capozzoli, Alfonso, 2025. "Rule extraction from deep reinforcement learning controller and comparative analysis with ASHRAE control sequences for the optimal management of Heating, Ventilation, and Air Conditioning (HVAC) syste," Applied Energy, Elsevier, vol. 381(C).
  2. Simona-Vasilica Oprea & Adela Bâra & Florina Camelia Puican & Ioan Cosmin Radu, 2021. "Anomaly Detection with Machine Learning Algorithms and Big Data in Electricity Consumption," Sustainability, MDPI, vol. 13(19), pages 1-20, October.
  3. Chen, Zhelun & O’Neill, Zheng & Wen, Jin & Pradhan, Ojas & Yang, Tao & Lu, Xing & Lin, Guanjing & Miyata, Shohei & Lee, Seungjae & Shen, Chou & Chiosa, Roberto & Piscitelli, Marco Savino & Capozzoli, , 2023. "A review of data-driven fault detection and diagnostics for building HVAC systems," Applied Energy, Elsevier, vol. 339(C).
  4. Choi, Sebin & Yoon, Sungmin, 2024. "Change-point model-based clustering for urban building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
  5. Enrique C. Quispe & Miguel Viveros Mira & Mauricio Chamorro Díaz & Rosaura Castrillón Mendoza & Juan R. Vidal Medina, 2025. "Energy Management Systems in Higher Education Institutions’ Buildings," Energies, MDPI, vol. 18(7), pages 1-35, April.
  6. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
  7. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
  8. Fan, Cheng & Xiao, Fu & Song, Mengjie & Wang, Jiayuan, 2019. "A graph mining-based methodology for discovering and visualizing high-level knowledge for building energy management," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  9. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
  10. Gleydson de Oliveira Cavalcanti & Handson Claudio Dias Pimenta, 2023. "Electric Energy Management in Buildings Based on the Internet of Things: A Systematic Review," Energies, MDPI, vol. 16(15), pages 1-29, August.
  11. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
  12. Li, Kehua & Ma, Zhenjun & Robinson, Duane & Ma, Jun, 2018. "Identification of typical building daily electricity usage profiles using Gaussian mixture model-based clustering and hierarchical clustering," Applied Energy, Elsevier, vol. 231(C), pages 331-342.
  13. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
  14. Simon P. Melgaard & Kamilla H. Andersen & Anna Marszal-Pomianowska & Rasmus L. Jensen & Per K. Heiselberg, 2022. "Fault Detection and Diagnosis Encyclopedia for Building Systems: A Systematic Review," Energies, MDPI, vol. 15(12), pages 1-50, June.
  15. Cristina Nichiforov & Antonio Martinez-Molina & Miltiadis Alamaniotis, 2021. "An Intelligent Approach for Performing Energy-Driven Classification of Buildings Utilizing Joint Electricity–Gas Patterns," Energies, MDPI, vol. 14(22), pages 1-11, November.
  16. Grillone, Benedetto & Mor, Gerard & Danov, Stoyan & Cipriano, Jordi & Sumper, Andreas, 2021. "A data-driven methodology for enhanced measurement and verification of energy efficiency savings in commercial buildings," Applied Energy, Elsevier, vol. 301(C).
  17. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
  18. Tang, Wenjun & Wang, Hao & Lee, Xian-Long & Yang, Hong-Tzer, 2022. "Machine learning approach to uncovering residential energy consumption patterns based on socioeconomic and smart meter data," Energy, Elsevier, vol. 240(C).
  19. Rongheng Lin & Fangchun Yang & Mingyuan Gao & Budan Wu & Yingying Zhao, 2019. "AUD-MTS: An Abnormal User Detection Approach Based on Power Load Multi-Step Clustering with Multiple Time Scales," Energies, MDPI, vol. 12(16), pages 1-19, August.
  20. Movahed, Paria & Taheri, Saman & Razban, Ali, 2023. "A bi-level data-driven framework for fault-detection and diagnosis of HVAC systems," Applied Energy, Elsevier, vol. 339(C).
  21. Chen, Zhe & Xiao, Fu & Guo, Fangzhou, 2023. "Similarity learning-based fault detection and diagnosis in building HVAC systems with limited labeled data," Renewable and Sustainable Energy Reviews, Elsevier, vol. 185(C).
  22. Zhou, Xinlei & Du, Han & Xue, Shan & Ma, Zhenjun, 2024. "Recent advances in data mining and machine learning for enhanced building energy management," Energy, Elsevier, vol. 307(C).
  23. Aguilar, J. & Garces-Jimenez, A. & R-Moreno, M.D. & García, Rodrigo, 2021. "A systematic literature review on the use of artificial intelligence in energy self-management in smart buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 151(C).
  24. Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
  25. Ciprian Cristea & Maria Cristea & Dan Doru Micu & Andrei Ceclan & Radu-Adrian Tîrnovan & Florica Mioara Șerban, 2022. "Tridimensional Sustainability and Feasibility Assessment of Grid-Connected Solar Photovoltaic Systems Applied for the Technical University of Cluj-Napoca," Sustainability, MDPI, vol. 14(17), pages 1-23, August.
  26. Himeur, Yassine & Ghanem, Khalida & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2021. "Artificial intelligence based anomaly detection of energy consumption in buildings: A review, current trends and new perspectives," Applied Energy, Elsevier, vol. 287(C).
  27. Gao, Ding & Zhi, Yuan & Rong, Xing & Yang, Xudong, 2025. "Mismatch analysis of rooftop photovoltaics supply and farmhouse load: Data dimensionality reduction and explicable load pattern mining via hybrid deep learning," Applied Energy, Elsevier, vol. 377(PB).
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