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Electric load shape benchmarking for small- and medium-sized commercial buildings

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  1. Rajabi, Amin & Eskandari, Mohsen & Ghadi, Mojtaba Jabbari & Li, Li & Zhang, Jiangfeng & Siano, Pierluigi, 2020. "A comparative study of clustering techniques for electrical load pattern segmentation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 120(C).
  2. Chen, Yixing & Hong, Tianzhen, 2018. "Impacts of building geometry modeling methods on the simulation results of urban building energy models," Applied Energy, Elsevier, vol. 215(C), pages 717-735.
  3. 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).
  4. Wang, Wei & Jing, Rui & Zhao, Yingru & Zhang, Chuan & Wang, Xiaonan, 2020. "A load-complementarity combined flexible clustering approach for large-scale urban energy-water nexus optimization," Applied Energy, Elsevier, vol. 270(C).
  5. Hou, Jin & Xu, Peng & Lu, Xing & Pang, Zhihong & Chu, Yiyi & Huang, Gongsheng, 2018. "Implementation of expansion planning in existing district energy system: A case study in China," Applied Energy, Elsevier, vol. 211(C), pages 269-281.
  6. Chen, Yibo & Wu, Jianzhong, 2018. "Distribution patterns of energy consumed in classified public buildings through the data mining process," Applied Energy, Elsevier, vol. 226(C), pages 240-251.
  7. Fernanda Spada Villar & Pedro Henrique Juliano Nardelli & Arun Narayanan & Renan Cipriano Moioli & Hader Azzini & Luiz Carlos Pereira da Silva, 2021. "Noninvasive Detection of Appliance Utilization Patterns in Residential Electricity Demand," Energies, MDPI, vol. 14(6), pages 1-23, March.
  8. 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.
  9. Lee, Junghun & Kim, Seohoon & Kim, Jonghun & Song, Doosam & Jeong, Hakgeun, 2018. "Thermal performance evaluation of low-income buildings based on indoor temperature performance," Applied Energy, Elsevier, vol. 221(C), pages 425-436.
  10. 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.
  11. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
  12. Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
  13. Shaik, Saleem & Yeboah, Osei-Agyeman, 2018. "Does climate influence energy demand? A regional analysis," Applied Energy, Elsevier, vol. 212(C), pages 691-703.
  14. Rosales-Asensio, Enrique & de Simón-Martín, Miguel & Borge-Diez, David & Blanes-Peiró, Jorge Juan & Colmenar-Santos, Antonio, 2019. "Microgrids with energy storage systems as a means to increase power resilience: An application to office buildings," Energy, Elsevier, vol. 172(C), pages 1005-1015.
  15. Henni, Sarah & Staudt, Philipp & Weinhardt, Christof, 2021. "A sharing economy for residential communities with PV-coupled battery storage: Benefits, pricing and participant matching," Applied Energy, Elsevier, vol. 301(C).
  16. 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).
  17. Jing, Rui & Wang, Meng & Zhang, Zhihui & Wang, Xiaonan & Li, Ning & Shah, Nilay & Zhao, Yingru, 2019. "Distributed or centralized? Designing district-level urban energy systems by a hierarchical approach considering demand uncertainties," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
  18. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
  19. Piscitelli, Marco Savino & Giudice, Rocco & Capozzoli, Alfonso, 2024. "A holistic time series-based energy benchmarking framework for applications in large stocks of buildings," Applied Energy, Elsevier, vol. 357(C).
  20. Wang, Zhikun & Crawley, Jenny & Li, Francis G.N. & Lowe, Robert, 2020. "Sizing of district heating systems based on smart meter data: Quantifying the aggregated domestic energy demand and demand diversity in the UK," Energy, Elsevier, vol. 193(C).
  21. Li, Wenzhuo & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Cha, Seung Hyun & Wang, Shengwei, 2020. "A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  22. Li, Han & Wang, Zhe & Hong, Tianzhen & Parker, Andrew & Neukomm, Monica, 2021. "Characterizing patterns and variability of building electric load profiles in time and frequency domains," Applied Energy, Elsevier, vol. 291(C).
  23. Koo, Choongwan & Li, Wenzhuo & Cha, Seung Hyun & Zhang, Shaojie, 2019. "A novel estimation approach for the solar radiation potential with its complex spatial pattern via machine-learning techniques," Renewable Energy, Elsevier, vol. 133(C), pages 575-592.
  24. Claudio Caromba & Corné Schutte & Jean van Laar, 2023. "Application of Clustering Techniques for Improved Energy Benchmarking on Deep-Level Mines," Energies, MDPI, vol. 16(19), pages 1-18, September.
  25. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
  26. Lee, Junsoo & Kim, Tae Wan & Koo, Choongwan, 2022. "A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
  27. Ye, Chengjin & Ding, Yi & Song, Yonghua & Lin, Zhenzhi & Wang, Lei, 2018. "A data driven multi-state model for distribution system flexible planning utilizing hierarchical parallel computing," Applied Energy, Elsevier, vol. 232(C), pages 9-25.
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