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Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data

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  1. Csereklyei, Zsuzsanna & Anantharama, Nandini & Kallies, Anne, 2021. "Electricity market transitions in Australia: Evidence using model-based clustering," Energy Economics, Elsevier, vol. 103(C).
  2. Huang, Pei & Sun, Yongjun, 2019. "A clustering based grouping method of nearly zero energy buildings for performance improvements," Applied Energy, Elsevier, vol. 235(C), pages 43-55.
  3. Li, Wenqiang & Gong, Guangcai & Fan, Houhua & Peng, Pei & Chun, Liang & Fang, Xi, 2021. "A clustering-based approach for “cross-scale” load prediction on building level in HVAC systems," Applied Energy, Elsevier, vol. 282(PB).
  4. Fu, Xueqian & Zhang, Xiurong, 2019. "Estimation of building energy consumption using weather information derived from photovoltaic power plants," Renewable Energy, Elsevier, vol. 130(C), pages 130-138.
  5. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
  6. Zhong, Hai & Wang, Jiajun & Jia, Hongjie & Mu, Yunfei & Lv, Shilei, 2019. "Vector field-based support vector regression for building energy consumption prediction," Applied Energy, Elsevier, vol. 242(C), pages 403-414.
  7. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
  8. Razavi, Rouzbeh & Gharipour, Amin & Fleury, Martin & Akpan, Ikpe Justice, 2019. "A practical feature-engineering framework for electricity theft detection in smart grids," Applied Energy, Elsevier, vol. 238(C), pages 481-494.
  9. Jafari-Marandi, Ruholla & Hu, Mengqi & Omitaomu, OluFemi A., 2016. "A distributed decision framework for building clusters with different heterogeneity settings," Applied Energy, Elsevier, vol. 165(C), pages 393-404.
  10. Ali Movahedi & Sybil Derrible, 2021. "Interrelationships between electricity, gas, and water consumption in large‐scale buildings," Journal of Industrial Ecology, Yale University, vol. 25(4), pages 932-947, August.
  11. 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).
  12. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
  13. Wang, Xin & Li, Zhengwei & Meng, Haixing & Wu, Jiang, 2017. "Identification of key energy efficiency drivers through global city benchmarking: A data driven approach," Applied Energy, Elsevier, vol. 190(C), pages 18-28.
  14. O’Neill, Zheng & O’Neill, Charles, 2016. "Development of a probabilistic graphical model for predicting building energy performance," Applied Energy, Elsevier, vol. 164(C), pages 650-658.
  15. William Nelson & Charles Culp, 2022. "Machine Learning Methods for Automated Fault Detection and Diagnostics in Building Systems—A Review," Energies, MDPI, vol. 15(15), pages 1-20, July.
  16. Moon Keun Kim & Jaehoon Cha & Eunmi Lee & Van Huy Pham & Sanghyuk Lee & Nipon Theera-Umpon, 2019. "Simplified Neural Network Model Design with Sensitivity Analysis and Electricity Consumption Prediction in a Commercial Building," Energies, MDPI, vol. 12(7), pages 1-13, March.
  17. Hua Chen & Shuang Dai & Fanlin Meng, 2023. "Smart Building Thermal Management: A Data-Driven Approach Based on Dynamic and Consensus Clustering," Sustainability, MDPI, vol. 15(21), pages 1-25, October.
  18. Ma, Weiwu & Fang, Song & Liu, Gang & Zhou, Ruoyu, 2017. "Modeling of district load forecasting for distributed energy system," Applied Energy, Elsevier, vol. 204(C), pages 181-205.
  19. Ciulla, G. & D'Amico, A., 2019. "Building energy performance forecasting: A multiple linear regression approach," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  20. Chengdong Li & Zixiang Ding & Dongbin Zhao & Jianqiang Yi & Guiqing Zhang, 2017. "Building Energy Consumption Prediction: An Extreme Deep Learning Approach," Energies, MDPI, vol. 10(10), pages 1-20, October.
  21. Job Taminiau & John Byrne, 2020. "City‐scale urban sustainability: Spatiotemporal mapping of distributed solar power for New York City," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 9(5), September.
  22. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
  23. 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.
  24. George M. Stavrakakis & Dimitris Bakirtzis & Korina-Konstantina Drakaki & Sofia Yfanti & Dimitris Al. Katsaprakakis & Konstantinos Braimakis & Panagiotis Langouranis & Konstantinos Terzis & Panagiotis, 2024. "Application of the Typology Approach for Energy Renovation Planning of Public Buildings’ Stocks at the Local Level: A Case Study in Greece," Energies, MDPI, vol. 17(3), pages 1-30, January.
  25. Csereklyei, Zsuzsanna & Thurner, Paul W. & Langer, Johannes & Küchenhoff, Helmut, 2017. "Energy paths in the European Union: A model-based clustering approach," Energy Economics, Elsevier, vol. 65(C), pages 442-457.
  26. Papadopoulos, Sokratis & Bonczak, Bartosz & Kontokosta, Constantine E., 2018. "Pattern recognition in building energy performance over time using energy benchmarking data," Applied Energy, Elsevier, vol. 221(C), pages 576-586.
  27. Tarek Rakha & Rawad El Kontar, 2019. "Community energy by design: A simulation-based design workflow using measured data clustering to calibrate Urban Building Energy Models (UBEMs)," Environment and Planning B, , vol. 46(8), pages 1517-1533, October.
  28. Zekić-Sušac Marijana & Scitovski Rudolf & Has Adela, 2018. "Cluster analysis and artificial neural networks in predicting energy efficiency of public buildings as a cost-saving approach," Croatian Review of Economic, Business and Social Statistics, Sciendo, vol. 4(2), pages 57-66, November.
  29. Chou, Jui-Sheng & Ngo, Ngoc-Tri, 2016. "Time series analytics using sliding window metaheuristic optimization-based machine learning system for identifying building energy consumption patterns," Applied Energy, Elsevier, vol. 177(C), pages 751-770.
  30. 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).
  31. Naji, Sareh & Keivani, Afram & Shamshirband, Shahaboddin & Alengaram, U. Johnson & Jumaat, Mohd Zamin & Mansor, Zulkefli & Lee, Malrey, 2016. "Estimating building energy consumption using extreme learning machine method," Energy, Elsevier, vol. 97(C), pages 506-516.
  32. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
  33. Kazas, Georgios & Fabrizio, Enrico & Perino, Marco, 2017. "Energy demand profile generation with detailed time resolution at an urban district scale: A reference building approach and case study," Applied Energy, Elsevier, vol. 193(C), pages 243-262.
  34. Lawal, Abiola S. & Servadio, Joseph L. & Davis, Tate & Ramaswami, Anu & Botchwey, Nisha & Russell, Armistead G., 2021. "Orthogonalization and machine learning methods for residential energy estimation with social and economic indicators," Applied Energy, Elsevier, vol. 283(C).
  35. Chengdong Li & Zixiang Ding & Jianqiang Yi & Yisheng Lv & Guiqing Zhang, 2018. "Deep Belief Network Based Hybrid Model for Building Energy Consumption Prediction," Energies, MDPI, vol. 11(1), pages 1-26, January.
  36. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
  37. Satre-Meloy, Aven & Diakonova, Marina & Grünewald, Philipp, 2020. "Cluster analysis and prediction of residential peak demand profiles using occupant activity data," Applied Energy, Elsevier, vol. 260(C).
  38. Thiago Eliandro de Oliveira Gomes & André Ross Borniatti & Vinícius Jacques Garcia & Laura Lisiane Callai dos Santos & Nelson Knak Neto & Rui Anderson Ferrarezi Garcia, 2023. "Clustering Electrical Customers with Source Power and Aggregation Constraints: A Reliability-Based Approach in Power Distribution Systems," Energies, MDPI, vol. 16(5), pages 1-20, March.
  39. Hribar, Rok & Potočnik, Primož & Šilc, Jurij & Papa, Gregor, 2019. "A comparison of models for forecasting the residential natural gas demand of an urban area," Energy, Elsevier, vol. 167(C), pages 511-522.
  40. 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).
  41. Debnath, Ramit & Bardhan, Ronita & Misra, Ashwin & Hong, Tianzhen & Rozite, Vida & Ramage, Michael H., 2022. "Lockdown impacts on residential electricity demand in India: A data-driven and non-intrusive load monitoring study using Gaussian mixture models," Energy Policy, Elsevier, vol. 164(C).
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