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Clustering analysis of residential electricity demand profiles

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  1. Viegas, Joaquim L. & Vieira, Susana M. & Melício, R. & Mendes, V.M.F. & Sousa, João M.C., 2016. "Classification of new electricity customers based on surveys and smart metering data," Energy, Elsevier, vol. 107(C), pages 804-817.
  2. McKenna, R. & Djapic, P. & Weinand, J. & Fichtner, W. & Strbac, G., 2018. "Assessing the implications of socioeconomic diversity for low carbon technology uptake in electrical distribution networks," Applied Energy, Elsevier, vol. 210(C), pages 856-869.
  3. 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.
  4. 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).
  5. Jacqueline Nicole Adams & Zsófia Deme Bélafi & Miklós Horváth & János Balázs Kocsis & Tamás Csoknyai, 2021. "How Smart Meter Data Analysis Can Support Understanding the Impact of Occupant Behavior on Building Energy Performance: A Comprehensive Review," Energies, MDPI, vol. 14(9), pages 1-23, April.
  6. Sachs, Julia & Moya, Diego & Giarola, Sara & Hawkes, Adam, 2019. "Clustered spatially and temporally resolved global heat and cooling energy demand in the residential sector," Applied Energy, Elsevier, vol. 250(C), pages 48-62.
  7. 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).
  8. Akito Ozawa & Ryota Furusato & Yoshikuni Yoshida, 2017. "Tailor-Made Feedback to Reduce Residential Electricity Consumption: The Effect of Information on Household Lifestyle in Japan," Sustainability, MDPI, vol. 9(4), pages 1-23, March.
  9. Luo, Xuan & Hong, Tianzhen & Chen, Yixing & Piette, Mary Ann, 2017. "Electric load shape benchmarking for small- and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 204(C), pages 715-725.
  10. Eunjung Lee & Jinho Kim & Dongsik Jang, 2020. "Load Profile Segmentation for Effective Residential Demand Response Program: Method and Evidence from Korean Pilot Study," Energies, MDPI, vol. 13(6), pages 1-18, March.
  11. Dong Gu Choi & Michael K. Lim & Karthik Murali & Valerie M. Thomas, 2020. "Why Have Voluntary Time‐of‐Use Tariffs Fallen Short in the Residential Sector?," Production and Operations Management, Production and Operations Management Society, vol. 29(3), pages 617-642, March.
  12. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
  13. Roberts, Mike B. & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2019. "Characterisation of Australian apartment electricity demand and its implications for low-carbon cities," Energy, Elsevier, vol. 180(C), pages 242-257.
  14. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
  15. Ye, Tinghan & Liu, Shanshan & Kontou, Eleftheria, 2024. "Managed residential electric vehicle charging minimizes electricity bills while meeting driver and community preferences," Transport Policy, Elsevier, vol. 149(C), pages 122-138.
  16. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
  17. Leibowicz, Benjamin D. & Lanham, Christopher M. & Brozynski, Max T. & Vázquez-Canteli, José R. & Castejón, Nicolás Castillo & Nagy, Zoltan, 2018. "Optimal decarbonization pathways for urban residential building energy services," Applied Energy, Elsevier, vol. 230(C), pages 1311-1325.
  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. Majid Hashemi, 2021. "The Effect of Reliability Improvements on Household Electricity Consumption and Coping Behavior: A Multi-dimensional Approach," Working Paper 1469, Economics Department, Queen's University.
  20. Rongheng Lin & Zezhou Ye & Yingying Zhao, 2019. "OPEC: Daily Load Data Analysis Based on Optimized Evolutionary Clustering," Energies, MDPI, vol. 12(14), pages 1-17, July.
  21. Klemm, Christian & Wiese, Frauke & Vennemann, Peter, 2023. "Model-based run-time and memory reduction for a mixed-use multi-energy system model with high spatial resolution," Applied Energy, Elsevier, vol. 334(C).
  22. Rafik Nafkha & Krzysztof Gajowniczek & Tomasz Ząbkowski, 2018. "Do Customers Choose Proper Tariff? Empirical Analysis Based on Polish Data Using Unsupervised Techniques," Energies, MDPI, vol. 11(3), pages 1-17, February.
  23. Kang, J. & Reiner, D., 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Cambridge Working Papers in Economics 2143, Faculty of Economics, University of Cambridge.
  24. Pesantez, Jorge E. & Li, Binbin & Lee, Christopher & Zhao, Zhizhen & Butala, Mark & Stillwell, Ashlynn S., 2023. "A Comparison Study of Predictive Models for Electricity Demand in a Diverse Urban Environment," Energy, Elsevier, vol. 283(C).
  25. Tanoto, Yusak & Haghdadi, Navid & Bruce, Anna & MacGill, Iain, 2020. "Clustering based assessment of cost, security and environmental tradeoffs with possible future electricity generation portfolios," Applied Energy, Elsevier, vol. 270(C).
  26. 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.
  27. Moral-Carcedo, Julián & Pérez-García, Julián, 2015. "Temperature effects on firms’ electricity demand: An analysis of sectorial differences in Spain," Applied Energy, Elsevier, vol. 142(C), pages 407-425.
  28. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  29. Vallés, Mercedes & Bello, Antonio & Reneses, Javier & Frías, Pablo, 2018. "Probabilistic characterization of electricity consumer responsiveness to economic incentives," Applied Energy, Elsevier, vol. 216(C), pages 296-310.
  30. Barkha Parkash & Tek Tjing Lie & Weihua Li & Shafiqur Rahman Tito, 2024. "End-to-End Top-Down Load Forecasting Model for Residential Consumers," Energies, MDPI, vol. 17(11), pages 1-20, May.
  31. Bandyopadhyay, Arkasama & Leibowicz, Benjamin D. & Webber, Michael E., 2021. "Solar panels and smart thermostats: The power duo of the residential sector?," Applied Energy, Elsevier, vol. 290(C).
  32. Hsu, David, 2015. "Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data," Applied Energy, Elsevier, vol. 160(C), pages 153-163.
  33. Qiu, Dawei & Wang, Yi & Wang, Junkai & Jiang, Chuanwen & Strbac, Goran, 2023. "Personalized retail pricing design for smart metering consumers in electricity market," Applied Energy, Elsevier, vol. 348(C).
  34. Yang Yu & Guangyi Liu & Wendong Zhu & Fei Wang & Bin Shu & Kai Zhang & Ram Rajagopal & Nicolas Astier, 2016. "Economic information from Smart Meter: Nexus Between Demand Profile and Electricity Retail Price Between Demand Profile and Electricity Retail Price," Papers 1701.02646, arXiv.org.
  35. Tang, Rui & Yildiz, Baran & Leong, Philip H.W. & Vassallo, Anthony & Dore, Jonathon, 2019. "Residential battery sizing model using net meter energy data clustering," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  36. Huang, Ransisi & Mahvi, Allison & James, Nelson & Kozubal, Eric & Woods, Jason, 2024. "Evaluation of phase change thermal storage in a cascade heat pump," Applied Energy, Elsevier, vol. 359(C).
  37. Robbert Claeys & Hakim Azaioud & Rémy Cleenwerck & Jos Knockaert & Jan Desmet, 2020. "A Novel Feature Set for Low-Voltage Consumers, Based on the Temporal Dependence of Consumption and Peak Demands," Energies, MDPI, vol. 14(1), pages 1-24, December.
  38. Sun, Mei & Li, Juan & Gao, Cuixia & Han, Dun, 2017. "Identifying regime shifts in the US electricity market based on price fluctuations," Applied Energy, Elsevier, vol. 194(C), pages 658-666.
  39. Wang, Chao & Du, Yuyan & Li, Hailong & Wallin, Fredrik & Min, Geyong, 2019. "New methods for clustering district heating users based on consumption patterns," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  40. 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.
  41. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
  42. Ang, Yu Qian & Berzolla, Zachary Michael & Reinhart, Christoph F., 2020. "From concept to application: A review of use cases in urban building energy modeling," Applied Energy, Elsevier, vol. 279(C).
  43. Frankel, Matthew & Xing, Lu & Chewning, Connor & Sela, Lina, 2021. "Water-energy benchmarking and predictive modeling in multi-family residential and non-residential buildings," Applied Energy, Elsevier, vol. 281(C).
  44. 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.
  45. García, Sebastián & Parejo, Antonio & Personal, Enrique & Ignacio Guerrero, Juan & Biscarri, Félix & León, Carlos, 2021. "A retrospective analysis of the impact of the COVID-19 restrictions on energy consumption at a disaggregated level," Applied Energy, Elsevier, vol. 287(C).
  46. Zhou, Kaile & Yang, Changhui & Shen, Jianxin, 2017. "Discovering residential electricity consumption patterns through smart-meter data mining: A case study from China," Utilities Policy, Elsevier, vol. 44(C), pages 73-84.
  47. Yu, Xinran & Ergan, Semiha, 2022. "Estimating power demand shaving capacity of buildings on an urban scale using extracted demand response profiles through machine learning models," Applied Energy, Elsevier, vol. 310(C).
  48. Wen, Hanguan & Liu, Xiufeng & Yang, Ming & Lei, Bo & Xu, Cheng & Chen, Zhe, 2024. "A novel approach for identifying customer groups for personalized demand-side management services using household socio-demographic data," Energy, Elsevier, vol. 286(C).
  49. Cansino, José M. & Dugo, Víctor & Gálvez-Ruiz, David & Román-Collado, Rocío, 2023. "What drove electricity consumption in the residential sector during the SARS-CoV-2 confinement? A special focus on university students in southern Spain," Energy, Elsevier, vol. 262(PB).
  50. Rongheng Lin & Budan Wu & Yun Su, 2018. "An Adaptive Weighted Pearson Similarity Measurement Method for Load Curve Clustering," Energies, MDPI, vol. 11(9), pages 1-17, September.
  51. Gianluca Trotta & Kirsten Gram-Hanssen & Pernille Lykke Jørgensen, 2020. "Heterogeneity of Electricity Consumption Patterns in Vulnerable Households," Energies, MDPI, vol. 13(18), pages 1-17, September.
  52. Russo, Marianna & Bertsch, Valentin, 2020. "A looming revolution: Implications of self-generation for the risk exposure of retailers," Energy Economics, Elsevier, vol. 92(C).
  53. Yildiz, B. & Bilbao, J.I. & Dore, J. & Sproul, A.B., 2017. "Recent advances in the analysis of residential electricity consumption and applications of smart meter data," Applied Energy, Elsevier, vol. 208(C), pages 402-427.
  54. 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.
  55. Gouveia, João Pedro & Seixas, Júlia & Mestre, Ana, 2017. "Daily electricity consumption profiles from smart meters - Proxies of behavior for space heating and cooling," Energy, Elsevier, vol. 141(C), pages 108-122.
  56. 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).
  57. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2017. "k-means based load estimation of domestic smart meter measurements," Applied Energy, Elsevier, vol. 194(C), pages 333-342.
  58. Yang, Ying & Campana, Pietro Elia & Yan, Jinyue, 2020. "Potential of unsubsidized distributed solar PV to replace coal-fired power plants, and profits classification in Chinese cities," Renewable and Sustainable Energy Reviews, Elsevier, vol. 131(C).
  59. Benjamin Auder & Jairo Cugliari & Yannig Goude & Jean-Michel Poggi, 2018. "Scalable Clustering of Individual Electrical Curves for Profiling and Bottom-Up Forecasting," Energies, MDPI, vol. 11(7), pages 1-22, July.
  60. Zhang, Xiaohai & Ramírez-Mendiola, José Luis & Li, Mingtao & Guo, Liejin, 2022. "Electricity consumption pattern analysis beyond traditional clustering methods: A novel self-adapting semi-supervised clustering method and application case study," Applied Energy, Elsevier, vol. 308(C).
  61. 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).
  62. Matteo Moncecchi & Alessandro Borselli & Davide Falabretti & Lorenzo Corghi & Marco Merlo, 2020. "Numerical and Experimental Efficiency Estimation in Household Battery Energy Storage Equipment," Energies, MDPI, vol. 13(11), pages 1-19, May.
  63. 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.
  64. Guo, Li & Hou, Ruosong & Liu, Yixin & Wang, Chengshan & Lu, Hai, 2020. "A novel typical day selection method for the robust planning of stand-alone wind-photovoltaic-diesel-battery microgrid," Applied Energy, Elsevier, vol. 263(C).
  65. Hao, Ying & Dong, Lei & Liao, Xiaozhong & Liang, Jun & Wang, Lijie & Wang, Bo, 2019. "A novel clustering algorithm based on mathematical morphology for wind power generation prediction," Renewable Energy, Elsevier, vol. 136(C), pages 572-585.
  66. Mario Flor & Sergio Herraiz & Ivan Contreras, 2021. "Definition of Residential Power Load Profiles Clusters Using Machine Learning and Spatial Analysis," Energies, MDPI, vol. 14(20), pages 1-15, October.
  67. He, Yi & Guo, Su & Zhou, Jianxu & Song, Guotao & Kurban, Aynur & Wang, Haowei, 2022. "The multi-stage framework for optimal sizing and operation of hybrid electrical-thermal energy storage system," Energy, Elsevier, vol. 245(C).
  68. 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).
  69. Yang, Ting & Ren, Minglun & Zhou, Kaile, 2018. "Identifying household electricity consumption patterns: A case study of Kunshan, China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 91(C), pages 861-868.
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