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A clustering approach to domestic electricity load profile characterisation using smart metering data

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  1. 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).
  2. 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.
  3. Guo, Zhifeng & O'Hanley, Jesse R. & Gibson, Stuart, 2022. "Predicting residential electricity consumption patterns based on smart meter and household data: A case study from the Republic of Ireland," Utilities Policy, Elsevier, vol. 79(C).
  4. Chen, Zhiqiang & Li, Jianbin & Cheng, Long & Liu, Xiufeng, 2023. "Federated-WDCGAN: A federated smart meter data sharing framework for privacy preservation," Applied Energy, Elsevier, vol. 334(C).
  5. 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.
  6. 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).
  7. Fateh Belaid & Christophe Rault, 2020. "Energy Expenditure in Egypt: Empirical Evidence Based on A Quantile Regression Approach," Working Papers 1446, Economic Research Forum, revised 20 Dec 2020.
  8. 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.
  9. Yilmaz, S. & Chambers, J. & Patel, M.K., 2019. "Comparison of clustering approaches for domestic electricity load profile characterisation - Implications for demand side management," Energy, Elsevier, vol. 180(C), pages 665-677.
  10. Afzalan, Milad & Jazizadeh, Farrokh, 2019. "Residential loads flexibility potential for demand response using energy consumption patterns and user segments," Applied Energy, Elsevier, vol. 254(C).
  11. Al-Wakeel, Ali & Wu, Jianzhong & Jenkins, Nick, 2016. "State estimation of medium voltage distribution networks using smart meter measurements," Applied Energy, Elsevier, vol. 184(C), pages 207-218.
  12. Wang, Yanmin & Li, Zhiwei & Liu, Junjie & Lu, Xuan & Zhao, Laifu & Zhao, Yan & Feng, Yongtao, 2024. "Analyzing daily change patterns of indoor temperature in district heating systems: A clustering and regression approach," Applied Energy, Elsevier, vol. 358(C).
  13. Juanwei, Chen & Tao, Yu & Yue, Xu & Xiaohua, Cheng & Bo, Yang & Baomin, Zhen, 2019. "Fast analytical method for reliability evaluation of electricity-gas integrated energy system considering dispatch strategies," Applied Energy, Elsevier, vol. 242(C), pages 260-272.
  14. 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.
  15. Motlagh, Omid & Berry, Adam & O'Neil, Lachlan, 2019. "Clustering of residential electricity customers using load time series," Applied Energy, Elsevier, vol. 237(C), pages 11-24.
  16. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
  17. Huang, Yunyou & Zhan, Jianfeng & Luo, Chunjie & Wang, Lei & Wang, Nana & Zheng, Daoyi & Fan, Fanda & Ren, Rui, 2019. "An electricity consumption model for synthesizing scalable electricity load curves," Energy, Elsevier, vol. 169(C), pages 674-683.
  18. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
  19. Li, Jianbin & Chen, Zhiqiang & Cheng, Long & Liu, Xiufeng, 2022. "Energy data generation with Wasserstein Deep Convolutional Generative Adversarial Networks," Energy, Elsevier, vol. 257(C).
  20. 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.
  21. Wang, Xuewei & Wang, Jing & Wang, Lin & Yuan, Ruiming, 2019. "Non-overlapping moving compressive measurement algorithm for electrical energy estimation of distorted m-sequence dynamic test signal," Applied Energy, Elsevier, vol. 251(C), pages 1-1.
  22. Li, Tong & Wang, Zhaohua & Zhao, Wenhui, 2022. "Comparison and application potential analysis of autoencoder-based electricity pattern mining algorithms for large-scale demand response," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
  23. 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.
  24. Alexander Martin Tureczek & Per Sieverts Nielsen, 2017. "Structured Literature Review of Electricity Consumption Classification Using Smart Meter Data," Energies, MDPI, vol. 10(5), pages 1-19, April.
  25. 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.
  26. 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).
  27. 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.
  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. Claudia Bustamante & Stephen Bird & Lisa Legault & Susan E. Powers, 2023. "Energy Hogs and Misers: Magnitude and Variability of Individuals’ Household Electricity Consumption," Sustainability, MDPI, vol. 15(5), pages 1-18, February.
  30. Hubert Szczepaniuk & Edyta Karolina Szczepaniuk, 2022. "Applications of Artificial Intelligence Algorithms in the Energy Sector," Energies, MDPI, vol. 16(1), pages 1-24, December.
  31. 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.
  32. 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).
  33. Anam-Nawaz Khan & Naeem Iqbal & Atif Rizwan & Rashid Ahmad & Do-Hyeun Kim, 2021. "An Ensemble Energy Consumption Forecasting Model Based on Spatial-Temporal Clustering Analysis in Residential Buildings," Energies, MDPI, vol. 14(11), pages 1-25, May.
  34. Lumbreras, Mikel & Garay-Martinez, Roberto & Arregi, Beñat & Martin-Escudero, Koldobika & Diarce, Gonzalo & Raud, Margus & Hagu, Indrek, 2022. "Data driven model for heat load prediction in buildings connected to District Heating by using smart heat meters," Energy, Elsevier, vol. 239(PD).
  35. Do-Hyeon Ryu & Ryu-Hee Kim & Seung-Hyun Choi & Kwang-Jae Kim & Young Myoung Ko & Young-Jin Kim & Minseok Song & Dong Gu Choi, 2020. "Utilizing Electricity Consumption Data to Assess the Noise Discomfort Caused by Electrical Appliances between Neighbors: A Case Study of a Campus Apartment Building," Sustainability, MDPI, vol. 12(20), pages 1-16, October.
  36. Liu, Bo & Hou, Yufan & Luan, Wenpeng & Liu, Zishuai & Chen, Sheng & Yu, Yixin, 2023. "A divide-and-conquer method for compression and reconstruction of smart meter data," Applied Energy, Elsevier, vol. 336(C).
  37. 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.
  38. Többen, Johannes & Schröder, Thomas, 2018. "A maximum entropy approach to the estimation of spatially and sectorally disaggregated electricity load curves," Applied Energy, Elsevier, vol. 225(C), pages 797-813.
  39. Bertsch, Valentin & Geldermann, Jutta & Lühn, Tobias, 2017. "What drives the profitability of household PV investments, self-consumption and self-sufficiency?," Applied Energy, Elsevier, vol. 204(C), pages 1-15.
  40. 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.
  41. 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.
  42. 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.
  43. 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.
  44. Giasemidis, Georgios & Haben, Stephen & Lee, Tamsin & Singleton, Colin & Grindrod, Peter, 2017. "A genetic algorithm approach for modelling low voltage network demands," Applied Energy, Elsevier, vol. 203(C), pages 463-473.
  45. 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).
  46. 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).
  47. Huang, Ke & Yuan, Jianjuan & Zhou, Zhihua & Zheng, Xuejing, 2022. "Analysis and evaluation of heat source data of large-scale heating system based on descriptive data mining techniques," Energy, Elsevier, vol. 251(C).
  48. 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.
  49. Troy Malatesta & Jessica K. Breadsell, 2022. "Identifying Home System of Practices for Energy Use with K-Means Clustering Techniques," Sustainability, MDPI, vol. 14(15), pages 1-21, July.
  50. Dominik Kryzia & Marta Kuta & Dominika Matuszewska & Piotr Olczak, 2020. "Analysis of the Potential for Gas Micro-Cogeneration Development in Poland Using the Monte Carlo Method," Energies, MDPI, vol. 13(12), pages 1-24, June.
  51. 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).
  52. Ruhang, Xu, 2020. "Efficient clustering for aggregate loads: An unsupervised pretraining based method," Energy, Elsevier, vol. 210(C).
  53. Troy Malatesta & Qilin Li & Jessica K. Breadsell & Christine Eon, 2023. "Distinguishing Household Groupings within a Precinct Based on Energy Usage Patterns Using Machine Learning Analysis," Energies, MDPI, vol. 16(10), pages 1-25, May.
  54. 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.
  55. 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).
  56. 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).
  57. Yuan, Jianjuan & Zhou, Zhihua & Tang, Huajie & Wang, Chendong & Lu, Shilei & Han, Zhao & Zhang, Ji & Sheng, Ying, 2020. "Identification heat user behavior for improving the accuracy of heating load prediction model based on wireless on-off control system," Energy, Elsevier, vol. 199(C).
  58. 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).
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  63. 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.
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