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Measuring the energy intensity of domestic activities from smart meter data

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  1. Yang, Lu & Xie, Pengli & Bi, Chongke & Zhang, Ronghui & Cai, Bowen & Shao, Xiaowei & Wang, Rongben, 2020. "Household power consumption pattern modeling through a single power sensor," Renewable Energy, Elsevier, vol. 155(C), pages 121-133.
  2. Liu, Chao & Akintayo, Adedotun & Jiang, Zhanhong & Henze, Gregor P. & Sarkar, Soumik, 2018. "Multivariate exploration of non-intrusive load monitoring via spatiotemporal pattern network," Applied Energy, Elsevier, vol. 211(C), pages 1106-1122.
  3. 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).
  4. De Lauretis, Simona & Ghersi, Frédéric & Cayla, Jean-Michel, 2017. "Energy consumption and activity patterns: An analysis extended to total time and energy use for French households," Applied Energy, Elsevier, vol. 206(C), pages 634-648.
  5. Violeta Mihaela Dincă & Mihail Busu & Zoltan Nagy-Bege, 2022. "Determinants with Impact on Romanian Consumers’ Energy-Saving Habits," Energies, MDPI, vol. 15(11), pages 1-18, June.
  6. Besagni, Giorgio & Borgarello, Marco, 2018. "The determinants of residential energy expenditure in Italy," Energy, Elsevier, vol. 165(PA), pages 369-386.
  7. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
  8. Dinesh, Chinthaka & Welikala, Shirantha & Liyanage, Yasitha & Ekanayake, Mervyn Parakrama B. & Godaliyadda, Roshan Indika & Ekanayake, Janaka, 2017. "Non-intrusive load monitoring under residential solar power influx," Applied Energy, Elsevier, vol. 205(C), pages 1068-1080.
  9. Li, Dandan & Li, Jiangfeng & Zeng, Xin & Stankovic, Vladimir & Stankovic, Lina & Xiao, Changjiang & Shi, Qingjiang, 2023. "Transfer learning for multi-objective non-intrusive load monitoring in smart building," Applied Energy, Elsevier, vol. 329(C).
  10. 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.
  11. Song, Chunhe & Jing, Wei & Zeng, Peng & Yu, Haibin & Rosenberg, Catherine, 2018. "Energy consumption analysis of residential swimming pools for peak load shaving," Applied Energy, Elsevier, vol. 220(C), pages 176-191.
  12. Kwok Tai Chui & Miltiadis D. Lytras & Anna Visvizi, 2018. "Energy Sustainability in Smart Cities: Artificial Intelligence, Smart Monitoring, and Optimization of Energy Consumption," Energies, MDPI, vol. 11(11), pages 1-20, October.
  13. Todic, Tamara & Stankovic, Vladimir & Stankovic, Lina, 2023. "An active learning framework for the low-frequency Non-Intrusive Load Monitoring problem," Applied Energy, Elsevier, vol. 341(C).
  14. Du, Feng & Yue, Hong & Zhang, Jiangfeng, 2023. "Influence of advertisement control to residential energy savings in large networks," Applied Energy, Elsevier, vol. 333(C).
  15. 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.
  16. Zhao, Bochao & Ye, Minxiang & Stankovic, Lina & Stankovic, Vladimir, 2020. "Non-intrusive load disaggregation solutions for very low-rate smart meter data," Applied Energy, Elsevier, vol. 268(C).
  17. Vavouris, Apostolos & Guasselli, Fernanda & Stankovic, Lina & Stankovic, Vladimir & Gram-Hanssen, Kirsten & Didierjean, Sébastien, 2024. "A complex mixed-methods data-driven energy-centric evaluation of net-positive households," Applied Energy, Elsevier, vol. 367(C).
  18. Ru-Guan Wang & Wen-Jen Ho & Kuei-Chun Chiang & Yung-Chieh Hung & Jen-Kuo Tai & Jia-Cheng Tan & Mei-Ling Chuang & Chi-Yun Ke & Yi-Fan Chien & An-Ping Jeng & Chien-Cheng Chou, 2023. "Analyzing Long-Term and High Instantaneous Power Consumption of Buildings from Smart Meter Big Data with Deep Learning and Knowledge Graph Techniques," Energies, MDPI, vol. 16(19), pages 1-24, September.
  19. 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.
  20. Shajalal, Md & Boden, Alexander & Stevens, Gunnar, 2024. "ForecastExplainer: Explainable household energy demand forecasting by approximating shapley values using DeepLIFT," Technological Forecasting and Social Change, Elsevier, vol. 206(C).
  21. Máté János Lőrincz & José Luis Ramírez-Mendiola & Jacopo Torriti, 2021. "Impact of Time-Use Behaviour on Residential Energy Consumption in the United Kingdom," Energies, MDPI, vol. 14(19), pages 1-32, October.
  22. Alexandre Lucas & Luca Jansen & Nikoleta Andreadou & Evangelos Kotsakis & Marcelo Masera, 2019. "Load Flexibility Forecast for DR Using Non-Intrusive Load Monitoring in the Residential Sector," Energies, MDPI, vol. 12(14), pages 1-19, July.
  23. Ahmadi-Karvigh, Simin & Ghahramani, Ali & Becerik-Gerber, Burcin & Soibelman, Lucio, 2018. "Real-time activity recognition for energy efficiency in buildings," Applied Energy, Elsevier, vol. 211(C), pages 146-160.
  24. Lesley Thomson & David Jenkins, 2023. "The Use of Real Energy Consumption Data in Characterising Residential Energy Demand with an Inventory of UK Datasets," Energies, MDPI, vol. 16(16), pages 1-29, August.
  25. Rashid, Haroon & Singh, Pushpendra & Stankovic, Vladimir & Stankovic, Lina, 2019. "Can non-intrusive load monitoring be used for identifying an appliance’s anomalous behaviour?," Applied Energy, Elsevier, vol. 238(C), pages 796-805.
  26. Wang, Shuangyuan & Li, Ran & Evans, Adrian & Li, Furong, 2020. "Regional nonintrusive load monitoring for low voltage substations and distributed energy resources," Applied Energy, Elsevier, vol. 260(C).
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