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Measuring the energy intensity of domestic activities from smart meter data

Author

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  • Stankovic, L.
  • Stankovic, V.
  • Liao, J.
  • Wilson, C.

Abstract

Household electricity consumption can be broken down to appliance end-use through a variety of methods such as modelling, sub-metering, load disaggregation or non-intrusive appliance load monitoring (NILM). We advance and complement this important field of energy research through an innovative methodology that characterises the energy consumption of domestic life by making the linkages between appliance end-use and activities through an ontology built from qualitative data about the household and NILM data. We use activities as a descriptive term for the common ways households spend their time at home. These activities, such as cooking or laundering, are meaningful to households’ own lived experience. Thus, besides strictly technical algorithmic approaches for processing quantitative smart meter data, we also draw on social science time use approaches and interview and ethnography data. Our method disaggregates a households total electricity load down to appliance level and provides the start time, duration, and total electricity consumption for each occurrence of appliance usage. We then make inferences about activities occurring in the home by combining these disaggregated data with an ontology that formally specifies the relationships between electricity-using appliances and activities. We also propose two novel standardised metrics to enable easy quantifiable comparison within and across households of the energy intensity and routine of activities of interest. Finally, we demonstrate our results over a sample of ten households with an in-depth analysis of which activities can be inferred with the qualitative and quantitative data available for each household at any time, and the level of accuracy with which each activity can be inferred, unique to each household. This work has important applications from providing meaningful energy feedback to households to comparing the energy efficiency of households’ daily activities, and exploring the potential to shift the timing of activities for demand management.

Suggested Citation

  • Stankovic, L. & Stankovic, V. & Liao, J. & Wilson, C., 2016. "Measuring the energy intensity of domestic activities from smart meter data," Applied Energy, Elsevier, vol. 183(C), pages 1565-1580.
  • Handle: RePEc:eee:appene:v:183:y:2016:i:c:p:1565-1580
    DOI: 10.1016/j.apenergy.2016.09.087
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    References listed on IDEAS

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    Cited by:

    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. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. 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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