IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v13y2020i18p4713-d411468.html
   My bibliography  Save this article

Heterogeneity of Electricity Consumption Patterns in Vulnerable Households

Author

Listed:
  • Gianluca Trotta

    (Department of the Built Environment, The Faculty of Engineering and Science, Aalborg University Copenhagen, A.C. Meyers Vænge 15, 2450 Copenhagen, Denmark)

  • Kirsten Gram-Hanssen

    (Department of the Built Environment, The Faculty of Engineering and Science, Aalborg University Copenhagen, A.C. Meyers Vænge 15, 2450 Copenhagen, Denmark)

  • Pernille Lykke Jørgensen

    (Department of the Built Environment, The Faculty of Engineering and Science, Aalborg University Copenhagen, A.C. Meyers Vænge 15, 2450 Copenhagen, Denmark)

Abstract

A key aspect of the design of specific tariff structures is to identify and characterize homogeneous electricity consumption profiles. Recent research in residential electricity demand has explored load profile segmentation via cluster analysis combined with descriptive data from the dwelling and occupants, which has partly explained electricity load patterns and their underlying drivers but has failed to investigate any consumption heterogeneity among similar households. Thus, the aim of this paper is to reverse this approach and investigate the extent that households with similar characteristics have different electricity consumption patterns. This study combines population-based register data with hourly electricity consumption data for a sample of 67 Danish households. First, a homogenous household group is selected based on several indicators that signal vulnerability. The specific group under investigation is single-person, older, low-income households in detached housing. Second, K-means clustering is used to identify similarities and differences in consumption patterns. The results indicate four distinct vulnerable household profiles characterized by different start and end times of peak and off-peak times, peak intensities, and overall consumption, which vary across seasons. These profiles are discussed concerning the performance of everyday practices and the design of demand-side management strategies targeted at vulnerable households.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4713-:d:411468
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/13/18/4713/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/13/18/4713/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. González-Eguino, Mikel, 2015. "Energy poverty: An overview," Renewable and Sustainable Energy Reviews, Elsevier, vol. 47(C), pages 377-385.
    2. Hobman, Elizabeth V. & Frederiks, Elisha R. & Stenner, Karen & Meikle, Sarah, 2016. "Uptake and usage of cost-reflective electricity pricing: Insights from psychology and behavioural economics," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 455-467.
    3. Jonas Katz & Lena Kitzing & Sascha T. Schröder & Frits Møller Andersen & Poul Erik Morthorst & Morten Stryg, 2018. "Household electricity consumers’ incentive to choose dynamic pricing under different taxation schemes," Wiley Interdisciplinary Reviews: Energy and Environment, Wiley Blackwell, vol. 7(1), January.
    4. 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.
    5. 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.
    6. Qiu, Yueming & Colson, Gregory & Wetzstein, Michael E., 2017. "Risk preference and adverse selection for participation in time-of-use electricity pricing programs," Resource and Energy Economics, Elsevier, vol. 47(C), pages 126-142.
    7. Anna Alberini, Olha Khymych, and Milan `casný, 2019. "Response to Extreme Energy Price Changes: Evidence from Ukraine," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    8. Christoph Flath & David Nicolay & Tobias Conte & Clemens Dinther & Lilia Filipova-Neumann, 2012. "Cluster Analysis of Smart Metering Data," Business & Information Systems Engineering: The International Journal of WIRTSCHAFTSINFORMATIK, Springer;Gesellschaft für Informatik e.V. (GI), vol. 4(1), pages 31-39, February.
    9. Hansen, Anders Rhiger, 2018. "Heating homes: Understanding the impact of prices," Energy Policy, Elsevier, vol. 121(C), pages 138-151.
    10. Lee V. White & Nicole D. Sintov, 2020. "Health and financial impacts of demand-side response measures differ across sociodemographic groups," Nature Energy, Nature, vol. 5(1), pages 50-60, January.
    11. Jones, Rory V. & Fuertes, Alba & Lomas, Kevin J., 2015. "The socio-economic, dwelling and appliance related factors affecting electricity consumption in domestic buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 43(C), pages 901-917.
    12. Trotta, Gianluca, 2018. "Factors affecting energy-saving behaviours and energy efficiency investments in British households," Energy Policy, Elsevier, vol. 114(C), pages 529-539.
    13. Anna Makles, 2012. "Stata tip 110: How to get the optimal k-means cluster solution," Stata Journal, StataCorp LP, vol. 12(2), pages 347-351, June.
    14. Anna Alberini & Andrea Bigano & Marco Boeri, 2013. "Looking for Free-riding: Energy Efficiency Incentives and Italian Homeowners," Working Papers 2013.24, Fondazione Eni Enrico Mattei.
    15. 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.
    16. Katrina Jessoe & David Rapson, 2014. "Knowledge Is (Less) Power: Experimental Evidence from Residential Energy Use," American Economic Review, American Economic Association, vol. 104(4), pages 1417-1438, April.
    17. 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).
    18. Danila Longo & Giulia Olivieri & Rossella Roversi & Giulia Turci & Beatrice Turillazzi, 2020. "Energy Poverty and Protection of Vulnerable Consumers. Overview of the EU Funding Programs FP7 and H2020 and Future Trends in Horizon Europe," Energies, MDPI, vol. 13(5), pages 1-17, February.
    19. McLoughlin, Fintan & Duffy, Aidan & Conlon, Michael, 2015. "A clustering approach to domestic electricity load profile characterisation using smart metering data," Applied Energy, Elsevier, vol. 141(C), pages 190-199.
    20. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    21. Olivia Ricci & Legendre Bérangère, 2014. "Measuring Fuel Poverty in France: Which Households Are the Most Vulnerable?," EcoMod2014 6923, EcoMod.
    22. Trotta, Gianluca, 2018. "The determinants of energy efficient retrofit investments in the English residential sector," Energy Policy, Elsevier, vol. 120(C), pages 175-182.
    23. Christensen, Toke Haunstrup & Friis, Freja & Bettin, Steffen & Throndsen, William & Ornetzeder, Michael & Skjølsvold, Tomas Moe & Ryghaug, Marianne, 2020. "The role of competences, engagement, and devices in configuring the impact of prices in energy demand response: Findings from three smart energy pilots with households," Energy Policy, Elsevier, vol. 137(C).
    24. Kirsten Gram-Hanssen & Anders Rhiger Hansen & Mette Mechlenborg, 2020. "Danish PV Prosumers’ Time-Shifting of Energy-Consuming Everyday Practices," Sustainability, MDPI, vol. 12(10), pages 1-19, May.
    25. Pina, André & Silva, Carlos & Ferrão, Paulo, 2012. "The impact of demand side management strategies in the penetration of renewable electricity," Energy, Elsevier, vol. 41(1), pages 128-137.
    26. Paul Simshauser and David Downer, 2016. "On the Inequity of Flat-rate Electricity Tariffs," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3).
    27. Eva Llera-Sastresa & Sabina Scarpellini & Pilar Rivera-Torres & Juan Aranda & Ignacio Zabalza-Bribián & Alfonso Aranda-Usón, 2017. "Energy Vulnerability Composite Index in Social Housing, from a Household Energy Poverty Perspective," Sustainability, MDPI, vol. 9(5), pages 1-20, April.
    28. Anderson, Ben & Torriti, Jacopo, 2018. "Explaining shifts in UK electricity demand using time use data from 1974 to 2014," Energy Policy, Elsevier, vol. 123(C), pages 544-557.
    29. Brian C. Prest, 2020. "Peaking Interest: How Awareness Drives the Effectiveness of Time-of-Use Electricity Pricing," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 7(1), pages 103-143.
    30. Roberto Barrella & Irene Priego & José Ignacio Linares & Eva Arenas & José Carlos Romero & Efraim Centeno, 2020. "Feasibility Study of a Centralised Electrically Driven Air Source Heat Pump Water Heater to Face Energy Poverty in Block Dwellings in Madrid (Spain)," Energies, MDPI, vol. 13(11), pages 1-23, May.
    31. Cappers, Peter & Spurlock, C. Anna & Todd, Annika & Jin, Ling, 2018. "Are vulnerable customers any different than their peers when exposed to critical peak pricing: Evidence from the U.S," Energy Policy, Elsevier, vol. 123(C), pages 421-432.
    32. Ahmad Faruqui, Sanem Sergici, and Lamine Akaba, 2014. "The Impact of Dynamic Pricing on Residential and Small Commercial and Industrial Usage: New Experimental Evidence from Connecticut," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    33. Rhodes, Joshua D. & Cole, Wesley J. & Upshaw, Charles R. & Edgar, Thomas F. & Webber, Michael E., 2014. "Clustering analysis of residential electricity demand profiles," Applied Energy, Elsevier, vol. 135(C), pages 461-471.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Joe F. Bozeman & Shauhrat S. Chopra & Philip James & Sajjad Muhammad & Hua Cai & Kangkang Tong & Maya Carrasquillo & Harold Rickenbacker & Destenie Nock & Weslynne Ashton & Oliver Heidrich & Sybil Der, 2023. "Three research priorities for just and sustainable urban systems: Now is the time to refocus," Journal of Industrial Ecology, Yale University, vol. 27(2), pages 382-394, April.
    2. Anders Rhiger Hansen & Daniel Leiria & Hicham Johra & Anna Marszal-Pomianowska, 2022. "Who Produces the Peaks? Household Variation in Peak Energy Demand for Space Heating and Domestic Hot Water," Energies, MDPI, vol. 15(24), pages 1-23, December.
    3. Gokturk Poyrazoglu, 2021. "Determination of Price Zones during Transition from Uniform to Zonal Electricity Market: A Case Study for Turkey," Energies, MDPI, vol. 14(4), pages 1-13, February.
    4. Ku, Arthur Lin & Qiu, Yueming (Lucy) & Lou, Jiehong & Nock, Destenie & Xing, Bo, 2022. "Changes in hourly electricity consumption under COVID mandates: A glance to future hourly residential power consumption pattern with remote work in Arizona," Applied Energy, Elsevier, vol. 310(C).
    5. Panu Kalmi & Gianluca Trotta & Andrius Kažukauskas, 2021. "Energy‐related financial literacy and electricity consumption: Survey‐based evidence from Finland," Journal of Consumer Affairs, Wiley Blackwell, vol. 55(3), pages 1062-1089, September.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    2. 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.
    3. Jieyi Kang & David Reiner, 2021. "Identifying residential consumption patterns using data-mining techniques: A large-scale study of smart meter data in Chengdu, China," Working Papers EPRG2114, Energy Policy Research Group, Cambridge Judge Business School, University of Cambridge.
    4. 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.
    5. 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.
    6. 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).
    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. 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.
    9. Schleich, Joachim & Faure, Corinne & Meissner, Thomas, 2021. "Adoption of retrofit measures among homeowners in EU countries: The effects of access to capital and debt aversion," Energy Policy, Elsevier, vol. 149(C).
    10. Alexander Tureczek & Per Sieverts Nielsen & Henrik Madsen, 2018. "Electricity Consumption Clustering Using Smart Meter Data," Energies, MDPI, vol. 11(4), pages 1-18, April.
    11. 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.
    12. 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.
    13. Jing Liang & Yueming Qiu & Bo Xing, 2021. "Social Versus Private Benefits of Energy Efficiency Under Time-of-Use and Increasing Block Pricing," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 78(1), pages 43-75, January.
    14. 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.
    15. Cédric Clastres & Haikel Khalfallah, 2020. "Retailers' strategies facing demand response and markets interactions," Working Papers hal-03167543, HAL.
    16. Khosrowpour, Ardalan & Jain, Rishee K. & Taylor, John E. & Peschiera, Gabriel & Chen, Jiayu & Gulbinas, Rimas, 2018. "A review of occupant energy feedback research: Opportunities for methodological fusion at the intersection of experimentation, analytics, surveys and simulation," Applied Energy, Elsevier, vol. 218(C), pages 304-316.
    17. Henningsen, Geraldine & Wiese, Catharina, 2019. "Do Household Characteristics Really Matter? A Meta-Analysis on the Determinants of Households’ Energy-Efficiency Investments," MPRA Paper 101701, University Library of Munich, Germany.
    18. 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.
    19. 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.
    20. 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).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:13:y:2020:i:18:p:4713-:d:411468. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.