IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v350y2023ics0306261923011145.html
   My bibliography  Save this article

Stochastic generation of residential load profiles with realistic variability based on wavelet-decomposed smart meter data

Author

Listed:
  • Claeys, Robbert
  • Cleenwerck, Rémy
  • Knockaert, Jos
  • Desmet, Jan

Abstract

Residential smart meter data with high time resolution are integral to many data-driven applications, ranging from hosting capacity studies to R&D activities of private enterprises. However, privacy legislation restricts public availability of large-scale datasets. Furthermore, existing datasets may suffer from imbalances in terms of underrepresented classes. To address these concerns, this study presents a novel decomposition–recombination approach for generating synthetic load profiles that exhibit realistic variability and demand peaks. High-frequency load profiles are decomposed into a low-frequency base load and high-frequency variability at the daily level through a discrete wavelet transformation. Components from different households are subsequently rescaled, shifted and recombined in a stochastic load profile generator to obtain new daily load profiles with high-fidelity behavior. The performance of this generator is evaluated through benchmarking, resulting in a mean average error of 0.09 kW on an average value of less than 3 kW for the daily peaks, whilst preserving their seasonality. The introduced load profile generator is validated as an alternative to privacy-sensitive residential smart meter data in a hosting capacity case study. The analysis focuses on the voltage drop caused by residential electric vehicle charging, considering both real and synthetic data. The synthetic data demonstrated voltage drops with a mean average error less than 0.2 V for the 10th and 90th percentile when benchmarked with respect to the real voltage level distribution. The introduced decomposition–recombination method is shown to accurately capture the high-frequency variability and peak behavior, and is suitable for practical applications at the daily level.

Suggested Citation

  • Claeys, Robbert & Cleenwerck, Rémy & Knockaert, Jos & Desmet, Jan, 2023. "Stochastic generation of residential load profiles with realistic variability based on wavelet-decomposed smart meter data," Applied Energy, Elsevier, vol. 350(C).
    • Handle: RePEc:eee:appene:v:350:y:2023:i:c:s0306261923011145
    DOI: 10.1016/j.apenergy.2023.121750
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923011145
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.121750?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Zahoor Ali Khan & Muhammad Adil & Nadeem Javaid & Malik Najmus Saqib & Muhammad Shafiq & Jin-Ghoo Choi, 2020. "Electricity Theft Detection Using Supervised Learning Techniques on Smart Meter Data," Sustainability, MDPI, vol. 12(19), pages 1-25, September.
    2. Protopapadaki, Christina & Saelens, Dirk, 2017. "Heat pump and PV impact on residential low-voltage distribution grids as a function of building and district properties," Applied Energy, Elsevier, vol. 192(C), pages 268-281.
    3. Edmunds, Calum & Galloway, Stuart & Dixon, James & Bukhsh, Waqquas & Elders, Ian, 2021. "Hosting capacity assessment of heat pumps and optimised electric vehicle charging on low voltage networks," Applied Energy, Elsevier, vol. 298(C).
    4. Alturki, Mansoor & Khodaei, Amin & Paaso, Aleksi & Bahramirad, Shay, 2018. "Optimization-based distribution grid hosting capacity calculations," Applied Energy, Elsevier, vol. 219(C), pages 350-360.
    5. Chicco, Gianfranco, 2012. "Overview and performance assessment of the clustering methods for electrical load pattern grouping," Energy, Elsevier, vol. 42(1), pages 68-80.
    6. Koirala, Arpan & Van Acker, Tom & D’hulst, Reinhilde & Van Hertem, Dirk, 2022. "Hosting capacity of photovoltaic systems in low voltage distribution systems: A benchmark of deterministic and stochastic approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 155(C).
    7. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    8. Beckel, Christian & Sadamori, Leyna & Staake, Thorsten & Santini, Silvia, 2014. "Revealing household characteristics from smart meter data," Energy, Elsevier, vol. 78(C), pages 397-410.
    9. Pelletier, Samuel & Jabali, Ola & Laporte, Gilbert & Veneroni, Marco, 2017. "Battery degradation and behaviour for electric vehicles: Review and numerical analyses of several models," Transportation Research Part B: Methodological, Elsevier, vol. 103(C), pages 158-187.
    10. Mohammad Zain ul Abideen & Omar Ellabban & Luluwah Al-Fagih, 2020. "A Review of the Tools and Methods for Distribution Networks’ Hosting Capacity Calculation," Energies, MDPI, vol. 13(11), pages 1-25, June.
    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. Dumisani Mtolo & David Dorrell & Rudiren Pillay Carpanen, 2023. "Balancing of Low-Voltage Supply Network with a Smart Utility Controller Leveraging Distributed Customer Energy Sources," Energies, MDPI, vol. 16(23), pages 1-30, November.

    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. Damianakis, Nikolaos & Mouli, Gautham Ram Chandra & Bauer, Pavol & Yu, Yunhe, 2023. "Assessing the grid impact of Electric Vehicles, Heat Pumps & PV generation in Dutch LV distribution grids," Applied Energy, Elsevier, vol. 352(C).
    2. Yamaguchi, Yohei & Shoda, Yuto & Yoshizawa, Shinya & Imai, Tatsuya & Perwez, Usama & Shimoda, Yoshiyuki & Hayashi, Yasuhiro, 2023. "Feasibility assessment of net zero-energy transformation of building stock using integrated synthetic population, building stock, and power distribution network framework," Applied Energy, Elsevier, vol. 333(C).
    3. 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.
    4. Shimoda, Yoshiyuki & Yamaguchi, Yohei & Iwafune, Yumiko & Hidaka, Kazuyoshi & Meier, Alan & Yagita, Yoshie & Kawamoto, Hisaki & Nishikiori, Soichi, 2020. "Energy demand science for a decarbonized society in the context of the residential sector," Renewable and Sustainable Energy Reviews, Elsevier, vol. 132(C).
    5. Y, Kiguchi & Y, Heo & M, Weeks & R, Choudhary, 2019. "Predicting intra-day load profiles under time-of-use tariffs using smart meter data," Energy, Elsevier, vol. 173(C), pages 959-970.
    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. Cho, Yongjun & Lee, Eunjung & Baek, Keon & Kim, Jinho, 2023. "Stochastic Optimization-Based hosting capacity estimation with volatile net load deviation in distribution grids," Applied Energy, Elsevier, vol. 341(C).
    8. Vincent Umoh & Innocent Davidson & Abayomi Adebiyi & Unwana Ekpe, 2023. "Methods and Tools for PV and EV Hosting Capacity Determination in Low Voltage Distribution Networks—A Review," Energies, MDPI, vol. 16(8), pages 1-25, April.
    9. Fang, Hongliang & Wang, Yan-Wu & Xiao, Jiang-Wen & Cui, Shichang & Qin, Zhaoyu, 2021. "A new mining framework with piecewise symbolic spatial clustering," Applied Energy, Elsevier, vol. 298(C).
    10. Konstantin Hopf & Mariya Sodenkamp & Thorsten Staake, 2018. "Enhancing energy efficiency in the residential sector with smart meter data analytics," Electronic Markets, Springer;IIM University of St. Gallen, vol. 28(4), pages 453-473, November.
    11. Rajabi, A. & Elphick, S. & David, J. & Pors, A. & Robinson, D., 2022. "Innovative approaches for assessing and enhancing the hosting capacity of PV-rich distribution networks: An Australian perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    12. Illia Diahovchenko & Lubov Petrichenko, 2022. "Comparative Analysis of Power Distribution Systems with Individual Prosumers Owing Photovoltaic Installations and Solar Energy Communities in Terms of Profitability and Hosting Capacity," Energies, MDPI, vol. 15(23), pages 1-20, November.
    13. 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.
    14. Ibrahim Mohamed Diaaeldin & Shady H. E. Abdel Aleem & Ahmed El-Rafei & Almoataz Y. Abdelaziz & Ahmed F. Zobaa, 2020. "Enhancement of Hosting Capacity with Soft Open Points and Distribution System Reconfiguration: Multi-Objective Bilevel Stochastic Optimization," Energies, MDPI, vol. 13(20), pages 1-20, October.
    15. 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.
    16. Guo, Rui & Meunier, Simon & Protopapadaki, Christina & Saelens, Dirk, 2023. "A review of European low-voltage distribution networks," Renewable and Sustainable Energy Reviews, Elsevier, vol. 173(C).
    17. 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.
    18. 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.
    19. Trotta, Gianluca, 2020. "An empirical analysis of domestic electricity load profiles: Who consumes how much and when?," Applied Energy, Elsevier, vol. 275(C).
    20. Sandström, Maria & Huang, Pei & Bales, Chris & Dotzauer, Erik, 2023. "Evaluation of hosting capacity of the power grid for electric vehicles – A case study in a Swedish residential area," Energy, Elsevier, vol. 284(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:eee:appene:v:350:y:2023:i:c:s0306261923011145. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    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.