IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v353y2024ipas0306261923014101.html
   My bibliography  Save this article

A comparative study of clustering algorithms for intermittent heating demand considering time series

Author

Listed:
  • Li, Jinwei
  • Ma, Rongjiang
  • Deng, Mengsi
  • Cao, Xiaoling
  • Wang, Xicheng
  • Wang, Xianlin

Abstract

Occupants' behavior has a significant impact on building energy consumption. Various clustering algorithms, represented by the K-means algorithm based on the “Euclidean” distance, are widely used to extract occupants' behavior patterns. However, these commonly used algorithms cannot effectively capture the temporal characteristics of occupants' behavior. Although some studies have started to adopt clustering algorithms considering time series for research, the differences in clustering effects of different algorithms on occupants' behavior are still unclear, especially in the clustering application study of behavior with intermittent characteristics, this intermittent behavior is a behavior that does not always exist, and the timing of its occurrence changes. Therefore, this study takes the heating demand behavior of occupants with obvious intermittency as a case and compares the clustering effect of the common clustering algorithm (K-means) and three clustering algorithms considering time series (K-shape, Dynamic Time Warping (DTW), and Derivative Dynamic Time Warping (DDTW)). The study also explores possible reasons leading to differences and introduces three evaluation methods for clustering results of intermittent heating demand. This study provides a reference for selecting appropriate clustering algorithms to study occupants' intermittent behavior and has practical application value in improving the extraction and prediction of behavior patterns of occupants with intermittent characteristics.

Suggested Citation

  • Li, Jinwei & Ma, Rongjiang & Deng, Mengsi & Cao, Xiaoling & Wang, Xicheng & Wang, Xianlin, 2024. "A comparative study of clustering algorithms for intermittent heating demand considering time series," Applied Energy, Elsevier, vol. 353(PA).
    • Handle: RePEc:eee:appene:v:353:y:2024:i:pa:s0306261923014101
    DOI: 10.1016/j.apenergy.2023.122046
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261923014101
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2023.122046?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. Chenari, Behrang & Dias Carrilho, João & Gameiro da Silva, Manuel, 2016. "Towards sustainable, energy-efficient and healthy ventilation strategies in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 59(C), pages 1426-1447.
    2. Baldi, Simone & Korkas, Christos D. & Lv, Maolong & Kosmatopoulos, Elias B., 2018. "Automating occupant-building interaction via smart zoning of thermostatic loads: A switched self-tuning approach," Applied Energy, Elsevier, vol. 231(C), pages 1246-1258.
    3. Wang, Zhe & Luo, Maohui & Geng, Yang & Lin, Borong & Zhu, Yingxin, 2018. "A model to compare convective and radiant heating systems for intermittent space heating," Applied Energy, Elsevier, vol. 215(C), pages 211-226.
    4. Johan Schot & Laur Kanger & Geert Verbong, 2016. "The roles of users in shaping transitions to new energy systems," Nature Energy, Nature, vol. 1(5), pages 1-7, May.
    5. Korkas, Christos D. & Baldi, Simone & Michailidis, Iakovos & Kosmatopoulos, Elias B., 2016. "Occupancy-based demand response and thermal comfort optimization in microgrids with renewable energy sources and energy storage," Applied Energy, Elsevier, vol. 163(C), pages 93-104.
    Full references (including those not matched with items on IDEAS)

    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. Hyeunguk Ahn & Jingjing Liu & Donghun Kim & Rongxin Yin & Tianzhen Hong & Mary Ann Piette, 2021. "How Can Floor Covering Influence Buildings’ Demand Flexibility?," Energies, MDPI, vol. 14(12), pages 1-17, June.
    2. Favero, Matteo & Kloppenborg Møller, Jan & Calì, Davide & Carlucci, Salvatore, 2022. "Human-in-the-loop methods for occupant-centric building design and operation," Applied Energy, Elsevier, vol. 325(C).
    3. David Vérez & Luisa F. Cabeza, 2021. "Which Building Services Are Considered to Have Impact on Climate Change?," Energies, MDPI, vol. 14(13), pages 1-16, June.
    4. Behzad Rismanchi & Juan Mahecha Zambrano & Bryan Saxby & Ross Tuck & Mark Stenning, 2019. "Control Strategies in Multi-Zone Air Conditioning Systems," Energies, MDPI, vol. 12(3), pages 1-14, January.
    5. Song, Kwonsik & Kim, Sooyoung & Park, Moonseo & Lee, Hyun-Soo, 2017. "Energy efficiency-based course timetabling for university buildings," Energy, Elsevier, vol. 139(C), pages 394-405.
    6. Cass, Noel & Schwanen, Tim & Shove, Elizabeth, 2018. "Infrastructures, intersections and societal transformations," Technological Forecasting and Social Change, Elsevier, vol. 137(C), pages 160-167.
    7. Felix Garcia-Torres & Ascension Zafra-Cabeza & Carlos Silva & Stephane Grieu & Tejaswinee Darure & Ana Estanqueiro, 2021. "Model Predictive Control for Microgrid Functionalities: Review and Future Challenges," Energies, MDPI, vol. 14(5), pages 1-26, February.
    8. Keon Baek & Woong Ko & Jinho Kim, 2019. "Optimal Scheduling of Distributed Energy Resources in Residential Building under the Demand Response Commitment Contract," Energies, MDPI, vol. 12(14), pages 1-19, July.
    9. Amir Faraji & Maria Rashidi & Fatemeh Rezaei & Payam Rahnamayiezekavat, 2023. "A Meta-Synthesis Review of Occupant Comfort Assessment in Buildings (2002–2022)," Sustainability, MDPI, vol. 15(5), pages 1-36, February.
    10. Luciano Cavalcante Siebert & Alexandre Rasi Aoki & Germano Lambert-Torres & Nelson Lambert-de-Andrade & Nikolaos G. Paterakis, 2020. "An Agent-Based Approach for the Planning of Distribution Grids as a Socio-Technical System," Energies, MDPI, vol. 13(18), pages 1-13, September.
    11. Gruber, Mario, 2020. "An evolutionary perspective on adoption-diffusion theory," Journal of Business Research, Elsevier, vol. 116(C), pages 535-541.
    12. Lu, Qing & Yu, Hao & Zhao, Kangli & Leng, Yajun & Hou, Jianchao & Xie, Pinjie, 2019. "Residential demand response considering distributed PV consumption: A model based on China's PV policy," Energy, Elsevier, vol. 172(C), pages 443-456.
    13. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    14. Manzano, J.M. & Salvador, J.R. & Romaine, J.B. & Alvarado-Barrios, L., 2022. "Economic predictive control for isolated microgrids based on real world demand/renewable energy data and forecast errors," Renewable Energy, Elsevier, vol. 194(C), pages 647-658.
    15. Kivimaa, Paula & Boon, Wouter & Hyysalo, Sampsa & Klerkx, Laurens, 2019. "Towards a typology of intermediaries in sustainability transitions: A systematic review and a research agenda," Research Policy, Elsevier, vol. 48(4), pages 1062-1075.
    16. Hyysalo, Sampsa & Juntunen, Jouni K. & Martiskainen, Mari, 2018. "Energy Internet forums as acceleration phase transition intermediaries," Research Policy, Elsevier, vol. 47(5), pages 872-885.
    17. Wilson, Charlie & Hargreaves, Tom & Hauxwell-Baldwin, Richard, 2017. "Benefits and risks of smart home technologies," Energy Policy, Elsevier, vol. 103(C), pages 72-83.
    18. Gomez-Herrera, Juan A. & Anjos, Miguel F., 2018. "Optimal collaborative demand-response planner for smart residential buildings," Energy, Elsevier, vol. 161(C), pages 370-380.
    19. Giaouris, Damian & Papadopoulos, Athanasios I. & Patsios, Charalampos & Walker, Sara & Ziogou, Chrysovalantou & Taylor, Phil & Voutetakis, Spyros & Papadopoulou, Simira & Seferlis, Panos, 2018. "A systems approach for management of microgrids considering multiple energy carriers, stochastic loads, forecasting and demand side response," Applied Energy, Elsevier, vol. 226(C), pages 546-559.
    20. Umeozor, Evar Chinedu & Trifkovic, Milana, 2016. "Operational scheduling of microgrids via parametric programming," Applied Energy, Elsevier, vol. 180(C), pages 672-681.

    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:353:y:2024:i:pa:s0306261923014101. 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.