IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v265y2023ics0360544222031620.html
   My bibliography  Save this article

Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality

Author

Listed:
  • Hong, Yejin
  • Yoon, Sungmin
  • Choi, Sebin

Abstract

Buildings are considered the enormous source of untapped energy efficiency potential in the global carbon neutrality. It is necessary to ensure that buildings are energy-efficient using operational pattern analytics and diagnostics. Therefore, this study proposes a novel symbolic hierarchical clustering method (named HOS-SAX) to evaluate the building system operation, efficiency, and energy usage patterns. The proposed HOS-SAX method is intended to enhance the existing methods that focus only on the energy usage characteristics and thus offer limited insights on the building system and operational efficiency. The proposed method consists of: (1) Holistic Operational Signature (HOS) and (2) HOS-based symbolic aggregate approximation (SAX) analyses. A HOS analysis is conducted to derive the representative operational signatures for building operation and efficiency using system-, building-, and weather-level data. Then, SAX is performed with the operational signatures derived from the HOS to cluster the building operation patterns. In a case study for a district heating substation serving residential buildings, the HOS-SAX cluster analysis showed 15 sections in the cluster map that visualize the: (1) energy usage, (2) design efficiency, and (3) control efficiency. The cluster map revealed that the sections that operated inefficiently account for approximately 71% of the entire operation period. Moreover, it is expected that the supply temperature of 0.87 °C can be reduced in the most inefficient sections.

Suggested Citation

  • Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    • Handle: RePEc:eee:energy:v:265:y:2023:i:c:s0360544222031620
    DOI: 10.1016/j.energy.2022.126276
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544222031620
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2022.126276?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. Rouleau, Jean & Gosselin, Louis, 2021. "Impacts of the COVID-19 lockdown on energy consumption in a Canadian social housing building," Applied Energy, Elsevier, vol. 287(C).
    2. Yu, Xinran & Ergan, Semiha & Dedemen, Gokmen, 2019. "A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
    4. Li, Kehua & Yang, Rebecca Jing & Robinson, Duane & Ma, Jun & Ma, Zhenjun, 2019. "An agglomerative hierarchical clustering-based strategy using Shared Nearest Neighbours and multiple dissimilarity measures to identify typical daily electricity usage profiles of university library b," Energy, Elsevier, vol. 174(C), pages 735-748.
    5. 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).
    6. Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
    7. Gadd, Henrik & Werner, Sven, 2014. "Achieving low return temperatures from district heating substations," Applied Energy, Elsevier, vol. 136(C), pages 59-67.
    8. Ivanko, Dmytro & Sørensen, Åse Lekang & Nord, Natasa, 2021. "Splitting measurements of the total heat demand in a hotel into domestic hot water and space heating heat use," Energy, Elsevier, vol. 219(C).
    9. Savolainen, Rebecka & Lahdelma, Risto, 2022. "Optimization of renewable energy for buildings with energy storages and 15-minute power balance," Energy, Elsevier, vol. 243(C).
    10. Ran, Fengming & Gao, Dian-ce & Zhang, Xu & Chen, Shuyue, 2020. "A virtual sensor based self-adjusting control for HVAC fast demand response in commercial buildings towards smart grid applications," Applied Energy, Elsevier, vol. 269(C).
    11. Liu, Jia & Yang, Hongxing & Zhou, Yuekuan, 2021. "Peer-to-peer trading optimizations on net-zero energy communities with energy storage of hydrogen and battery vehicles," Applied Energy, Elsevier, vol. 302(C).
    12. Lund, Henrik & Østergaard, Poul Alberg & Nielsen, Tore Bach & Werner, Sven & Thorsen, Jan Eric & Gudmundsson, Oddgeir & Arabkoohsar, Ahmad & Mathiesen, Brian Vad, 2021. "Perspectives on fourth and fifth generation district heating," Energy, Elsevier, vol. 227(C).
    13. Kang, Hyuna & An, Jongbaek & Kim, Hakpyeong & Ji, Changyoon & Hong, Taehoon & Lee, Seunghye, 2021. "Changes in energy consumption according to building use type under COVID-19 pandemic in South Korea," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    14. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    15. Mohammad Aldubyan & Moncef Krarti, 2022. "Impact of Stay Home Living on Energy Demand of Residential Buildings Case Study of Saudi Arabia," Discussion Papers ks--2022-dp02, King Abdullah Petroleum Studies and Research Center.
    16. Chegari, Badr & Tabaa, Mohamed & Simeu, Emmanuel & Moutaouakkil, Fouad & Medromi, Hicham, 2022. "An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design," Energy, Elsevier, vol. 248(C).
    17. Mei, Jun & Xia, Xiaohua, 2017. "Energy-efficient predictive control of indoor thermal comfort and air quality in a direct expansion air conditioning system," Applied Energy, Elsevier, vol. 195(C), pages 439-452.
    18. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    19. Pérez-Andreu, Víctor & Aparicio-Fernández, Carolina & Martínez-Ibernón, Ana & Vivancos, José-Luis, 2018. "Impact of climate change on heating and cooling energy demand in a residential building in a Mediterranean climate," Energy, Elsevier, vol. 165(PA), pages 63-74.
    20. Capozzoli, Alfonso & Piscitelli, Marco Savino & Brandi, Silvio & Grassi, Daniele & Chicco, Gianfranco, 2018. "Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings," Energy, Elsevier, vol. 157(C), pages 336-352.
    21. Noussan, Michel & Jarre, Matteo & Poggio, Alberto, 2017. "Real operation data analysis on district heating load patterns," Energy, Elsevier, vol. 129(C), pages 70-78.
    22. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    23. Gadd, Henrik & Werner, Sven, 2015. "Fault detection in district heating substations," Applied Energy, Elsevier, vol. 157(C), pages 51-59.
    24. Guo, Chenyu & Wang, Xin & Zheng, Yihui & Zhang, Feng, 2022. "Real-time optimal energy management of microgrid with uncertainties based on deep reinforcement learning," Energy, Elsevier, vol. 238(PC).
    25. Zhan, Sicheng & Liu, Zhaoru & Chong, Adrian & Yan, Da, 2020. "Building categorization revisited: A clustering-based approach to using smart meter data for building energy benchmarking," Applied Energy, Elsevier, vol. 269(C).
    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. Choi, Sebin & Yoon, Sungmin, 2024. "Change-point model-based clustering for urban building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    2. Aya Nabil Sayed & Faycal Bensaali & Yassine Himeur & Mahdi Houchati, 2023. "Edge-Based Real-Time Occupancy Detection System through a Non-Intrusive Sensing System," Energies, MDPI, vol. 16(5), pages 1-14, March.
    3. Yoon, Sungmin & Lee, Jechan, 2024. "Perspective for waste upcycling-driven zero energy buildings," Energy, Elsevier, vol. 289(C).

    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. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    2. Kim, Ryunhee & Hong, Yejin & Choi, Youngwoong & Yoon, Sungmin, 2021. "System-level fouling detection of district heating substations using virtual-sensor-assisted building automation system," Energy, Elsevier, vol. 227(C).
    3. Choi, Sebin & Yoon, Sungmin, 2024. "Change-point model-based clustering for urban building energy analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    4. Chicherin, Stanislav & Anvari-Moghaddam, Amjad, 2021. "Adjusting heat demands using the operational data of district heating systems," Energy, Elsevier, vol. 235(C).
    5. Koo, Jabeom & Yoon, Sungmin, 2022. "In-situ sensor virtualization and calibration in building systems," Applied Energy, Elsevier, vol. 325(C).
    6. Calikus, Ece & Nowaczyk, Sławomir & Sant'Anna, Anita & Gadd, Henrik & Werner, Sven, 2019. "A data-driven approach for discovering heat load patterns in district heating," Applied Energy, Elsevier, vol. 252(C), pages 1-1.
    7. Østergaard, Dorte Skaarup & Smith, Kevin Michael & Tunzi, Michele & Svendsen, Svend, 2022. "Low-temperature operation of heating systems to enable 4th generation district heating: A review," Energy, Elsevier, vol. 248(C).
    8. Roberto Chiosa & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "A Data Analytics-Based Energy Information System (EIS) Tool to Perform Meter-Level Anomaly Detection and Diagnosis in Buildings," Energies, MDPI, vol. 14(1), pages 1-28, January.
    9. Romanov, D. & Leiss, B., 2022. "Geothermal energy at different depths for district heating and cooling of existing and future building stock," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
    10. Neumayer, Martin & Stecher, Dominik & Grimm, Sebastian & Maier, Andreas & Bücker, Dominikus & Schmidt, Jochen, 2023. "Fault and anomaly detection in district heating substations: A survey on methodology and data sets," Energy, Elsevier, vol. 276(C).
    11. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    12. Sara Månsson & Kristin Davidsson & Patrick Lauenburg & Marcus Thern, 2018. "Automated Statistical Methods for Fault Detection in District Heating Customer Installations," Energies, MDPI, vol. 12(1), pages 1-18, December.
    13. Zhang, Fan & Bales, Chris & Fleyeh, Hasan, 2021. "Night setback identification of district heat substations using bidirectional long short term memory with attention mechanism," Energy, Elsevier, vol. 224(C).
    14. Chicherin, Stanislav, 2020. "Methodology for analyzing operation data for optimum district heating (DH) system design: Ten-year data of Omsk, Russia," Energy, Elsevier, vol. 211(C).
    15. Lee, Junsoo & Kim, Tae Wan & Koo, Choongwan, 2022. "A novel process model for developing a scalable room-level energy benchmark using real-time bigdata: Focused on identifying representative energy usage patterns," Renewable and Sustainable Energy Reviews, Elsevier, vol. 169(C).
    16. Månsson, Sara & Johansson Kallioniemi, Per-Olof & Thern, Marcus & Van Oevelen, Tijs & Sernhed, Kerstin, 2019. "Faults in district heating customer installations and ways to approach them: Experiences from Swedish utilities," Energy, Elsevier, vol. 180(C), pages 163-174.
    17. Hong, Yejin & Yoon, Sungmin & Kim, Yong-Shik & Jang, Hyangin, 2021. "System-level virtual sensing method in building energy systems using autoencoder: Under the limited sensors and operational datasets," Applied Energy, Elsevier, vol. 301(C).
    18. Danica Djurić Ilić, 2020. "Classification of Measures for Dealing with District Heating Load Variations—A Systematic Review," Energies, MDPI, vol. 14(1), pages 1-27, December.
    19. Stanislav Chicherin & Vladislav Mašatin & Andres Siirde & Anna Volkova, 2020. "Method for Assessing Heat Loss in A District Heating Network with A Focus on the State of Insulation and Actual Demand for Useful Energy," Energies, MDPI, vol. 13(17), pages 1-15, September.
    20. Tsai, I-Chun & Chen, Han-Bo & Lin, Che-Chun, 2024. "The ability of energy commodities to hedge the dynamic risk of epidemic black swans," Resources Policy, Elsevier, vol. 89(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:energy:v:265:y:2023:i:c:s0360544222031620. 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.journals.elsevier.com/energy .

    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.