IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v264y2020ics0306261920302270.html
   My bibliography  Save this article

Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data

Author

Listed:
  • Westermann, Paul
  • Deb, Chirag
  • Schlueter, Arno
  • Evins, Ralph

Abstract

A high-quality building energy retrofit analysis requires knowledge of building characteristics like the type of installed heating system. This means auditing the building in person or conducting a detailed survey, which is not readily scalable for many buildings.

Suggested Citation

  • Westermann, Paul & Deb, Chirag & Schlueter, Arno & Evins, Ralph, 2020. "Unsupervised learning of energy signatures to identify the heating system and building type using smart meter data," Applied Energy, Elsevier, vol. 264(C).
    • Handle: RePEc:eee:appene:v:264:y:2020:i:c:s0306261920302270
    DOI: 10.1016/j.apenergy.2020.114715
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261920302270
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2020.114715?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. Love, Jenny & Smith, Andrew Z.P. & Watson, Stephen & Oikonomou, Eleni & Summerfield, Alex & Gleeson, Colin & Biddulph, Phillip & Chiu, Lai Fong & Wingfield, Jez & Martin, Chris & Stone, Andy & Lowe, R, 2017. "The addition of heat pump electricity load profiles to GB electricity demand: Evidence from a heat pump field trial," Applied Energy, Elsevier, vol. 204(C), pages 332-342.
    2. Park, June Young & Yang, Xiya & Miller, Clayton & Arjunan, Pandarasamy & Nagy, Zoltan, 2019. "Apples or oranges? Identification of fundamental load shape profiles for benchmarking buildings using a large and diverse dataset," Applied Energy, Elsevier, vol. 236(C), pages 1280-1295.
    3. Lundström, Lukas & Wallin, Fredrik, 2016. "Heat demand profiles of energy conservation measures in buildings and their impact on a district heating system," Applied Energy, Elsevier, vol. 161(C), pages 290-299.
    4. Hong, Tianzhen & Yang, Le & Hill, David & Feng, Wei, 2014. "Data and analytics to inform energy retrofit of high performance buildings," Applied Energy, Elsevier, vol. 126(C), pages 90-106.
    5. Miller, Clayton & Nagy, Zoltán & Schlueter, Arno, 2018. "A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1365-1377.
    6. Gabor J. Szekely & Maria L. Rizzo, 2005. "Hierarchical Clustering via Joint Between-Within Distances: Extending Ward's Minimum Variance Method," Journal of Classification, Springer;The Classification Society, vol. 22(2), pages 151-183, September.
    7. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
    8. Marco Cuturi & Mathieu Blondel, 2017. "Soft-DTW: a Differentiable Loss Function for Time-Series," Working Papers 2017-81, Center for Research in Economics and Statistics.
    9. Nagy, Zoltán & Rossi, Dino & Hersberger, Christian & Irigoyen, Silvia Domingo & Miller, Clayton & Schlueter, Arno, 2014. "Balancing envelope and heating system parameters for zero emissions retrofit using building sensor data," Applied Energy, Elsevier, vol. 131(C), pages 56-66.
    10. Tong, Xing & Li, Ran & Li, Furong & Kang, Chongqing, 2016. "Cross-domain feature selection and coding for household energy behavior," Energy, Elsevier, vol. 107(C), pages 9-16.
    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. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
    2. Deb, C. & Gelder, L.V. & Spiekman, M. & Pandraud, Guillaume & Jack, R. & Fitton, R., 2021. "Measuring the heat transfer coefficient (HTC) in buildings: A stakeholder's survey," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    3. Benedetta Grassi & Edoardo Alessio Piana & Gian Paolo Beretta & Mariagrazia Pilotelli, 2020. "Dynamic Approach to Evaluate the Effect of Reducing District Heating Temperature on Indoor Thermal Comfort," Energies, MDPI, vol. 14(1), pages 1-25, December.
    4. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
    5. Hong, Yejin & Yoon, Sungmin, 2022. "Holistic Operational Signatures for an energy-efficient district heating substation in buildings," Energy, Elsevier, vol. 250(C).
    6. Wang, Qiaochu & Ding, Yan & Kong, Xiangfei & Tian, Zhe & Xu, Linrui & He, Qing, 2022. "Load pattern recognition based optimization method for energy flexibility in office buildings," Energy, Elsevier, vol. 254(PC).
    7. Deb, C. & Schlueter, A., 2021. "Review of data-driven energy modelling techniques for building retrofit," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    8. Mishra, Kakuli & Basu, Srinka & Maulik, Ujjwal, 2022. "Load profile mining using directed weighted graphs with application towards demand response management," Applied Energy, Elsevier, vol. 311(C).
    9. Gao, Lei & Liu, Tianyuan & Cao, Tao & Hwang, Yunho & Radermacher, Reinhard, 2021. "Comparing deep learning models for multi energy vectors prediction on multiple types of building," Applied Energy, Elsevier, vol. 301(C).
    10. Naderi, Shayan & Heslop, Simon & Chen, Dong & Watts, Scott & MacGill, Iain & Pignatta, Gloria & Sproul, Alistair, 2023. "Clustering based analysis of residential duck curve mitigation through solar pre-cooling: A case study of Australian housing stock," Renewable Energy, Elsevier, vol. 216(C).
    11. Dong, Bing & Liu, Yapan & Fontenot, Hannah & Ouf, Mohamed & Osman, Mohamed & Chong, Adrian & Qin, Shuxu & Salim, Flora & Xue, Hao & Yan, Da & Jin, Yuan & Han, Mengjie & Zhang, Xingxing & Azar, Elie & , 2021. "Occupant behavior modeling methods for resilient building design, operation and policy at urban scale: A review," Applied Energy, Elsevier, vol. 293(C).
    12. 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.
    13. Sofia Agostinelli & Fabrizio Cumo & Giambattista Guidi & Claudio Tomazzoli, 2021. "Cyber-Physical Systems Improving Building Energy Management: Digital Twin and Artificial Intelligence," Energies, MDPI, vol. 14(8), pages 1-25, April.

    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. Roth, Jonathan & Martin, Amory & Miller, Clayton & Jain, Rishee K., 2020. "SynCity: Using open data to create a synthetic city of hourly building energy estimates by integrating data-driven and physics-based methods," Applied Energy, Elsevier, vol. 280(C).
    2. 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).
    3. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
    4. Peacock, Malcolm & Fragaki, Aikaterini & Matuszewski, Bogdan J, 2023. "The impact of heat electrification on the seasonal and interannual electricity demand of Great Britain," Applied Energy, Elsevier, vol. 337(C).
    5. Xavier Serrano-Guerrero & Guillermo Escrivá-Escrivá & Santiago Luna-Romero & Jean-Michel Clairand, 2020. "A Time-Series Treatment Method to Obtain Electrical Consumption Patterns for Anomalies Detection Improvement in Electrical Consumption Profiles," Energies, MDPI, vol. 13(5), pages 1-23, February.
    6. Xu, Xiaojing & Chen, Chien-fei & Zhu, Xiaojuan & Hu, Qinran, 2018. "Promoting acceptance of direct load control programs in the United States: Financial incentive versus control option," Energy, Elsevier, vol. 147(C), pages 1278-1287.
    7. Behzadi, Amirmohammad & Holmberg, Sture & Duwig, Christophe & Haghighat, Fariborz & Ooka, Ryozo & Sadrizadeh, Sasan, 2022. "Smart design and control of thermal energy storage in low-temperature heating and high-temperature cooling systems: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 166(C).
    8. Zdeňka Náglová & Tereza Horáková, 2017. "Position of the Bakery Enterprises in the Czech Republic According to Detailed Specification of the Businesses," Acta Universitatis Agriculturae et Silviculturae Mendelianae Brunensis, Mendel University Press, vol. 65(5), pages 1719-1727.
    9. Jia, Shuning & Sheng, Kai & Huang, Dehai & Hu, Kai & Xu, Yizhe & Yan, Chengchu, 2023. "Design optimization of energy systems for zero energy buildings based on grid-friendly interaction with smart grid," Energy, Elsevier, vol. 284(C).
    10. Deakin, Matthew & Bloomfield, Hannah & Greenwood, David & Sheehy, Sarah & Walker, Sara & Taylor, Phil C., 2021. "Impacts of heat decarbonization on system adequacy considering increased meteorological sensitivity," Applied Energy, Elsevier, vol. 298(C).
    11. Tommy Rosén & Louise Ödlund, 2019. "Active Management of Heat Customers Towards Lower District Heating Return Water Temperature," Energies, MDPI, vol. 12(10), pages 1-20, May.
    12. Truong, Nguyen Le & Dodoo, Ambrose & Gustavsson, Leif, 2018. "Effects of energy efficiency measures in district-heated buildings on energy supply," Energy, Elsevier, vol. 142(C), pages 1114-1127.
    13. Brijnesh Jain & Vincent Froese & David Schultz, 2023. "An average-compress algorithm for the sample mean problem under dynamic time warping," Journal of Global Optimization, Springer, vol. 86(4), pages 885-903, August.
    14. Quessy, Jean-François, 2021. "A Szekely–Rizzo inequality for testing general copula homogeneity hypotheses," Journal of Multivariate Analysis, Elsevier, vol. 186(C).
    15. Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
    16. Fionn Murtagh & Pierre Legendre, 2014. "Ward’s Hierarchical Agglomerative Clustering Method: Which Algorithms Implement Ward’s Criterion?," Journal of Classification, Springer;The Classification Society, vol. 31(3), pages 274-295, October.
    17. Lidberg, T. & Gustafsson, M. & Myhren, J.A. & Olofsson, T. & Ödlund (former Trygg), L., 2018. "Environmental impact of energy refurbishment of buildings within different district heating systems," Applied Energy, Elsevier, vol. 227(C), pages 231-238.
    18. Zdeněk Hlávka & Marie Hušková & Simos G. Meintanis, 2020. "Change-point methods for multivariate time-series: paired vectorial observations," Statistical Papers, Springer, vol. 61(4), pages 1351-1383, August.
    19. Jeong, Kwangbok & Hong, Taehoon & Kim, Jimin & Cho, Kyuman, 2019. "Development of a multi-objective optimization model for determining the optimal CO2 emissions reduction strategies for a multi-family housing complex," Renewable and Sustainable Energy Reviews, Elsevier, vol. 110(C), pages 118-131.
    20. Jonathan Roth & Jayashree Chadalawada & Rishee K. Jain & Clayton Miller, 2021. "Uncertainty Matters: Bayesian Probabilistic Forecasting for Residential Smart Meter Prediction, Segmentation, and Behavioral Measurement and Verification," Energies, MDPI, vol. 14(5), pages 1-22, March.

    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:264:y:2020:i:c:s0306261920302270. 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.