IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v306y2022ipas0306261921012678.html
   My bibliography  Save this article

Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network

Author

Listed:
  • Geraldi, Matheus Soares
  • Ghisi, Enedir

Abstract

Energy benchmarking of buildings has an important role in improving energy performance by establishing a reference for the energy efficiency of the building stock. The simulation of archetypes followed by a generalisation model has been widely used to obtain benchmarks. However, even though archetypes summarise the main features of the building stock, the uncertainties must be accounted for in the modelling process. Moreover, testing the response of the benchmarking model using the actual building stock data supports the reliability of the method. This paper aims to propose an innovative framework to reduce the uncertainty of archetypes for benchmarking buildings. A standard framework for data compiling is proposed and an assessment of the uncertainty of variables using entropy and cluster analysis allowed to obtain representative archetypes. An Artificial Neural Network (ANN) was used as a predictive tool, and it was applied to benchmark a sample of actual buildings. Also, the simulation outcomes were used to determine energy end-uses according to the climatic zones. The framework proposition is presented alongside with a practical application. The result is an unprecedented benchmarking model: the archetype considers more variation in parameters of the building stock with higher uncertainties. Additionally, the modelling process showed to be robust for combining different datasets, and the ANN achieved high-performance metrics. Conclusion indicates the potential of using the framework for other typologies. Moreover, the framework demonstration is used for a school stock in Brazil, showing a trend to the inefficiency while a specific case study was explored, showing the potential of the method to find faults in the building energy use.

Suggested Citation

  • Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Data-driven framework towards realistic bottom-up energy benchmarking using an Artificial Neural Network," Applied Energy, Elsevier, vol. 306(PA).
    • Handle: RePEc:eee:appene:v:306:y:2022:i:pa:s0306261921012678
    DOI: 10.1016/j.apenergy.2021.117960
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261921012678
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2021.117960?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. Tso, Geoffrey K.F. & Yau, Kelvin K.W., 2007. "Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks," Energy, Elsevier, vol. 32(9), pages 1761-1768.
    2. Hsu, David, 2014. "How much information disclosure of building energy performance is necessary?," Energy Policy, Elsevier, vol. 64(C), pages 263-272.
    3. Papadopoulos, Sokratis & Kontokosta, Constantine E., 2019. "Grading buildings on energy performance using city benchmarking data," Applied Energy, Elsevier, vol. 233, pages 244-253.
    4. Dario Cottafava & Giulia Sonetti & Paolo Gambino & Andrea Tartaglino, 2018. "Explorative Multidimensional Analysis for Energy Efficiency: DataViz versus Clustering Algorithms," Energies, MDPI, vol. 11(5), pages 1-18, May.
    5. Tatiana Santos Saraiva & Edson Martins da Silva & Manuela Almeida & Luís Bragança, 2019. "Comparative Study of Comfort Indicators for School Constructions in Sustainability Methodologies: Schools in the Amazon and the Southeast Region of Brazil," Sustainability, MDPI, vol. 11(19), pages 1-14, September.
    6. Wong, S.L. & Wan, Kevin K.W. & Lam, Tony N.T., 2010. "Artificial neural networks for energy analysis of office buildings with daylighting," Applied Energy, Elsevier, vol. 87(2), pages 551-557, February.
    7. Buck, J. & Young, D., 2007. "The potential for energy efficiency gains in the Canadian commercial building sector: A stochastic frontier study," Energy, Elsevier, vol. 32(9), pages 1769-1780.
    8. Chung, William, 2011. "Review of building energy-use performance benchmarking methodologies," Applied Energy, Elsevier, vol. 88(5), pages 1470-1479, May.
    9. Dias Pereira, Luísa & Raimondo, Daniela & Corgnati, Stefano Paolo & Gameiro da Silva, Manuel, 2014. "Energy consumption in schools – A review paper," Renewable and Sustainable Energy Reviews, Elsevier, vol. 40(C), pages 911-922.
    10. Chung, William & Yeung, Iris M.H., 2017. "Benchmarking by convex non-parametric least squares with application on the energy performance of office buildings," Applied Energy, Elsevier, vol. 203(C), pages 454-462.
    11. Li, Honglian & Huang, Jin & Hu, Yao & Wang, Shangyu & Liu, Jing & Yang, Liu, 2021. "A new TMY generation method based on the entropy-based TOPSIS theory for different climatic zones in China," Energy, Elsevier, vol. 231(C).
    12. Wang, Endong & Alp, Neslihan & Shi, Jonathan & Wang, Chao & Zhang, Xiaodong & Chen, Hong, 2017. "Multi-criteria building energy performance benchmarking through variable clustering based compromise TOPSIS with objective entropy weighting," Energy, Elsevier, vol. 125(C), pages 197-210.
    13. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    14. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    15. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
    16. Wang, Zhe & Hong, Tianzhen & Piette, Mary Ann, 2019. "Predicting plug loads with occupant count data through a deep learning approach," Energy, Elsevier, vol. 181(C), pages 29-42.
    17. Koo, Choongwan & Hong, Taehoon, 2015. "Development of a dynamic operational rating system in energy performance certificates for existing buildings: Geostatistical approach and data-mining technique," Applied Energy, Elsevier, vol. 154(C), pages 254-270.
    18. Lee, Wen-Shing, 2010. "Benchmarking the energy performance for cooling purposes in buildings using a novel index-total performance of energy for cooling purposes," Energy, Elsevier, vol. 35(1), pages 50-54.
    19. 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).
    20. Charrad, Malika & Ghazzali, Nadia & Boiteau, Véronique & Niknafs, Azam, 2014. "NbClust: An R Package for Determining the Relevant Number of Clusters in a Data Set," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 61(i06).
    21. Lizana, Jesus & Serrano-Jimenez, Antonio & Ortiz, Carlos & Becerra, Jose A. & Chacartegui, Ricardo, 2018. "Energy assessment method towards low-carbon energy schools," Energy, Elsevier, vol. 159(C), pages 310-326.
    22. Paola Marrone & Paola Gori & Francesco Asdrubali & Luca Evangelisti & Laura Calcagnini & Gianluca Grazieschi, 2018. "Energy Benchmarking in Educational Buildings through Cluster Analysis of Energy Retrofitting," Energies, MDPI, vol. 11(3), pages 1-20, March.
    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. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    2. Arash Mohammadi Fallah & Ehsan Ghafourian & Ladan Shahzamani Sichani & Hossein Ghafourian & Behdad Arandian & Moncef L. Nehdi, 2023. "Novel Neural Network Optimized by Electrostatic Discharge Algorithm for Modification of Buildings Energy Performance," Sustainability, MDPI, vol. 15(4), pages 1-15, February.
    3. Dongsu Kim & Jongman Lee & Sunglok Do & Pedro J. Mago & Kwang Ho Lee & Heejin Cho, 2022. "Energy Modeling and Model Predictive Control for HVAC in Buildings: A Review of Current Research Trends," Energies, MDPI, vol. 15(19), pages 1-30, October.
    4. Piscitelli, Marco Savino & Giudice, Rocco & Capozzoli, Alfonso, 2024. "A holistic time series-based energy benchmarking framework for applications in large stocks of buildings," Applied Energy, Elsevier, vol. 357(C).
    5. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
    6. Kim, Dongsu & Seomun, Gu & Lee, Yongjun & Cho, Heejin & Chin, Kyungil & Kim, Min-Hwi, 2024. "Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning," Applied Energy, Elsevier, vol. 368(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. Geraldi, Matheus Soares & Ghisi, Enedir, 2022. "Integrating evidence-based thermal satisfaction in energy benchmarking: A data-driven approach for a whole-building evaluation," Energy, Elsevier, vol. 244(PB).
    2. Zhou, Yuren & Lork, Clement & Li, Wen-Tai & Yuen, Chau & Keow, Yeong Ming, 2019. "Benchmarking air-conditioning energy performance of residential rooms based on regression and clustering techniques," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    3. Arjunan, Pandarasamy & Poolla, Kameshwar & Miller, Clayton, 2020. "EnergyStar++: Towards more accurate and explanatory building energy benchmarking," Applied Energy, Elsevier, vol. 276(C).
    4. Andrews, Abigail & Jain, Rishee K., 2022. "Beyond Energy Efficiency: A clustering approach to embed demand flexibility into building energy benchmarking," Applied Energy, Elsevier, vol. 327(C).
    5. Li, Tian & Bie, Haipei & Lu, Yi & Sawyer, Azadeh Omidfar & Loftness, Vivian, 2024. "MEBA: AI-powered precise building monthly energy benchmarking approach," Applied Energy, Elsevier, vol. 359(C).
    6. Roth, Jonathan & Lim, Benjamin & Jain, Rishee K. & Grueneich, Dian, 2020. "Examining the feasibility of using open data to benchmark building energy usage in cities: A data science and policy perspective," Energy Policy, Elsevier, vol. 139(C).
    7. Piscitelli, Marco Savino & Giudice, Rocco & Capozzoli, Alfonso, 2024. "A holistic time series-based energy benchmarking framework for applications in large stocks of buildings," Applied Energy, Elsevier, vol. 357(C).
    8. Roth, Jonathan & Rajagopal, Ram, 2018. "Benchmarking building energy efficiency using quantile regression," Energy, Elsevier, vol. 152(C), pages 866-876.
    9. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
    10. Liu, Jiangyan & Chen, Huanxin & Liu, Jiahui & Li, Zhengfei & Huang, Ronggeng & Xing, Lu & Wang, Jiangyu & Li, Guannan, 2017. "An energy performance evaluation methodology for individual office building with dynamic energy benchmarks using limited information," Applied Energy, Elsevier, vol. 206(C), pages 193-205.
    11. Attia, Shady & Shadmanfar, Niloufar & Ricci, Federico, 2020. "Developing two benchmark models for nearly zero energy schools," Applied Energy, Elsevier, vol. 263(C).
    12. Jeong, Jaewook & Hong, Taehoon & Ji, Changyoon & Kim, Jimin & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan, 2017. "Improvements of the operational rating system for existing residential buildings," Applied Energy, Elsevier, vol. 193(C), pages 112-124.
    13. Salah Vaisi & Saleh Mohammadi & Benedetto Nastasi & Kavan Javanroodi, 2020. "A New Generation of Thermal Energy Benchmarks for University Buildings," Energies, MDPI, vol. 13(24), pages 1-18, December.
    14. Vaisi, Salah & Varmazyari, Pouya & Esfandiari, Masoud & Sharbaf, Sara A., 2023. "Developing a multi-level energy benchmarking and certification system for office buildings in a cold climate region," Applied Energy, Elsevier, vol. 336(C).
    15. Liu, Jiangyan & Wang, Jiangyu & Li, Guannan & Chen, Huanxin & Shen, Limei & Xing, Lu, 2017. "Evaluation of the energy performance of variable refrigerant flow systems using dynamic energy benchmarks based on data mining techniques," Applied Energy, Elsevier, vol. 208(C), pages 522-539.
    16. Khayatian, Fazel & Sarto, Luca & Dall'O', Giuliano, 2017. "Building energy retrofit index for policy making and decision support at regional and national scales," Applied Energy, Elsevier, vol. 206(C), pages 1062-1075.
    17. Wang, Endong, 2015. "Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach," Applied Energy, Elsevier, vol. 146(C), pages 92-103.
    18. Park, Hyo Seon & Lee, Minhyun & Kang, Hyuna & Hong, Taehoon & Jeong, Jaewook, 2016. "Development of a new energy benchmark for improving the operational rating system of office buildings using various data-mining techniques," Applied Energy, Elsevier, vol. 173(C), pages 225-237.
    19. 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).
    20. Amasyali, Kadir & El-Gohary, Nora M., 2018. "A review of data-driven building energy consumption prediction studies," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1192-1205.

    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:306:y:2022:i:pa:s0306261921012678. 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.