IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1266-d745435.html
   My bibliography  Save this article

Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need

Author

Listed:
  • Alberto Barbaresi

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Mattia Ceccarelli

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Giulia Menichetti

    (Department of Physics, Northeastern University, Boston, MA 02115, USA)

  • Daniele Torreggiani

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Patrizia Tassinari

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

  • Marco Bovo

    (Department of Agricultural and Food Sciences, University of Bologna, 40127 Bologna, Italy)

Abstract

Accurate prediction of building energy need plays a fundamental role in building design, despite the high computational cost to search for optimal energy saving solutions. An important advancement in the reduction of computational time could come from the application of machine learning models to circumvent energy simulations. With the goal of drastically limiting the number of simulations, in this paper we investigate the regression performance of different machine learning models, i.e., Support Vector Machine, Random Forest, and Extreme Gradient Boosting, trained on a small data-set of energy simulations performed on a case study building. Among the XX algorithms, the tree-based Extreme Gradient Boosting showed the best performance. Overall, we find that machine learning methods offer efficient and interpretable solutions, that could help academics and professionals in shaping better design strategies, informed by feature importance.

Suggested Citation

  • Alberto Barbaresi & Mattia Ceccarelli & Giulia Menichetti & Daniele Torreggiani & Patrizia Tassinari & Marco Bovo, 2022. "Application of Machine Learning Models for Fast and Accurate Predictions of Building Energy Need," Energies, MDPI, vol. 15(4), pages 1-16, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1266-:d:745435
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1266/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1266/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Gholami, M. & Torreggiani, D. & Tassinari, P. & Barbaresi, A., 2021. "Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
    2. Stella Tsoka & Kondylia Velikou & Konstantia Tolika & Aikaterini Tsikaloudaki, 2021. "Evaluating the Combined Effect of Climate Change and Urban Microclimate on Buildings’ Heating and Cooling Energy Demand in a Mediterranean City," Energies, MDPI, vol. 14(18), pages 1-23, September.
    3. Andra Blumberga & Gatis Bazbauers & Selina Vancane & Ivars Ijabs & Jurijs Nikisins & Dagnija Blumberga, 2021. "Unintended Effects of Energy Efficiency Policy: Lessons Learned in the Residential Sector," Energies, MDPI, vol. 14(22), pages 1-31, November.
    4. Karol Bot & Samira Santos & Inoussa Laouali & Antonio Ruano & Maria da Graça Ruano, 2021. "Design of Ensemble Forecasting Models for Home Energy Management Systems," Energies, MDPI, vol. 14(22), pages 1-37, November.
    5. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
    6. Hanaa Talei & Driss Benhaddou & Carlos Gamarra & Houda Benbrahim & Mohamed Essaaidi, 2021. "Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning," Energies, MDPI, vol. 14(19), pages 1-21, September.
    7. Francesco Causone & Rossano Scoccia & Martina Pelle & Paola Colombo & Mario Motta & Sibilla Ferroni, 2021. "Neighborhood Energy Modeling and Monitoring: A Case Study," Energies, MDPI, vol. 14(12), pages 1-19, June.
    8. Simone Ferrari & Federica Zagarella & Paola Caputo & Giuliano Dall’O’, 2021. "A GIS-Based Procedure for Estimating the Energy Demand Profiles of Buildings towards Urban Energy Policies," Energies, MDPI, vol. 14(17), pages 1-16, September.
    9. George M. Stavrakakis & Dimitris Al. Katsaprakakis & Markos Damasiotis, 2021. "Basic Principles, Most Common Computational Tools, and Capabilities for Building Energy and Urban Microclimate Simulations," Energies, MDPI, vol. 14(20), pages 1-41, October.
    10. Upma Singh & Mohammad Rizwan & Muhannad Alaraj & Ibrahim Alsaidan, 2021. "A Machine Learning-Based Gradient Boosting Regression Approach for Wind Power Production Forecasting: A Step towards Smart Grid Environments," Energies, MDPI, vol. 14(16), pages 1-21, August.
    11. Ahmed Abdelaziz & Vitor Santos & Miguel Sales Dias, 2021. "Machine Learning Techniques in the Energy Consumption of Buildings: A Systematic Literature Review Using Text Mining and Bibliometric Analysis," Energies, MDPI, vol. 14(22), pages 1-31, November.
    12. Joan Manuel Felix Benitez & Luis Alfonso del Portillo-Valdés & Victor José del Campo Díaz & Koldobika Martin Escudero, 2020. "Simulation and Thermo-Energy Analysis of Building Types in the Dominican Republic to Evaluate and Introduce Energy Efficiency in the Envelope," Energies, MDPI, vol. 13(14), pages 1-14, July.
    13. William Mounter & Chris Ogwumike & Huda Dawood & Nashwan Dawood, 2021. "Machine Learning and Data Segmentation for Building Energy Use Prediction—A Comparative Study," Energies, MDPI, vol. 14(18), pages 1-42, September.
    14. Turki Alajmi & Patrick Phelan, 2020. "Modeling and Forecasting End-Use Energy Consumption for Residential Buildings in Kuwait Using a Bottom-Up Approach," Energies, MDPI, vol. 13(8), pages 1-19, April.
    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. Fábio Antônio do Nascimento Setúbal & Sérgio de Souza Custódio Filho & Newton Sure Soeiro & Alexandre Luiz Amarante Mesquita & Marcus Vinicius Alves Nunes, 2022. "Force Identification from Vibration Data by Response Surface and Random Forest Regression Algorithms," Energies, MDPI, vol. 15(10), pages 1-15, May.
    2. Tamás Storcz & Géza Várady & István Kistelegdi & Zsolt Ercsey, 2023. "Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation," Energies, MDPI, vol. 16(16), pages 1-20, August.

    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. Tamás Storcz & Géza Várady & István Kistelegdi & Zsolt Ercsey, 2023. "Regression Models and Shape Descriptors for Building Energy Demand and Comfort Estimation," Energies, MDPI, vol. 16(16), pages 1-20, August.
    2. Martina Pelle & Francesco Causone & Laura Maturi & David Moser, 2023. "Opaque Coloured Building Integrated Photovoltaic (BIPV): A Review of Models and Simulation Frameworks for Performance Optimisation," Energies, MDPI, vol. 16(4), pages 1-20, February.
    3. Chen, Hanfei & Lin, ChihChieh & Longtin, Jon P., 2019. "Dynamic modeling and parameter optimization of a free-piston Vuilleumier heat pump with dwell-based motion," Applied Energy, Elsevier, vol. 242(C), pages 741-751.
    4. Zoltan Varga & Ervin Racz, 2022. "Machine Learning Analysis on the Performance of Dye-Sensitized Solar Cell—Thermoelectric Generator Hybrid System," Energies, MDPI, vol. 15(19), pages 1-18, October.
    5. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
    6. Eva Lucas Segarra & Germán Ramos Ruiz & Vicente Gutiérrez González & Antonis Peppas & Carlos Fernández Bandera, 2020. "Impact Assessment for Building Energy Models Using Observed vs. Third-Party Weather Data Sets," Sustainability, MDPI, vol. 12(17), pages 1-27, August.
    7. Vicente Gutiérrez González & Germán Ramos Ruiz & Carlos Fernández Bandera, 2021. "Impact of Actual Weather Datasets for Calibrating White-Box Building Energy Models Base on Monitored Data," Energies, MDPI, vol. 14(4), pages 1-16, February.
    8. Sabarathinam Srinivasan & Suresh Kumarasamy & Zacharias E. Andreadakis & Pedro G. Lind, 2023. "Artificial Intelligence and Mathematical Models of Power Grids Driven by Renewable Energy Sources: A Survey," Energies, MDPI, vol. 16(14), pages 1-56, July.
    9. António Gomes Martins & Luís Pires Neves & José Luís Sousa, 2023. "Electricity Demand Side Management," Energies, MDPI, vol. 16(16), pages 1-3, August.
    10. Yuriy Leonidovich Zhukovskiy & Margarita Sergeevna Kovalchuk & Daria Evgenievna Batueva & Nikita Dmitrievich Senchilo, 2021. "Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response," Sustainability, MDPI, vol. 13(24), pages 1-26, December.
    11. George M. Stavrakakis & Dimitris A. Katsaprakakis & Konstantinos Braimakis, 2023. "A Computational Fluid Dynamics Modelling Approach for the Numerical Verification of the Bioclimatic Design of a Public Urban Area in Greece," Sustainability, MDPI, vol. 15(15), pages 1-27, July.
    12. Wu, Wenjie & Hou, Hui & Zhu, Shaohua & Liu, Qin & Wei, Ruizeng & He, Huan & Wang, Lei & Luo, Yingting, 2024. "An intelligent power grid emergency allocation technology considering secondary disaster and public opinion under typhoon disaster," Applied Energy, Elsevier, vol. 353(PA).
    13. George Halkos, 2022. "New Assessment Methods of Future Conditions for Main Vulnerabilities and Risks from Climate Change," Energies, MDPI, vol. 15(19), pages 1-5, October.
    14. Roman V. Klyuev & Irbek D. Morgoev & Angelika D. Morgoeva & Oksana A. Gavrina & Nikita V. Martyushev & Egor A. Efremenkov & Qi Mengxu, 2022. "Methods of Forecasting Electric Energy Consumption: A Literature Review," Energies, MDPI, vol. 15(23), pages 1-33, November.
    15. Saeed Salah & Husain R. Alsamamra & Jawad H. Shoqeir, 2022. "Exploring Wind Speed for Energy Considerations in Eastern Jerusalem-Palestine Using Machine-Learning Algorithms," Energies, MDPI, vol. 15(7), pages 1-16, April.
    16. Komi Bernard Bedra & Bohong Zheng & Jiayu Li & Xi Luo, 2023. "A Parametric-Simulation Method to Study the Interconnections between Urban-Street-Morphology Indicators and Their Effects on Pedestrian Thermal Comfort in Tropical Summer," Sustainability, MDPI, vol. 15(11), pages 1-23, May.
    17. Poggi, Francesca & Amado, Miguel, 2024. "The spatial dimension of energy consumption in cities," Energy Policy, Elsevier, vol. 187(C).
    18. George M. Stavrakakis & Panagiotis L. Zervas & Konstantinos Terzis & Panagiotis Langouranis & Panagiota Saranti & Yorgos J. Stephanedes, 2023. "Exploitation of Mediterranean Cooperation Projects’ Tools for the Development of Public Buildings’ Energy Efficiency Plans at Local Level: A Case Study in Greece," Energies, MDPI, vol. 16(8), pages 1-33, April.
    19. Bartlomiej Kawa & Piotr Borkowski, 2023. "Integration of Machine Learning Solutions in the Building Automation System," Energies, MDPI, vol. 16(11), pages 1-18, June.
    20. Anastasios I. Dounis, 2022. "Machine Intelligence in Smart Buildings," Energies, MDPI, vol. 16(1), pages 1-5, December.

    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:gam:jeners:v:15:y:2022:i:4:p:1266-:d:745435. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.