IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v17y2024i2p376-d1317672.html
   My bibliography  Save this article

From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices

Author

Listed:
  • Amir Shahcheraghian

    (Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

  • Hatef Madani

    (Department of Energy Technology, KTH Royal Institute of Technology, SE-100 44 Stockholm, Sweden)

  • Adrian Ilinca

    (Department of Mechanical Engineering, École de Technologie Supérieure, Montreal, QC H3C 1K3, Canada)

Abstract

Buildings consume significant energy worldwide and account for a substantial proportion of greenhouse gas emissions. Therefore, building energy management has become critical with the increasing demand for sustainable buildings and energy-efficient systems. Simulation tools have become crucial in assessing the effectiveness of buildings and their energy systems, and they are widely used in building energy management. These simulation tools can be categorized into white-box and black-box models based on the level of detail and transparency of the model’s inputs and outputs. This review publication comprehensively analyzes the white-box, black-box, and web tool models for building energy simulation tools. We also examine the different simulation scales, ranging from single-family homes to districts and cities, and the various modelling approaches, such as steady-state, quasi-steady-state, and dynamic. This review aims to pinpoint the advantages and drawbacks of various simulation tools, offering guidance for upcoming research in the field of building energy management. We aim to help researchers, building designers, and engineers better understand the available simulation tools and make informed decisions when selecting and using them.

Suggested Citation

  • Amir Shahcheraghian & Hatef Madani & Adrian Ilinca, 2024. "From White to Black-Box Models: A Review of Simulation Tools for Building Energy Management and Their Application in Consulting Practices," Energies, MDPI, vol. 17(2), pages 1-45, January.
    • Handle: RePEc:gam:jeners:v:17:y:2024:i:2:p:376-:d:1317672
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/17/2/376/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/17/2/376/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Fan, Cheng & Xiao, Fu & Zhao, Yang, 2017. "A short-term building cooling load prediction method using deep learning algorithms," Applied Energy, Elsevier, vol. 195(C), pages 222-233.
    2. Webb, Matthew & Aye, Lu & Green, Ray, 2018. "Simulation of a biomimetic façade using TRNSYS," Applied Energy, Elsevier, vol. 213(C), pages 670-694.
    3. Blonsky, Michael & Maguire, Jeff & McKenna, Killian & Cutler, Dylan & Balamurugan, Sivasathya Pradha & Jin, Xin, 2021. "OCHRE: The Object-oriented, Controllable, High-resolution Residential Energy Model for Dynamic Integration Studies," Applied Energy, Elsevier, vol. 290(C).
    4. Salkuti, Surender Reddy, 2019. "Day-ahead thermal and renewable power generation scheduling considering uncertainty," Renewable Energy, Elsevier, vol. 131(C), pages 956-965.
    5. 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.
    6. Eva Schito & Elena Lucchi, 2023. "Advances in the Optimization of Energy Use in Buildings," Sustainability, MDPI, vol. 15(18), pages 1-3, September.
    7. Choo, Kim-Kwang Raymond & Ashman, Helen, 2016. "Context-oriented web application protection modelAuthor-Name: Prokhorenko, Victor," Applied Mathematics and Computation, Elsevier, vol. 285(C), pages 59-78.
    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. Fan, Cheng & Sun, Yongjun & Zhao, Yang & Song, Mengjie & Wang, Jiayuan, 2019. "Deep learning-based feature engineering methods for improved building energy prediction," Applied Energy, Elsevier, vol. 240(C), pages 35-45.
    2. Amir Shahcheraghian & Adrian Ilinca, 2024. "Advanced Machine Learning Techniques for Energy Consumption Analysis and Optimization at UBC Campus: Correlations with Meteorological Variables," Energies, MDPI, vol. 17(18), pages 1-22, September.
    3. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    4. Moaaz Elkabalawy & Abobakr Al-Sakkaf & Eslam Mohammed Abdelkader & Ghasan Alfalah, 2024. "CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors," Sustainability, MDPI, vol. 16(17), pages 1-21, August.
    5. Liu, Che & Sun, Bo & Zhang, Chenghui & Li, Fan, 2020. "A hybrid prediction model for residential electricity consumption using holt-winters and extreme learning machine," Applied Energy, Elsevier, vol. 275(C).
    6. Fu, Chun & Miller, Clayton, 2022. "Using Google Trends as a proxy for occupant behavior to predict building energy consumption," Applied Energy, Elsevier, vol. 310(C).
    7. 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.
    8. Rongjiang Ma & Shen Yang & Xianlin Wang & Xi-Cheng Wang & Ming Shan & Nanyang Yu & Xudong Yang, 2020. "Systematic Method for the Energy-Saving Potential Calculation of Air-Conditioning Systems via Data Mining. Part I: Methodology," Energies, MDPI, vol. 14(1), pages 1-15, December.
    9. 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.
    10. Fan, Cheng & Sun, Yongjun & Xiao, Fu & Ma, Jie & Lee, Dasheng & Wang, Jiayuan & Tseng, Yen Chieh, 2020. "Statistical investigations of transfer learning-based methodology for short-term building energy predictions," Applied Energy, Elsevier, vol. 262(C).
    11. 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.
    12. Molina-Solana, Miguel & Ros, María & Ruiz, M. Dolores & Gómez-Romero, Juan & Martin-Bautista, M.J., 2017. "Data science for building energy management: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 70(C), pages 598-609.
    13. Gabriel Gomez-Ruiz & Reyes Sanchez-Herrera & Jose M. Andujar & Juan Luis Rubio Sanchez, 2024. "Simulation-Based Education Tool for Understanding Thermostatically Controlled Loads," Sustainability, MDPI, vol. 16(3), pages 1-24, January.
    14. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    15. Jessica Walther & Matthias Weigold, 2021. "A Systematic Review on Predicting and Forecasting the Electrical Energy Consumption in the Manufacturing Industry," Energies, MDPI, vol. 14(4), pages 1-24, February.
    16. 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.
    17. Wei, Ziqing & Zhang, Tingwei & Yue, Bao & Ding, Yunxiao & Xiao, Ran & Wang, Ruzhu & Zhai, Xiaoqiang, 2021. "Prediction of residential district heating load based on machine learning: A case study," Energy, Elsevier, vol. 231(C).
    18. Stefano Villa & Claudio Sassanelli, 2020. "The Data-Driven Multi-Step Approach for Dynamic Estimation of Buildings’ Interior Temperature," Energies, MDPI, vol. 13(24), pages 1-23, December.
    19. Robinson, Caleb & Dilkina, Bistra & Hubbs, Jeffrey & Zhang, Wenwen & Guhathakurta, Subhrajit & Brown, Marilyn A. & Pendyala, Ram M., 2017. "Machine learning approaches for estimating commercial building energy consumption," Applied Energy, Elsevier, vol. 208(C), pages 889-904.
    20. Zhou, Yuekuan & Zheng, Siqian, 2020. "Climate adaptive optimal design of an aerogel glazing system with the integration of a heuristic teaching-learning-based algorithm in machine learning-based optimization," Renewable Energy, Elsevier, vol. 153(C), pages 375-391.

    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:17:y:2024:i:2:p:376-:d:1317672. 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.