IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v15y2022i4p1328-d747469.html
   My bibliography  Save this article

Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives

Author

Listed:
  • Abhinandana Boodi

    (CESI Brest Campus, EA 7527 LINEACT, 29200 Brest, France)

  • Karim Beddiar

    (CESI Brest Campus, EA 7527 LINEACT, 29200 Brest, France)

  • Yassine Amirat

    (ISEN Yncréa Ouest, L@bISEN, 29200 Brest, France)

  • Mohamed Benbouzid

    (Institut de Recherche Dupuy de Lôme (UMR CNRS 6027 IRDL), University of Brest, 29238 Brest, France
    Logistics Engineering College, Shanghai Maritime University, Shanghai 201306, China)

Abstract

The development of smart buildings, as well as the great need for energy demand reduction, has renewed interest in building energy demand prediction. Intelligent controllers are a solution for optimizing building energy consumption while maintaining indoor comfort. The controller efficiency on the other hand, is mainly determined by the prediction of thermal behavior from building models. Due to the development complexity of the models, these intelligent controllers are not yet implemented on an industrial scale. There are primarily three types of building models studied in the literature: white-box, black-box, and gray-box. The gray-box models are found to be robust, efficient, of low cost computationally, and of moderate modeling complexity. Furthermore, there is no standard model configuration, development method, or operation conditions. These parameters have a significant influence on the model performance accuracy. This motivates the need for this review paper, in which we examined various gray-box models, their configurations, parametric identification techniques, and influential parameters.

Suggested Citation

  • Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2022. "Building Thermal-Network Models: A Comparative Analysis, Recommendations, and Perspectives," Energies, MDPI, vol. 15(4), pages 1-27, February.
    • Handle: RePEc:gam:jeners:v:15:y:2022:i:4:p:1328-:d:747469
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/15/4/1328/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1996-1073/15/4/1328/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Ferracuti, Francesco & Fonti, Alessandro & Ciabattoni, Lucio & Pizzuti, Stefano & Arteconi, Alessia & Helsen, Lieve & Comodi, Gabriele, 2017. "Data-driven models for short-term thermal behaviour prediction in real buildings," Applied Energy, Elsevier, vol. 204(C), pages 1375-1387.
    2. Široký, Jan & Oldewurtel, Frauke & Cigler, Jiří & Prívara, Samuel, 2011. "Experimental analysis of model predictive control for an energy efficient building heating system," Applied Energy, Elsevier, vol. 88(9), pages 3079-3087.
    3. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    4. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    5. Schmidt, Mischa & Åhlund, Christer, 2018. "Smart buildings as Cyber-Physical Systems: Data-driven predictive control strategies for energy efficiency," Renewable and Sustainable Energy Reviews, Elsevier, vol. 90(C), pages 742-756.
    6. 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.
    7. Evins, Ralph, 2013. "A review of computational optimisation methods applied to sustainable building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 22(C), pages 230-245.
    8. Antonopoulos, K.A. & Koronaki, E., 1998. "Apparent and effective thermal capacitance of buildings," Energy, Elsevier, vol. 23(3), pages 183-192.
    9. Mary B. Wilson & Rogelio Luck & Pedro J. Mago, 2015. "A First-Order Study of Reduced Energy Consumption via Increased Thermal Capacitance with Thermal Storage Management in a Micro-Building," Energies, MDPI, vol. 8(10), pages 1-17, October.
    10. Sadineni, Suresh B. & Madala, Srikanth & Boehm, Robert F., 2011. "Passive building energy savings: A review of building envelope components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 15(8), pages 3617-3631.
    11. Panchabikesan, Karthik & Haghighat, Fariborz & Mankibi, Mohamed El, 2021. "Data driven occupancy information for energy simulation and energy use assessment in residential buildings," Energy, Elsevier, vol. 218(C).
    12. Lee, Dasheng & Cheng, Chin-Chi, 2016. "Energy savings by energy management systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 760-777.
    13. Abhinandana Boodi & Karim Beddiar & Yassine Amirat & Mohamed Benbouzid, 2020. "Simplified Building Thermal Model Development and Parameters Evaluation Using a Stochastic Approach," Energies, MDPI, vol. 13(11), pages 1-23, June.
    14. Webb, Amanda L., 2017. "Energy retrofits in historic and traditional buildings: A review of problems and methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 77(C), pages 748-759.
    15. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    16. 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.
    17. Ji, Ying & Xu, Peng & Duan, Pengfei & Lu, Xing, 2016. "Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data," Applied Energy, Elsevier, vol. 169(C), pages 309-323.
    18. Ali Bagheri & Véronique Feldheim & Christos S. Ioakimidis, 2018. "On the Evolution and Application of the Thermal Network Method for Energy Assessments in Buildings," Energies, MDPI, vol. 11(4), pages 1-20, April.
    19. Pohekar, S. D. & Ramachandran, M., 2004. "Application of multi-criteria decision making to sustainable energy planning--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 8(4), pages 365-381, August.
    20. Dounis, A.I. & Caraiscos, C., 2009. "Advanced control systems engineering for energy and comfort management in a building environment--A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(6-7), pages 1246-1261, August.
    21. Wang, Zeyu & Srinivasan, Ravi S., 2017. "A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 796-808.
    22. Tashtoush, Bourhan & Molhim, M. & Al-Rousan, M., 2005. "Dynamic model of an HVAC system for control analysis," Energy, Elsevier, vol. 30(10), pages 1729-1745.
    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. Kontoleon, Karolos J. & Saboor, Shaik & Mazzeo, Domenico & Ahmad, Jawad & Cuce, Erdem, 2023. "Thermal sensitivity and potential cooling-related energy saving of masonry walls through the lens of solar heat-rejecting paints at varying orientations," Applied Energy, Elsevier, vol. 329(C).
    2. Joanna Piotrowska-Woroniak & Krzysztof Cieśliński & Grzegorz Woroniak & Jonas Bielskus, 2022. "The Impact of Thermo-Modernization and Forecast Regulation on the Reduction of Thermal Energy Consumption and Reduction of Pollutant Emissions into the Atmosphere on the Example of Prefabricated Build," Energies, MDPI, vol. 15(8), pages 1-32, April.
    3. Anh Tuan Phan & Thi Tuyet Hong Vu & Dinh Quang Nguyen & Eleonora Riva Sanseverino & Hang Thi-Thuy Le & Van Cong Bui, 2022. "Data Compensation with Gaussian Processes Regression: Application in Smart Building’s Sensor Network," Energies, MDPI, vol. 15(23), pages 1-16, December.
    4. Sameh Mahjoub & Sami Labdai & Larbi Chrifi-Alaoui & Bruno Marhic & Laurent Delahoche, 2023. "Short-Term Occupancy Forecasting for a Smart Home Using Optimized Weight Updates Based on GA and PSO Algorithms for an LSTM Network," Energies, MDPI, vol. 16(4), pages 1-18, February.
    5. Hessam Golmohamadi, 2022. "Demand-Side Flexibility in Power Systems: A Survey of Residential, Industrial, Commercial, and Agricultural Sectors," Sustainability, MDPI, vol. 14(13), pages 1-16, June.
    6. Wei, Ziqing & Ren, Fukang & Zhu, Yikang & Yue, Bao & Ding, Yunxiao & Zheng, Chunyuan & Li, Bin & Zhai, Xiaoqiang, 2022. "Data-driven two-step identification of building thermal characteristics: A case study of office building," Applied Energy, Elsevier, vol. 326(C).
    7. Piotr Michalak, 2022. "Thermal Network Model for an Assessment of Summer Indoor Comfort in a Naturally Ventilated Residential Building," Energies, MDPI, vol. 15(10), pages 1-19, May.
    8. Yue, Bao & Wei, Ziqing & Zheng, Chunyuan & Ding, Yunxiao & Li, Bin & Li, Dongdong & Liang, Xingang & Zhai, Xiaoqiang, 2023. "Power consumption prediction of variable refrigerant flow system through data-physics hybrid approach: An online prediction test in office building," Energy, Elsevier, vol. 278(PA).
    9. Alejandra Aversa & Luis Ballestero & Miguel Chen Austin, 2022. "Highlighting the Probabilistic Behavior of Occupants’ Preferences in Energy Consumption by Integrating a Thermal Comfort Controller in a Tropical Climate," Sustainability, MDPI, vol. 14(15), pages 1-16, 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. Alice Mugnini & Gianluca Coccia & Fabio Polonara & Alessia Arteconi, 2020. "Performance Assessment of Data-Driven and Physical-Based Models to Predict Building Energy Demand in Model Predictive Controls," Energies, MDPI, vol. 13(12), pages 1-18, June.
    2. Paulína Šujanová & Monika Rychtáriková & Tiago Sotto Mayor & Affan Hyder, 2019. "A Healthy, Energy-Efficient and Comfortable Indoor Environment, a Review," Energies, MDPI, vol. 12(8), pages 1-37, April.
    3. Zhan, Sicheng & Chong, Adrian, 2021. "Data requirements and performance evaluation of model predictive control in buildings: A modeling perspective," Renewable and Sustainable Energy Reviews, Elsevier, vol. 142(C).
    4. Kathirgamanathan, Anjukan & De Rosa, Mattia & Mangina, Eleni & Finn, Donal P., 2021. "Data-driven predictive control for unlocking building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 135(C).
    5. Sachin Kumar & Zairu Nisha & Jagvinder Singh & Anuj Kumar Sharma, 2022. "Sensor network driven novel hybrid model based on feature selection and SVR to predict indoor temperature for energy consumption optimisation in smart buildings," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 13(6), pages 3048-3061, December.
    6. Abhinandana Boodi & Karim Beddiar & Malek Benamour & Yassine Amirat & Mohamed Benbouzid, 2018. "Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations," Energies, MDPI, vol. 11(10), pages 1-26, September.
    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. Giovanni Bianco & Stefano Bracco & Federico Delfino & Lorenzo Gambelli & Michela Robba & Mansueto Rossi, 2020. "A Building Energy Management System Based on an Equivalent Electric Circuit Model," Energies, MDPI, vol. 13(7), pages 1-23, April.
    9. Afroz, Zakia & Shafiullah, GM & Urmee, Tania & Higgins, Gary, 2018. "Modeling techniques used in building HVAC control systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 83(C), pages 64-84.
    10. Baloch, Ashfaque Ahmed & Shaikh, Pervez Hameed & Shaikh, Faheemullah & Leghari, Zohaib Hussain & Mirjat, Nayyar Hussain & Uqaili, Muhammad Aslam, 2018. "Simulation tools application for artificial lighting in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 82(P3), pages 3007-3026.
    11. Wang, Zeyu & Liu, Jian & Zhang, Yuanxin & Yuan, Hongping & Zhang, Ruixue & Srinivasan, Ravi S., 2021. "Practical issues in implementing machine-learning models for building energy efficiency: Moving beyond obstacles," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
    12. Shaoxiong Li & Le Liu & Changhai Peng, 2020. "A Review of Performance-Oriented Architectural Design and Optimization in the Context of Sustainability: Dividends and Challenges," Sustainability, MDPI, vol. 12(4), pages 1-36, February.
    13. Alessandro Franco & Carlo Bartoli & Paolo Conti & Lorenzo Miserocchi & Daniele Testi, 2021. "Multi-Objective Optimization of HVAC Operation for Balancing Energy Use and Occupant Comfort in Educational Buildings," Energies, MDPI, vol. 14(10), pages 1-19, May.
    14. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
    15. Chen, Yibo & Zhang, Fengyi & Berardi, Umberto, 2020. "Day-ahead prediction of hourly subentry energy consumption in the building sector using pattern recognition algorithms," Energy, Elsevier, vol. 211(C).
    16. Wenquan Jin & Israr Ullah & Shabir Ahmad & Dohyeun Kim, 2019. "Occupant Comfort Management Based on Energy Optimization Using an Environment Prediction Model in Smart Homes," Sustainability, MDPI, vol. 11(4), pages 1-18, February.
    17. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    18. Muhammad Fayaz & DoHyeun Kim, 2018. "Energy Consumption Optimization and User Comfort Management in Residential Buildings Using a Bat Algorithm and Fuzzy Logic," Energies, MDPI, vol. 11(1), pages 1-22, January.
    19. Yang, Shiyu & Wan, Man Pun & Ng, Bing Feng & Dubey, Swapnil & Henze, Gregor P. & Chen, Wanyu & Baskaran, Krishnamoorthy, 2020. "Experimental study of model predictive control for an air-conditioning system with dedicated outdoor air system," Applied Energy, Elsevier, vol. 257(C).
    20. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(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:gam:jeners:v:15:y:2022:i:4:p:1328-:d:747469. 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.