IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v380y2025ics0306261924023961.html
   My bibliography  Save this article

Demonstration of a low-cost solution for implementing MPC in commercial buildings with legacy equipment

Author

Listed:
  • Walnum, Harald Taxt
  • Sartori, Igor
  • Ward, Peder
  • Gros, Sebastien

Abstract

Model predictive control (MPC) approaches for HVAC systems in buildings have shown potential for significant reduction of energy use and for enabling flexible operation. However, a widespread adoption is long in coming, due to the high implementation costs and efforts, and data availability and quality. In this article, we demonstrate the implementation of a scalable, minimalistic and low-cost approach for replacing weather compensated control (WCC) with MPC in commercial buildings with legacy equipment. The concept utilizes the ventilation extract temperature as a proxy for indoor temperature to avoid excessive sensor installation and data treatment. The experiment demonstrates the applicability of the solution and indicates an energy cost saving of 33% during one month in early spring. While several points for improvement of the control algorithm are highlighted, the implementation shows robust behaviour even with up to eight hours gaps in the measurement updates.

Suggested Citation

  • Walnum, Harald Taxt & Sartori, Igor & Ward, Peder & Gros, Sebastien, 2025. "Demonstration of a low-cost solution for implementing MPC in commercial buildings with legacy equipment," Applied Energy, Elsevier, vol. 380(C).
    • Handle: RePEc:eee:appene:v:380:y:2025:i:c:s0306261924023961
    DOI: 10.1016/j.apenergy.2024.125012
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261924023961
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.apenergy.2024.125012?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. 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.
    2. Blum, David & Wang, Zhe & Weyandt, Chris & Kim, Donghun & Wetter, Michael & Hong, Tianzhen & Piette, Mary Ann, 2022. "Field demonstration and implementation analysis of model predictive control in an office HVAC system," Applied Energy, Elsevier, vol. 318(C).
    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. Lago, Jesus & Marcjasz, Grzegorz & De Schutter, Bart & Weron, Rafał, 2021. "Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark," Applied Energy, Elsevier, vol. 293(C).
    5. 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).
    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. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    2. Liu, Mingzhe & Guo, Mingyue & Fu, Yangyang & O’Neill, Zheng & Gao, Yuan, 2024. "Expert-guided imitation learning for energy management: Evaluating GAIL’s performance in building control applications," Applied Energy, Elsevier, vol. 372(C).
    3. Knudsen, Michael Dahl & Georges, Laurent & Skeie, Kristian Stenerud & Petersen, Steffen, 2021. "Experimental test of a black-box economic model predictive control for residential space heating," Applied Energy, Elsevier, vol. 298(C).
    4. Chen, Yibo & Gao, Junxi & Yang, Jianzhong & Berardi, Umberto & Cui, Guoyou, 2023. "An hour-ahead predictive control strategy for maximizing natural ventilation in passive buildings based on weather forecasting," Applied Energy, Elsevier, vol. 333(C).
    5. Yang, Shiyu & Oliver Gao, H. & You, Fengqi, 2022. "Model predictive control in phase-change-material-wallboard-enhanced building energy management considering electricity price dynamics," Applied Energy, Elsevier, vol. 326(C).
    6. Hu, Zehuan & Gao, Yuan & Sun, Luning & Mae, Masayuki & Imaizumi, Taiji, 2024. "Improved robust model predictive control for residential building air conditioning and photovoltaic power generation with battery energy storage system under weather forecast uncertainty," Applied Energy, Elsevier, vol. 371(C).
    7. Deng, Zhipeng & Wang, Xuezheng & Jiang, Zixin & Zhou, Nianxin & Ge, Haiwang & Dong, Bing, 2023. "Evaluation of deploying data-driven predictive controls in buildings on a large scale for greenhouse gas emission reduction," Energy, Elsevier, vol. 270(C).
    8. Chen, Wei-Han & You, Fengqi, 2024. "Sustainable energy management and control for Decarbonization of complex multi-zone buildings with renewable solar and geothermal energies using machine learning, robust optimization, and predictive c," Applied Energy, Elsevier, vol. 372(C).
    9. Guo, Yurun & Wang, Shugang & Wang, Jihong & Zhang, Tengfei & Ma, Zhenjun & Jiang, Shuang, 2024. "Key district heating technologies for building energy flexibility: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 189(PB).
    10. 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.
    11. Muniak, Damian Piotr, 2014. "A new methodology to determine the pre-setting of the control valve in a heating installation. A general model," Applied Energy, Elsevier, vol. 135(C), pages 35-42.
    12. Molinari, Marco & Anund Vogel, Jonas & Rolando, Davide & Lundqvist, Per, 2023. "Using living labs to tackle innovation bottlenecks: the KTH Live-In Lab case study," Applied Energy, Elsevier, vol. 338(C).
    13. Bartosz Uniejewski, 2024. "Regularization for electricity price forecasting," Operations Research and Decisions, Wroclaw University of Science and Technology, Faculty of Management, vol. 34(3), pages 267-286.
    14. Reynolds, Jonathan & Rezgui, Yacine & Kwan, Alan & Piriou, Solène, 2018. "A zone-level, building energy optimisation combining an artificial neural network, a genetic algorithm, and model predictive control," Energy, Elsevier, vol. 151(C), pages 729-739.
    15. Galarneau-Vincent, Rémi & Gauthier, Geneviève & Godin, Frédéric, 2023. "Foreseeing the worst: Forecasting electricity DART spikes," Energy Economics, Elsevier, vol. 119(C).
    16. Khatibi, Mahmood & Rahnama, Samira & Vogler-Finck, Pierre & Dimon Bendtsen, Jan & Afshari, Alireza, 2023. "Towards designing an aggregator to activate the energy flexibility of multi-zone buildings using a hierarchical model-based scheme," Applied Energy, Elsevier, vol. 333(C).
    17. 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.
    18. Karol Pilot & Alicja Ganczarek-Gamrot & Krzysztof Kania, 2024. "Dealing with Anomalies in Day-Ahead Market Prediction Using Machine Learning Hybrid Model," Energies, MDPI, vol. 17(17), pages 1-20, September.
    19. Hauzenberger, Niko & Pfarrhofer, Michael & Rossini, Luca, 2025. "Sparse time-varying parameter VECMs with an application to modeling electricity prices," International Journal of Forecasting, Elsevier, vol. 41(1), pages 361-376.
    20. 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.

    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:380:y:2025:i:c:s0306261924023961. 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.