IDEAS home Printed from https://ideas.repec.org/p/osf/socarx/wmeax_v1.html

Cognitive and Proactive Building Operation: Current Status and an Eight-Gap Research Agenda

Author

Listed:
  • Huemmer, Matthias

Abstract

Buildings dominate final energy demand, but operation remains reactive. This paper reviews cognitive and proactive building operation and derives a research agenda. Two questions guide the analysis: RQ1, the current status of seven constituent streams; RQ2, the gaps explaining the persistent distance between concept and practice. A critical review (July 2026) covers cognitive building concepts, MPC, RL/data-driven control, OCC, AFDD, cognitive digital twins with semantic infrastructure, and LLM-based agentic systems. Retrieval prioritised recent reviews and field deployments, with snowballing. An explicit evidence hierarchy and gap-derivation rule are stated, and bibliographic records were verified against metadata. An independent bibliometric strand uses a documented Web of Science query on cognitive, adaptive, cybernetic, and AI vocabulary, yielding 113 records (2011-2026). This corpus is profiled bibliometrically and mapped onto the gap taxonomy. The query string is reproduced verbatim, its retrieval behaviour is audited, and two artefacts materially affecting interpretation are discussed. The status pattern is consistent: conceptually rich, simulation-mature, field-poor. Bibliometrics sharpen this: 49% of records are from 2024-2026; a single 2020 review accounts for 49% of citations; only 20% are building-related (rest in software, manufacturing, general digital twins); cybernetic-building intersections contain only three records, the latest from 2015; co-word analysis reveals structural zeros, with cybernetics and self-adaptive systems never co-occurring. The query audit confirms: although the viable system model is named, exactly one of 113 records engages Beer's model, and it does not concern buildings. Outside the database, this organizing principle is applied in production management, software architecture, and agentic AI, but not in contemporary building operation. Eight gaps are derived: G1 definitional/theoretical, G2 reactive-to-proactive practice, G3 simulation-to-reality, G4 fragmentation, G5 architectural mechanism, G6 evaluation, G7 longevity/resilience, and G8 human factors/governance. G1-G4 and G6-G8 restate explicit deficits; G5 is an interpretive synthesis. The paper argues G5, the absence of an organizing principle for allocating sensing, computation, and actuation across subsystems, spatial scales, and time horizons, is structurally prior to several other gaps, and candidate principles are compared. Contributions include a cross-stream status consolidation, an evidence-anchored gap taxonomy with derivation rules, bibliometric quantitative support, and a defended keystone-gap argument. The review is evaluative rather than exhaustive and positions its findings as an evidence-based hypothesis set for a subsequent protocol-driven systematic review.

Suggested Citation

  • Huemmer, Matthias, 2026. "Cognitive and Proactive Building Operation: Current Status and an Eight-Gap Research Agenda," SocArXiv wmeax_v1, Center for Open Science.
    • Handle: RePEc:osf:socarx:wmeax_v1
    DOI: 10.31235/osf.io/wmeax_v1
    as

    Download full text from publisher

    File URL: https://osf.io/download/6a6c5419fc4106b6d30e4b92/
    Download Restriction: no
    File URL: https://libkey.io/10.31235/osf.io/wmeax_v1?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
    ---><---

    References listed on IDEAS

    as
    1. Khabbazi, Arash J. & Pergantis, Elias N. & Reyes Premer, Levi D. & Papageorgiou, Panagiotis & Lee, Alex H. & Braun, James E. & Henze, Gregor P. & Kircher, Kevin J., 2025. "Lessons learned from field demonstrations of model predictive control and reinforcement learning for residential and commercial HVAC: A review," Applied Energy, Elsevier, vol. 399(C).
    2. Saloux, Etienne & Candanedo, José A. & Vallianos, Charalampos & Morovat, Navid & Zhang, Kun, 2025. "From theory to practice: A critical review of model predictive control field implementations in the built environment," Applied Energy, Elsevier, vol. 393(C).
    3. Ma, Zhihao & Jiang, Gang & Hu, Yuqing & Chen, Jianli, 2025. "A review of physics-informed machine learning for building energy modeling," Applied Energy, Elsevier, vol. 381(C).
    4. Wang, Zhe & Hong, Tianzhen, 2020. "Reinforcement learning for building controls: The opportunities and challenges," Applied Energy, Elsevier, vol. 269(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. Hussain, Zakir & Kim, Hyemin & You, Nayoung & Cho, Honghyun, 2026. "Advances in AI-enhanced model predictive control for heat pumps: A comprehensive review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 233(C).
    2. Lei, Nuo & Zhang, Hao & Pang, Zichuan & Hu, Jingjing & Chen, Hu & Hu, Zunyan & Wang, Zhi, 2026. "Physics-informed surrogate-assisted co-optimization of fuel cell electric vehicle powertrain and energy management for long-haul heavy-duty applications," Applied Energy, Elsevier, vol. 414(C).
    3. Yassine Chemingui & Adel Gastli & Omar Ellabban, 2020. "Reinforcement Learning-Based School Energy Management System," Energies, MDPI, vol. 13(23), pages 1-21, December.
    4. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    5. Jenkins, Cody & Milcarek, Ryan J., 2026. "Solar photovoltaic system optimization and key factor identification using real commercial and industrial electrical demand profiles," Applied Energy, Elsevier, vol. 402(PB).
    6. Manfren, Massimiliano & Gonzalez-Carreon, Karla M., 2025. "Tracking decarbonisation: Scalable and interpretable data-driven methods for district energy systems," Applied Energy, Elsevier, vol. 391(C).
    7. Dominik Latoń & Jakub Grela & Andrzej Ożadowicz, 2024. "Applications of Deep Reinforcement Learning for Home Energy Management Systems: A Review," Energies, MDPI, vol. 17(24), pages 1-30, December.
    8. Davide Coraci & Silvio Brandi & Marco Savino Piscitelli & Alfonso Capozzoli, 2021. "Online Implementation of a Soft Actor-Critic Agent to Enhance Indoor Temperature Control and Energy Efficiency in Buildings," Energies, MDPI, vol. 14(4), pages 1-26, February.
    9. 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).
    10. Langer, Lissy & Volling, Thomas, 2022. "A reinforcement learning approach to home energy management for modulating heat pumps and photovoltaic systems," Applied Energy, Elsevier, vol. 327(C).
    11. Omar Al-Ani & Sanjoy Das, 2022. "Reinforcement Learning: Theory and Applications in HEMS," Energies, MDPI, vol. 15(17), pages 1-37, September.
    12. Tungom, Chia E. & Niu, Ben & Wang, Hong, 2025. "SWAPP: Swarm precision policy optimization with dynamic action bound adjustment for energy management in smart cities," Applied Energy, Elsevier, vol. 377(PA).
    13. Pinto, Giuseppe & Piscitelli, Marco Savino & Vázquez-Canteli, José Ramón & Nagy, Zoltán & Capozzoli, Alfonso, 2021. "Coordinated energy management for a cluster of buildings through deep reinforcement learning," Energy, Elsevier, vol. 229(C).
    14. Li, Yanxue & Wang, Zixuan & Xu, Wenya & Gao, Weijun & Xu, Yang & Xiao, Fu, 2023. "Modeling and energy dynamic control for a ZEH via hybrid model-based deep reinforcement learning," Energy, Elsevier, vol. 277(C).
    15. Dai, Mingkun & Li, Hangxin & Wang, Shengwei, 2023. "A reinforcement learning-enabled iterative learning control strategy of air-conditioning systems for building energy saving by shortening the morning start period," Applied Energy, Elsevier, vol. 334(C).
    16. Nweye, Kingsley & Sankaranarayanan, Siva & Nagy, Zoltan, 2023. "MERLIN: Multi-agent offline and transfer learning for occupant-centric operation of grid-interactive communities," Applied Energy, Elsevier, vol. 346(C).
    17. Piscitelli, Marco Savino & Mele, Alessandro Aniello & Razzano, Giuseppe & Capozzoli, Alfonso, 2026. "Extracting a transferable rule-based controller from deep reinforcement learning agents with validation across multiple scenarios for heat pump systems with thermal storage," Applied Energy, Elsevier, vol. 412(C).
    18. Liu, Yan & Yoon, Yeobeom & Amasyali, Kadir & Pasini, Massimiliano Lupo & Smith, Robert & Im, Piljae & Zandi, Helia, 2026. "A transfer learning approach to energy-efficient control of small and medium-sized commercial buildings," Applied Energy, Elsevier, vol. 413(C).
    19. Alexandra Brintrup & George Baryannis & Ashutosh Tiwari & Svetan Ratchev & Giovanna Martinez-Arellano & Jatinder Singh, 2023. "Trustworthy, responsible, ethical AI in manufacturing and supply chains: synthesis and emerging research questions," Papers 2305.11581, arXiv.org.
    20. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).

    More about this item

    Statistics

    Access and download statistics

    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:osf:socarx:wmeax_v1. 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: OSF (email available below). General contact details of provider: https://socarxiv.org .

    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.