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Artificial intelligence for buildings

Author

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  • Loveday, D. L.
  • Virk, G. S.

Abstract

For many years environments within buildings have been controlled using analogue electromechanical devices. In more recent times the advent of the microprocessor introduced Direct Digital Control (DDC) to building services. This has popularly become known as [`]BEMS' (Building Energy Management Systems). It represented a major development offering the ability to control services systems with greater flexibility than ever before whilst providing a better quality environment for the building user. This paper reviews the evolution of BEMS from its beginning in the 1970s to the present, concentrating on the control aspects. The current status of the technology in this fast-moving field is described in terms of system layout, system hardware and control techniques; current UK research is discussed, including that of the authors, together with its implications for the systems of the future. The main thrust of national control research in this area at present is towards the development of adaptive systems which maintain optimal performance as conditions change. The successful development of these self-tuning techniques will be a further step towards the truly intelligent building.

Suggested Citation

  • Loveday, D. L. & Virk, G. S., 1992. "Artificial intelligence for buildings," Applied Energy, Elsevier, vol. 41(3), pages 201-221.
    • Handle: RePEc:eee:appene:v:41:y:1992:i:3:p:201-221
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    Cited by:

    1. Petersen, Steffen & Svendsen, Svend, 2011. "Method for simulating predictive control of building systems operation in the early stages of building design," Applied Energy, Elsevier, vol. 88(12), pages 4597-4606.
    2. Petersen, Steffen & Bundgaard, Katrine Wieck, 2014. "The effect of weather forecast uncertainty on a predictive control concept for building systems operation," Applied Energy, Elsevier, vol. 116(C), pages 311-321.
    3. Wang, Yi-Fei & Chen, Qun, 2015. "A direct optimal control strategy of variable speed pumps in heat exchanger networks and experimental validations," Energy, Elsevier, vol. 85(C), pages 609-619.
    4. Shafaghat, Arezou & Keyvanfar, Ali & Abd. Majid, Muhd Zaimi & Lamit, Hasanuddin Bin & Ahmad, Mohd Hamdan & Ferwati, Mohamed Salim & Ghoshal, Sib Krishna, 2016. "Methods for adaptive behaviors satisfaction assessment with energy efficient building design," Renewable and Sustainable Energy Reviews, Elsevier, vol. 57(C), pages 250-259.

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