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The effect of weather forecast uncertainty on a predictive control concept for building systems operation

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  • Petersen, Steffen
  • Bundgaard, Katrine Wieck

Abstract

This paper investigates the effects of weather forecast uncertainty on the performance of a concept for predictive control of building systems operation. The concept uses a computational physically-based building model and weather forecasts to predict future heating or cooling requirement. This information enables the building systems to respond proactively to keep the operational temperature within the thermal comfort range with the minimum use of energy. The effect of weather forecast uncertainty was assessed using weather data from two different years in a temperate climate in the simulation of 24 building design scenarios. Despite the uncertainty in the weather forecasts, the predictive control concept demonstrated a potential for energy savings and/or improvements in thermal indoor environment when compared to a conventional rule-based control.

Suggested Citation

  • 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.
    • Handle: RePEc:eee:appene:v:116:y:2014:i:c:p:311-321
    DOI: 10.1016/j.apenergy.2013.11.060
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    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. Loveday, D. L. & Craggs, C., 1993. "Stochastic modelling of temperatures for a full-scale occupied building zone subject to natural random influences," Applied Energy, Elsevier, vol. 45(4), pages 295-312.
    3. Loveday, D. L. & Virk, G. S., 1992. "Artificial intelligence for buildings," Applied Energy, Elsevier, vol. 41(3), pages 201-221.
    4. 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.
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    Cited by:

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    17. Hou, Juan & Li, Haoran & Nord, Natasa, 2022. "Nonlinear model predictive control for the space heating system of a university building in Norway," Energy, Elsevier, vol. 253(C).
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