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Method for simulating predictive control of building systems operation in the early stages of building design

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  • Petersen, Steffen
  • Svendsen, Svend

Abstract

A method for simulating predictive control of building systems operation in the early stages of building design is presented. The method uses building simulation based on weather forecasts to predict whether there is a future heating or cooling requirement. This information enables the thermal control systems of the building to respond proactively to keep the operational temperature within the thermal comfort range with the minimum use of energy. The method is implemented in an existing building simulation tool designed to inform decisions in the early stages of building design through parametric analysis. This enables building designers to predict the performance of the method and include it as a part of the solution space. The method furthermore facilitates the task of configuring appropriate building systems control schemes in the tool, and it eliminates time consuming manual reconfiguration when making parametric analysis. A test case featuring an office located in Copenhagen, Denmark, indicates that the method has a potential to save energy and improve thermal comfort compared to more conventional systems control. Further investigations of this potential and the general performance of the method are, however, needed before implementing it in a real building design.

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  • 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.
    • Handle: RePEc:eee:appene:v:88:y:2011:i:12:p:4597-4606
    DOI: 10.1016/j.apenergy.2011.05.053
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    1. Hou, Zhijian & Lian, Zhiwei & Yao, Ye & Yuan, Xinjian, 2006. "Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique," Applied Energy, Elsevier, vol. 83(9), pages 1033-1046, September.
    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.
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