SWIMMING POOL HALL HVAC MODELLING, SIMULATION AND END OF SETBACK NEURAL NETWORK PREDICTION: A DETAILED CASE STUDY
Raymond Sterling Garay, Andrea Costa, Thomas Messervey, Christian Mastrodonato, Marcus M. Keane
This paper presents a novel methodology for applying modelling and simulation techniques to a swimming pool environment aimed at achieving energy savings by optimising the end of setback (EoS) schedule of the heating, ventilation and air conditioning (HVAC) system. The Building Controls Virtual Test-Bed (BCVTB) simulation platform is used to integrate different modelling and simulation tools to improve the accuracy of the simulations while reducing modelling effort. To test the feasibility of using artificial intelligence techniques in optimising the EoS, an application using artificial neural networks (ANN) is developed and discussed. A detailed case study is also presented and discussed.
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