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• W2019475221 abstract "This work presents a hybrid model to be used for effectively controlling indoor thermal comfort in a heating, ventilating and air conditioning (HVAC) system. The first modeling part is related to the building structure and its fixture. Since building models contain many nonlinearities and have large thermal inertia and high delay time, empirical calculations based on the residential load factor (RLF) is adopted to represent the model. The second part is associated with the indoor thermal comfort itself. To evaluate indoor thermal comfort situations, predicted mean vote (PMV) and predicted percentage of dissatisfaction (PPD) indicators were used. This modeling part is represented as a fuzzy PMV/PPD model which is regarded as a white-box model. This modeling is achieved using a Takagi-Sugeno (TS) fuzzy model and tuned by Gauss-Newton method for nonlinear regression (GNMNR) algorithm. The main reason for combining the two models is to obtain a proper reference signal for the HVAC system. Unlike the widely used temperature reference signal, the proposed reference signal resulting from this work is closely related to thermal sensation comfort; Temperature is one of the factors affecting the thermal comfort but is not the main measure, and therefore, it is insignificant to control thermal comfort when the temperature is used as the reference for the HVAC system. The overall proposed model is tested on a wide range of parameter variation. The corresponding results show that a good modeling capability is achieved without employing any complicated optimization procedures for structure identification with the TS model." @default.
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• W2019475221 date "2012-03-01" @default.
• W2019475221 modified "2023-09-30" @default.
• W2019475221 title "RLF and TS fuzzy model identification of indoor thermal comfort based on PMV/PPD" @default.
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• W2019475221 doi "https://doi.org/10.1016/j.buildenv.2011.09.012" @default.
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