Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving
Automatic Control System For Thermal Comfort Based On Predicted Mean Vote And Energy Saving
For human-centered automation, this study presents a wireless sensor network using predicte d mean vote (PMV) as a thermal comfort index around occupants in buildings. The network automatically controls air conditioning by means of changing temperature settings in air condition ers. Interior devices of air conditioners thus do not have to be replaced. An adaptive neurofuzzy inference system and a particle swarm algorithm are adopted for solving a nonlinear multivaria ble inverse PMV model so as to determine thermal comfort temperatures. In solving inverse PMV models, the particle swarm algorithm is more accurate than ANFIS according to computational r esults. Based on the comfort temperature, this study utilizes feedforward–feedback control and digital self-tuning control, respectively, to satisfy thermal comfort. The control methods are val idated by experimental results. Compared with conventional ﬁxed temperature settings, the present control methods effectively maintain the PMV value within the range of and energy is save d more than 30% in this study. Note to Practitioners—For advanced control of unitary air conditioners in rooms, air co nditioners may have to be retroﬁtted or connected with extra devices by wire connection, whose processes may be difﬁcult for users, and inappropriate installation may damage original air-cond itioning units. This study hence presents a noninvasive method for indoor thermal comfort with a wireless sensor network. The present method facilitates hardware implementation without changin g interior devices of the air conditioner. The wireless sensor network measures temperature, air velocity, and humidity around occupants and further transmits temperature commands for air conditioner control. Based on the measured data, a PMV model is adopted to evaluate thermal comfort. Using an inverse PMV model with feedforward–feedback control and selftuning cont rol, respectively, this study aims to automatically maintain human thermal comfort as well as save energy. The ANFIS model and a particle swarm algorithm are used to solve the inverse PMV model and determine the thermal comfort temperature. Based on that temperature, feedforward–feedback control, and self-tuning control are used to determine appropriate temperature s ettings in the air conditioner so as to change the cooling capacity and maintain thermal comfort. Experimental results show that the present control method can maintain thermal comfort and save s 30% more energy than the conventional method that the present control method can maintain thermal comfort and save s 30% more energy than the conventional method
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