Automatic Control System for Thermal Comfort Based on Predicted Mean Vote and Energy Saving

Abstract

For human-centered automation, this study presents a wireless sensor network using predicted 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 conditioners. 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 multivariable 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 results. 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 validated 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 saved more than 30% in this study.Note to Practitioners-For advanced control of unitary air conditioners in rooms, air conditioners 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-conditioning units. This study hence presents a noninvasive method for indoor thermal comfort with a wireless sensor network. The present method facilitates hardware implementation without changing 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 control, 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 settings 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 saves 30% more energy than the conventional method.

Effects of Building Design Elements on Residential Thermal Environment

“…The free version of ENVI-met can output PMV value and has been used by several researchers. The research results show that PMV can accurately characterize outdoor thermal comfort in a local region [6,51].…”

Real-time estimation of thermal comfort indices in an office building with a solar powered HVAC system

“…On the other hand, it is possible to make the mean radiant temperature equal to the air temperature. Due to the difficulty to measure this variable, this approximation is suggested by some researchers [11], [12].…”

Performance Analysis of Data-Driven and Model-Based Control Strategies Applied to a Thermal Unit Model

“…The main advantage of ANN controllers is represented by their interesting features of automatic learning, easy adaptation and straightforward generalisation. However, more efficient solutions were proposed and based on the ANFIS tool [15][16][17][18]. In particular, in [15], ANFIS was successfully exploited as an alternative control strategy with heating, ventilation and air conditioning (HVAC) systems to achieve accurate tracking errors.…”