Dynamic Thermal Comfort Control And Energy Consumption Analysis Based On User’s Preference

In the intelligent buildings,the building automation system can effectively control and manage building equipment,which provides favorable conditions to create comfortable indoor thermal environment.However,thermal preference is not only affected by local climate,lifestyle,customs,heat experience,heat expectation,thermal environment response,personal physique and other factors,but also has certain dynamic characteristics with the change of activities,clothing,diet,and emotions.Meanwhile,the traditional steady-state thermal environment control based on PMV-PPD meets the thermal requirements of the thermal comfort for most occupants from statistical aspect,but it is difficult to meet the diversified requirements of thermal comfort for individual or group resulting in unnecessary energy waste.In response to the above problems,a dynamic evolving neural-fuzzy inference system is designed in this research.Based on the construction of online prediction model of user’s thermal comfort,a dynamic temperature control strategy is designed to optimize the control of central air conditioning system and achieve the purpose of system energy saving on the premise of satisfying individual thermal comfort.The main research contents are as follows: Firstly,an intelligent interactive system of mobile terminal is developed to further establish the data sets of user’s thermal feedback and filed measured data.Secondly,a dynamic evolving neural-fuzzy inference system is designed for different individuals to established the on-line prediction model of thermal comfort.The dynamic self-tuning of the fuzzy rules and the output function coefficients of the model are completed by using the measured learning samples to realize data-driven,and the temperature range of the occupant’s preference is predicted by reasoning.Thirdly,the dynamic temperature control strategy is constructed based on the fluctuation of outdoor environment comfort and the physiological characteristics of human thermal sensation in dynamic thermal environment.Finally,the energy consumption model of the main energy-consuming equipment of the air conditioning system is constructed,and objective function and constraints of the system are obtained.On this basis,the effective set method and particle swarm optimization algorithm are designed to search for the global optimal set points,respectively,so as to meet the comprehensive needs of dynamic thermal comfort and system energy consumption.The simulation and experimental results show that the proposed method can track the change trend of outdoor environmental comfort effectively,meet the physiological characteristics of human thermal sensation,and improve the energy saving rate of air conditioning system significantly.Meanwhile,both the thermal environment control strategy and the optimization algorithm have influences on the energy consumption of air conditioning system,but the energy saving rate of reasonable temperature control strategies is higher than that of the selection of optimization algorithm.In addition,in the optimization scheme involved in this thesis,the particle swarm optimization algorithm is combined with the dynamic temperature control strategy based on outdoor temperature to optimize the system energy consumption,which can achieve the best energy saving effect.This study can provide algorithm and data support for the establishment of user’s preference prediction model,dynamic control of indoor thermal environment,air conditioning system optimization and energy saving control strategy.