| Smart home refers to a residence as a platform to provide a certain degree of digital connectivity or optimization services for occupants,forming an intelligent system that integrates system,structure,service and management.The indoor environment inside the smart home always takes comfort as the pursuit goal.To maintain thermal comfort inside the building,a certain amount of energy is consumed to supply the normal operation of end devices,and to achieve energy saving and emission reduction,often at the expense of the user’s living comfort.Therefore,the most important issue is to balance the high requirements of occupants regarding comfort with the high energy consumption.In addition to this there are problems such as individual differences in the definition of comfort and the existence of more disturbing environments.In this paper,the problems between energy saving and comfort management in the rooms in the building are fully analyzed,argued and reasonable control methods are developed to try to achieve a balance between energy saving and comfort in the smart home building and to provide conditions for optimal scheduling and efficient use of energy.The main work of this paper is as follows.(1)Quantifying and analyzing the evaluation index of thermal comfort,optimizing the prediction process of PMV index,achieving a prediction accuracy of 94.57% and reducing the prediction time by 65%,and achieving a satisfactory prediction effect.(2)Through the analysis and comparison of control variables,control methods and control strategies,a comprehensive control strategy for indoor environment is provided,and the innovative introduction and optimization of window opening and ventilation module reacts to the positive impact of this behavior on indoor comfort and energy consumption.(3)The heat transfer form of building rooms is fully considered and simulation is carried out using typical annual meteorological data.From the comfort perspective and other control methods are compared horizontally,the air conditioning outlet temperature,indoor air temperature and indoor PMV indexes are selected to complete a time-by-time comparison under typical working conditions in summer and winter,and satisfactory results are obtained.The results prove that due to the comprehensive consideration of factors and continuous adjustment and coordination of parameters such as indoor air temperature,the PMV indexes in summer and winter are basically guaranteed to fluctuate within the pre-set interval and achieve comfortable control purposes.(4)Following the building room model and meteorological data,the indoor loads under typical working conditions in summer and winter were selected for time-by-time comparison and analysis from the perspective of energy efficiency,and satisfactory results were also obtained.The results prove that the control strategy can save 68.08% of indoor energy consumption by opening windows in the morning and evening usually in summer time,and 69.56% in winter by opening windows often in the midday.(5)Taking indoor environmental comfort and energy saving as the common optimization goal,we find the balance point between the two,complete the user interface of the control platform,and reverse infer the correctness of the control strategy through typical case analysis,and realize the landing of the control platform for indoor environmental comfort and energy saving in smart homes.This paper starts from the prediction of thermal comfort of indoor environment,optimizes the prediction process with the goal of high prediction accuracy and fast prediction speed,and monitors the level of indoor thermal environment in real time;then proposes the indoor joint control strategy,and demonstrates the superiority of the control system from two perspectives of comfort and energy saving,and finally completes the prediction,control,simulation and platform design for indoor thermal environment in smart home environment,etc.A series of comprehensive researches are completed for prediction,control,simulation and platform design of indoor thermal environment in smart home environment. |
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