Indoor Environmental Quality Assessment and Building Shading Measures Research

This paper starts with the evaluation of indoor environmental quality, and on this basis, analyzes the relationship between human comfort and various environmental factors, establishes a temperature-comfort evaluation model, and uses ECOTEC software to correlate according to the evaluation results of influencing factors. The laboratory simulation of the energy-saving measures for building shading shows that the temperature parameter is an important indicator for evaluating comfort. The relevant optimization measures such as building shading can not only save energy, but also significantly improve the environmental comfort. Introduction China's current green building assessment system puts forward higher requirements and standards for indoor environmental quality, indoor environment comfort and building energy conservation. Comfort is not a single feeling, but a combination of people and the environment, with a combination of environmental factors. Therefore, we collect the environmental parameters we need under appropriate conditions, make an accurate assessment of the excellent indoor environment, and associate it with comfort to complete the evaluation of comfort. This paper starts with the evaluation of indoor environmental quality, and analyzes the relationship between human comfort and various environmental factors, establishes a temperature-comfort evaluation model, and uses ECOTEC software to carry out relevant energy-saving measures based on the evaluation results of influencing factors. The laboratory simulation shows that the use of three-dimensional information technology to assist the design of the building can not only improve the design efficiency, but also make the design of the building more reasonable and the user more comfortable. Determination of Environmental Quality Assessment In the comprehensive environmental assessment, determining the membership function is an extremely important part, which largely determines the accuracy of data fusion. In this study, the Cauchy distribution was used to calculate membership. refers to the degree of attachment of the value of each sensor to the jth element in the set of reviews V established by it, then: ) 0 , 0 ( , ) ( 1 / 1 , 1              a a x a x a a x r ij i ij i ij i ij (1) According to the principle determined by the above comment set, combined with the relevant specifications, the membership degree of each sensor is set separately, and the normalized analysis is completed to obtain the fuzzy relationship evaluation matrix R: