Development of multi-parametric functional index model for evaluating the indoor comfort in built environment

Abstract

Due to rapid urbanization of developing cities, the planners have a major challenge in designing climate responsive built environment for modern urban dwellers. In order to maintain a good built environmental quality, the various key design parameters of existing built form would need to be reviewed and analyzed to seek appropriate improvement. The present paper highlights the case study of a test room model in an educational campus. The experimentation was carried out in composite climatic zone of Nagpur in India. The desired built environment parameters viz., indoor light intensity, temperature and humidity was recorded and analyzed during experiments under controlled conditions of room openings. A functional index model was developed by this study using the multi-parametric regression analysis of the results. The qualitative assessment of the aforesaid parameters in terms of indoor comfort in the built environment was carried out. The developed mathematical index model for various functional parameters over the given geographic location can suitably be used by the designers for evaluating and enhancing the indoor comfort of a built environment suitable for the climate of Nagpur, India.

The impact of indoor environment quality (IEQ) on school children's overall comfort in the UK; a regression approach

“…Results reveal that thermal comfort is the most important aspect (W Thermal = 0.34), followed by visual comfort (WVisual =0.31), acoustic comfort (WAcoustic=0.26) and IAQ (WIAQ = 0.08) [34]. Several other studies have suggested the most important aspect(s) of IEQ in educational buildings [11,35,36], Table 2. Astolfi and Pellerey (2008) have carried out a subjective survey on perceived environmental quality in 51 secondary-school classrooms in Italy [11].…”

“…Results show that with the same dissatisfaction for thermal, IAQ and acoustical, students attributed more weight to the acoustical conditions in the overall quality judgment [11]. have developed a comfort model by using multi-parametric regression analysis of the results in a test room model in an educational campus in India [35]. Indoor environmental variables including indoor light intensity, temperature and humidity were recorded from July 2011-March 2012 on an hourly basis [35].…”

“…have developed a comfort model by using multi-parametric regression analysis of the results in a test room model in an educational campus in India [35]. Indoor environmental variables including indoor light intensity, temperature and humidity were recorded from July 2011-March 2012 on an hourly basis [35]. For the developed model, the considered functional parameters are prioritized by relative importance to indoor temperature, relative humidity and daylight [35].…”

“…Indoor environmental variables including indoor light intensity, temperature and humidity were recorded from July 2011-March 2012 on an hourly basis [35]. For the developed model, the considered functional parameters are prioritized by relative importance to indoor temperature, relative humidity and daylight [35]. The study by Kim, et.al.…”

Grey preference analysis of indoor environmental factors using sub-indexes based on Weber/Fechner's law and predicted mean vote

“…[3][4][5][6] An indoor environment can be affected by many factors and four factors are generally considered when evaluating IEQ: [7][8][9] thermal environment, acoustic environment, indoor air quality (IAQ) and light environment. Various methods for IEQ evaluations have been developed by some scholars, such as: the analytic hierarchy process method, 5 multivariate logistic regression model, 10 multi-parametric regression analysis, 11 post-occupancy evaluation 3 and a few others. IEQ evaluation indexes, such as the standard index, 7 using a piecewise linear interpolation method 12 were also developed.…”