Please use this identifier to cite or link to this item: http://localhost:8081/xmlui/handle/123456789/14593
Full metadata record
DC FieldValueLanguage
dc.contributor.authorSingh, Shailza-
dc.guideChani, P.S.-
dc.guideKulkarni, S. Y.-
dc.description.abstractIndia ranks fifth in primary energy consumption and accounts for about 3.5% of the world’s commercial energy demand (World Business Council for Sustainable Development, 2008). About 75% of this total energy-use (direct or indirect) is expended on Indian households (S. Pachauri and D. Spreng, 2002). Reports show that the cooling load of residential sector accounts for up to 45% of the total electricity consumption in India (Energy Conservation Building Codes, 2007). With the predicted rise in the growth rate of construction spending in housing (i.e. approx. 10% per annum from 2013-18) (Asia Construction Outlook, 2013) and high disposable incomes of the people (A.B. Lall,2008; D.C. Srivastava,2007), it is envisaged that the energy demand for better indoor thermal environment (through space heating/cooling) will continue to rise in the foreseeable future. Considering the highly variable climate of India, at macro and micro scale; the existing energy codes (i.e. the Energy Conservation Building Codes; ECBC) for naturally ventilated residential buildings are quite ambiguous and nonspecific. It follows a prescriptive component-based approach, where energy efficiency guidelines are applicable on only air-conditioned area of 1000 m2 (or more) having a connected load of 500kW or more (ECBC). On the other hand, thermal comfort standards (as advocated by National Building Code; NBC) follows a narrow range of indoor temperatures in summer (23-26°C) and winter (21-23°C) and is based on Fanger’s model, which overlooks the adaptive behavior & its effect on the thermal perception of the subjects. Field studies in tropical climate (Humphrey, 1977; Sharma & Ali, 1986; Nicol, 1999; Mallick, 1996; Heidari, 2002; Indraganti, 2010 etc.) have shown a broader comfort range and high comfort temperature, using adaptive model, as opposed to what is suggested by the current standards. This research has followed an integrated approach to evaluate the thermal performance of a naturally ventilated multi-storied apartments and the thermal perception of their residents in a composite climatic zone of north India. The fact that the conditions in naturally ventilated buildings is not quite comparable to those of the conditioned ones; the adaptive approach of thermal comfort has been employed. A Class II level longitudinal survey was conducted to analyze the thermal responses of the subjects, and to establish the temperature which people finds comfortable. In total, 54 apartment units are visited and 82 ii subjects were interviewed on a monthly basis for the year of 2012. The survey was fairly distributed between lower floors, middle floors and top floors, to analyze its effect on the thermal behavior of the buildings and its occupants. Chapter3 gives the detail of the study area, i.e. Chandigarh and Roorkee (composite climatic region of north India), along with the description of the longitudinal field survey that has been conducted for the studied period. The Design Builder’s (DB) v. is employed to evaluate the energy performance and thermal behavior of the surveyed buildings. Measured data and real building information is used to assign the simulation input values for walls, roof, windows etc. The operation schedules for lighting, heating &cooling system (i.e. fan, A/c’s, heater/hot blowers), occupancy etc. are also framed on the basis of responses received during the survey. The simulation arrangement of the baseline model is thoroughly explained and supported with the necessary statistical indices for validation. The Coefficient of Variation of Root Mean Squared Error (CV (RMSE) and Mean Bias Error (MBE) for the monthly electricity consumption is within the acceptable tolerances (as recommended by ASHRAE Guideline 14-2002), i.e. ±15% for CV (RMSE) and ±5% for MBE. This chapter can be referred for the dataset that has been used for the analysis of the thermal comfort of the subjects and the energy-use of the studied buildings. Thermal evaluation of the occupants in warm climate has always been debated by the propagators of the thermal comfort models (i.e. Fanger’s Model & Adaptive model). Fanger’s model is premised on the assumption that the thermal response of the subject, to the given thermal environment, is proportional to the physics of the heat and mass exchanges between the body and the environment. However, it accounts for some degrees of behavioral adaptation, such as adjustments to the clothing and local air velocity. It still undermines the psychological dimension of adaptation and its effect on the thermal perception of the subjects. Earlier field studies have shown that PMV yields satisfactory results for thermal sensation in air-conditioned buildings but overestimates the subjective thermal responses in naturally ventilated buildings (P.O. Fanger and J. Toftum, 2002). The adaptive approach, on the contrary, advocates that a person is no longer a passive recipient of the given thermal environment, but instead an active agent interacting with the personenvironment system via multiple feedback loops (G.S. Brager and R.J. de Dear, 1998). As the subject’s experience of a place is a multivariate phenomenon (A.K. Mishra and M. Ramgopal, 2013), it is important to understand the factors which stimulate the thermal sensation of the occupants. Chapter 4 is focussed on the estimation of the comfort iii temperatures, comfort range and evaluation of the behavioral adjustments of the occupants in response to the thermal discomfort. The adaptive use of various controls (‘in-built controls’, ‘seasonal controls’ and personal controls’) with the change in the seasonal variations is elaborately discussed. It is observed that at extreme weather conditions subjects are switching to the energy intensive appliances (i.e. fans, A/c’s and heaters/hot blowers) or ‘seasonal controls’ as oppose to the ‘in-built controls’ (i.e. windows, balcony doors & blinds) or ‘personal controls’ (i.e. changing clothing levels/ ‘clo’ and metabolic rates/‘met’). Fans, A/c’s and heaters work instantly at the discomfort hours and accentuate the feeling of degree of control of the subjects. With this feeling of control on the indoor conditions, the thermal perception of the occupants is elated which explains the high regression coefficient of the ‘seasonal controls’. The results have inferred that the efficient design of the building (i.e. ‘in-built controls’) is essential in order to minimize the dependence on ‘seasonal controls ‘and the resultant energy load. Thermal comfort is related to the environmental and personal variables which are, in turn, dependent on the building parameters (both physical and thermal properties). Indoor environment varies within a small time scale (Peeters et al., 2009) and depends upon the constantly changing outdoor physical variables, internal heat gains and the ventilation rates of the building. Chapter5 has evaluated the thermal performance of the surveyed multistoried apartments (all five). The energy-use analysis is conducted, using a ‘whole building calibrated simulation approach’ (ASHRAE 14-2002), to assess the effect of heat flow through the building envelope, lighting system, heating/cooling systems etc. ECBC standard is referred for the resistance (R-value) and conductance values (U-value) of each of the assembly (wall, roof) or SHGC value of glazing unit to compare the changes. Simulation results have indicated that the source of heat gain/ loss can help in identifying the design parameters that needs to be focused to optimize energy loads and the indoor comfort conditions. This study has identified ‘glazing’, ‘wall’ and ‘lighting’ as the energy intensive predictors. Internal gains through ‘solar gains through exterior windows’ and ‘zone sensible cooling’ is observed to be maximum in the baseline models, whereas heat flows through the building envelope is maximum through ‘glazing’, ‘walls’ and ‘air infiltration’. The retrofit suggestions for the identified predictors are employed, one by one, keeping all other variables same as in the baseline model. It is observed that the any change to building component has consequently affected the overall heat conduction processes of the other structural elements. It is inferred from the results that a thorough iv understanding of the interactive processes between building components is important before suggesting any retrofits. Also, parameters like orientation, window to wall ratio, building form etc. significantly affects the thermal behavior of the building. Chapter 6 gives an insight to the questions like- what affects the thermal perception of the occupants? Why are the heat conduction flows so high in some buildings whereas moderately low in others? How the building-design affects the thermal behavior of the surveyed buildings? The derived adaptive model of thermal comfort is compared with the Fanger’s PMV model. The discrepancies between the two is further extended by analyzing the demographic (age & gender) and contextual (seasonal variation & exposure to roof) variables. This basically established the, already accepted, concept that there are factors beyond the physical variables that affects the thermal perception of the occupants. It is notable that as the discomfort level surpasses the endurable thresholds of the human body it becomes important to take measures at the building level. Controlling the microclimate to reduce the effect of the outdoor temperature, reducing the direct or diffused solar gains and other heat gain/loss and allow cross ventilation, among all, are few of the significant ones (R. Gupta and M. Gregg,2012). In the later part of the chapter, therefore, parameters pertaining to the energy-use & thermal behavior of the surveyed buildings are evaluated with respect to its design. Building orientation, WWR and building form is observed to have significantly influenced the internal gains and the heat conduction gains through the building. The results presented in this study are merely a snapshot of ‘how’ and ‘what’ affects the thermal perception of the occupants along with the thermal behavior of the building. The present study has only focused on the composite climatic zone of India and, thus, the adaptive model of thermal comfort is applicable to multi-storied apartment (with similar construction strategies) in this climatic zone only.en_US
dc.subjectIndia Ranks Fifth in Primaryen_US
dc.subjectWorld's Commercial Energyen_US
dc.subjectElectricity Consumptionen_US
dc.subjectEnergy Conservation Buildingen_US
dc.typeDoctoral Thesisen_US
Appears in Collections:DOCTORAL THESES (A&P)

Files in This Item:
File Description SizeFormat 
G24495-SINGH-T.pdf7.67 MBAdobe PDFView/Open

Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.