The final energy use in buildings has increased by 20% in the last decade; in 2019, it resulted in 28% of the global energy-related CO2 emissions (International Energy Agency (IEA) 2020). Heating, ventilation and air-conditioning (HVAC) is the most energy-intensive process in conventional buildings. Refrigerant-cooled split and window-type residential room air-conditioners (RACs) have been adopted for cooling purposes globally. In 2017, residential buildings accounted for approximately 70% of building energy consumption (IEA 2018).
Developing countries such as India are the prime drivers of the growth in the adoption of RACs. The energy demand for Indian buildings is projected to grow by more than five times by 2100 primarily due to RACs (Chaturvedi & Shukla 2014; US Energy Information Administration n.d.). This necessitates the adoption of RAC-centric energy efficiency policies to help limit global warming (CLASP 2017; Graham & Rawal 2019). Therefore, there are three pertinent questions for this study:
The current standards and labelling (S&L) in India address the first question through the lens of energy consumption. However, they do not account for the ability of RAC to provide thermal comfort or its environmental impact through direct and indirect emissions. This poses a gap in the understanding of the true performance of RACs.
This study attempts to address the aforementioned questions using three metrics to account for the energy efficiency, thermal comfort and environmental impact associated with RACs. Energy efficiency is assessed as the Indian seasonal energy efficiency ratio (ISEER), thermal comfort is assessed as the cooling seasonal energy consumption (CSEC), and the environmental impact is assessed as the total equivalent warming impact (TEWI). The proposed methodology calculates the three metrics subject to the static and adaptive (air-conditioned, AC; and mixed-mode, MM) thermal setpoints of 55 Indian cities and five RAC cases.
The present study is limited to small-capacity (<2.5 tons of refrigeration) RACs and two refrigerants (R410a and R32). NB: A ton of refrigeration is a unit of power used in India to describe the heat-extraction capacity of air-conditioning equipment. It is defined as the rate of heat transfer that results in the freezing or melting of 2000 lbs, or 907 kg, of pure ice at 0°C in 24 h. It is approximately equivalent to 12,000 BTU/h or 3.5 kWh. It accounts for the indoor and outdoor dry bulb temperatures to assess the thermal comfort and does not account for relative humidity (RH). It evaluates the energy-consumption-associated indirect emissions based on the emission factors identified under the Clean Development Mechanism (CDM). The study calculates the total carbon emissions based on reasoned assumptions involving leakage rate, recovery factor, refrigerant charge and operating life.
The findings of this study can benefit manufacturers in producing climate-friendly and energy efficient RACs. They will help embolden the prominence of non-refrigerant-based cooling technologies. Policymakers may benefit from deriving minimum energy performance standards and devise appropriate policies. This study can also help in predicting annual energy use due to RACs, scalable from a city level to a country level. Moreover, the three metrics of ISEER, CSEC and TEWI can help acquaint the end-user with the components of RAC performance and empower them in selecting the holistically best-performing RAC.
A quantification of RAC performance requires an enquiry from the perspectives of electrical energy consumption, thermal comfort and environmental impact. The following subsections include the findings of published research on each of the three perspectives and they discuss the relevance of this study with respect to the research gaps.
The energy performance of RACs is quantified as the ratio of the thermal energy output to the electrical energy input, known as the coefficient of performance (COP). COP is widely used to compare RACs since it can be measured instantaneously. The energy performance of RACs is quantified using the energy efficiency ratio (EER), seasonal energy efficiency ratio (SEER) and heating seasonal performance factor (HSPF), as defined in various studies (Abhyankar et al. 2017; Power Knot 2011; Shah et al. 2017; Waide et al. 2011).
The applicability of one metric over the other differs on a case-by-case basis and test conditions. Therefore, although the aforementioned metrics represent RAC energy performance, they cannot be used interchangeably. At standard temperature conditions, EER reflects the energy efficiency of an RAC at its maximum cooling capacity; however, in real-world conditions, RACs operate at the maximum cooling capacity at the hottest outdoor temperatures, which account for a small fraction of the annual operational hours. Most of the time, RACs operate on part load. SEER captures the energy efficiency of the RAC at the peak as well as part load conditions; therefore, it serves as a more appropriate metric to assess RAC energy efficiency.
SEER was first proposed under the American standard of the Air-conditioning, Heating and Refrigeration Institute (AHRI) 210/240 in the early 1970s (Waide et al. 2011). Other countries adopted similar standards for incorporating SEER (Table 1).
|India||ISO 16358-1-2013 (CSPF)|
|Japan||JISC 9612:2013, JRA 4046|
The Indian variant of SEER is termed the Indian seasonal energy efficiency ratio (ISEER). The formulation of ISEER followed the methodology prescribed in ISO 16358-1 (2013) while accounting for the Indian climatic context (Shah et al. 2016). It relies on a reference outdoor temperature bin distribution range of 24–43°C, as prescribed by IS1391-1992 (Parts 1 and 2) (Bureau of Indian Standards 1992a, 1992b; Ministry of Power 2017).
Improving the energy efficiency of RACs in India can translate to a saving of nearly 60 GW of peak energy demand by 2030 (Phadke et al. 2014). Energy efficient RACs also reserve the potential to reduce the annual energy consumption of Indian residences by 19% by 2027. However, the literature indicates that a long payback period acts as a prominent barrier to the popularisation of the Bureau of Energy Efficiency’s (BEE) star rating system among Indian consumers (AEEE 2015). Additionally, the RAC energy savings stipulated by the BEE assume an annual RAC usage of 1600 h (Ministry of Power 2017). This is not consistent with the actual number of RAC usage hours in India and may lead to a significant gap between expected and actual RAC performance.
Conventionally, RACs have been designed to operate within a marginal variation of comfortable temperatures; this was in accordance with the static thermal comfort model prescribed by American National Standards Institute/American Society of Heating, Refrigerating and Air-Conditioning Engineers (ANSI/ASHRAE) Standard 55-1992 (2017). Adoption of adaptive thermal comfort models that have a regard for varying outdoor conditions can contribute to energy efficiency and help maintain year-long indoor thermal comfort through occupant interaction (Aparicio-Ruiz et al. 2018; Sehar et al. 2017). Although studies continually proposed the applicability of adaptive operation over static, their scope was limited to centrally controlled HVAC systems and did not apply to RACs in small-scale spaces (Carlucci et al. 2018).
The climate dependence of the Indian residential buildings coupled with a higher tolerance of warm thermal sensations by the occupants helps establish the relevance of adaptive thermal comfort models for RAC operation in India (Doctor-Pingel et al. 2019). There have been efforts to understand thermal comfort in India since the mid-1980s with the development of metrics such as the tropical summer index (TSI) (Sharma & Ali 1986). Studies have also devised adaptive thermal comfort models based on the data collected from Indian buildings and occupants subject to a vast variability of climate and geography (Indraganti et al. 2014; Thirumaran and Subhashini 2014; Singh et al. 2015, 2017). However, the Indian model for adaptive comfort (IMAC) was the first model to devise thermal comfort models for naturally ventilated (NV), air-conditioned (AC) and mixed-mode (MM) building operation for all Indian climate zones (Manu et al. 2016).
The ASHRAE Global Thermal Comfort Database II includes the data collected under IMAC (Földváry Ličina et al. 2018). This model was also adopted in the nationally applicable Energy Conservation Building Code in 2017 as well as the National Building Code of India (BEE 2017; Bureau of Indian Standards 2016). The present study uses IMAC to calculate the comfortable temperature limits for AC and MM buildings.
Furthermore, Kumar et al. (2018b) included results from psychrometric lab testing of two popularly used RAC units—the results from these two cases have been referred to in the present study for relevant calculations and analyses. They also recommend further enquiries on correlating the indoor thermal setpoint with RAC energy performance—the present study attempts to bridge this research gap.
The RACs affect the environment in two ways: direct emissions due to the use of refrigerants with a high global warming potential (GWP), and indirect emissions due to the energy consumption during operation. Under direct emissions, the high-GWP refrigerants contribute to the concentration of atmospheric hydrofluorocarbons (HFCs), which are established as one of the root causes of global warming. The Kigali Amendment has categorised HFCs as a controlled substance under the Montreal Protocol, while the Kigali Cooling Efficiency Program is also working to limit the usage of HFCs (IEA 2017). Indirect emissions are accounted for while discussing the energy efficiency of RACs; particularly in Indian parlance, energy efficient RACs have the potential to reduce indirect emissions by 25% by 2027 (Kumar et al. 2018a).
The environmental impact of RACs has been studied using metrics such as TEWI, life cycle climate performance (LCCP) and GWP (Makhnatch & Khodabandeh 2014). Of these, TEWI was regarded as the most reliable metric while selecting the most environmentally friendly refrigerant. TEWI evaluates the overall impact of the RAC on global warming based on the direct emissions due to the disposal of refrigerants and indirect emissions due to the RAC energy consumption. It is measured as the mass of carbon dioxide equivalent (CO2e) and therefore has a unit of kilograms.
A TEWI analysis comparing the performance of low-GWP refrigerants revealed that the direct emission values were low due to minimised refrigerant leakage and were further reduced due to the low-GWP refrigerants (Makhnatch et al. 2019). The study also highlighted that the energy efficiency of the refrigerants had a prominent contribution to the environmental impact of the RAC. Another study points towards the need to focus on reducing annual space cooling demand (kWh) because it contributes majorly to indirect emissions (Goetzler et al. 2016). Goetzler et al. (2016) also highlighted that throughout the life span of RACs, their carbon footprint is dominated by indirect emissions primarily because of the high carbon intensity of fossil fuels used for electricity generation.
Despite the documented applicability of TEWI, it has some limitations. TEWI-associated calculations rely on assumptions concerning RAC performance, usage patterns, refrigerant properties and electricity generation efficiencies. Moreover, it is likely that the small differences in TEWI do not have any physical significance; therefore, to realistically compare the environmental impact of RACs, TEWI must relate to the systems of ‘equal duty and function’ (The Australian Institute of Refrigeration Air Conditioning and Heating 2012).
The literature suggests that ISEER is the most appropriate metric to assess the energy efficiency of RACs in India subject to the identification of the actual number of cooling hours. The actual RAC usage in India can be determined by identifying the hours that require cooling >24°C—the present study uses this method to determine the annual cooling hours for 55 Indian cities. To determine the annual cooling hours and comfortable temperature limits, the IMAC model for AC and MM buildings was identified. Finally, TEWI was identified to assess the environmental impact of RACs in India, subject to the assumptions of leakage rate, recovery factor, etc., for a feasible comparison of the five RAC cases.
This study assesses the performance of RACs in an Indian context through the three steps of literature review, data collection and calculations. The objective, methods and outcome of the three steps are summarised in Table 2.
|LITERATURE REVIEW||DATA COLLECTION||CALCULATIONS|
|Objective||To identify metrics to quantify thermal comfort, energy efficiency and the environmental impact of RACs||To prepare a database of variables affecting RAC performance and to analyse their relationships||To propose a method to quantify the metrics of thermal comfort, energy efficiency and environmental impact and to establish a correlation between them|
A total of 55 Indian cities were selected based on the availability of typical meteorological year (TMY) weather data files for the duration 2003–17 from the websiteClimate.onebuilding.org. The selection of the cities was also based on the availability of at least 200 temperature bin hours >36°C. The cities with a temperature >36°C for >200 h were identified to experience prominent RAC use, as prescribed by the thermal comfort band of IMAC (MM). Similarly, cities with a high population were also expected to experience high RAC use despite fewer temperature bin hours >36°C. They included Bengaluru, Kolkata, Mumbai and Surat. Table A in the supplemental data online includes the relevant details of the selected cities.
Subsequently, an internet search-based market survey was conducted for the Indian RAC market in 2018. The survey targeted the knowledge of–distribution of RAC capacity in the Indian market, RAC ISEER ratings, refrigerant use and RAC technology. The detailed survey methodology and findings have been described in Jain & Rawal (2019); however, a summary of the survey can be found in Section 1 in the supplemental data online. The market survey results helped define the selection criteria for RACs such as the device and manufacturer, refrigerant, cooling capacity, etc.
These details were used as an input into the software Coolselector2 to assess the performance of the RACs at various outdoor temperatures (Danfoss n.d.). Coolselector2 was the only freely available software that provided the relevant RAC performance curves. It allowed the selection of the best-suited RAC components based on parameters such as cooling capacity, refrigerant type, evaporation temperature, condensation temperature and other variables. The software also featured calculations based on the user’s requirements or standard operating conditions.
Tables B–D in the supplemental data online include details of RAC selection criteria, Coolselector2 inputs and their basis, and compressor specifications, respectively. This analysis helped identify three RACs, given as cases 3–5 in Table 3. Two additional RACs were selected from Kumar et al. (2018b). As mentioned in section 2.1, they conducted psychrometric tests on RACs in steady-state laboratory conditions; the results from these tests were used in the present study to calculate the seasonal energy performance of RACs. Therefore, a total of five RAC cases were studied; their details are given in Table 3.
The calculations depend on outdoor temperature. The reference outdoor temperature bin hours were calculated for 1 March–31 October (the cooling period), as specified by the BEE; this temperature bin distribution range was modified to 24–43°C to better represent the Indian climate conditions, as shown in Table E in the supplemental data online. Identical outdoor temperature bin hours were used to quantify the temperatures of the cities using TMY weather data.
With the temperature bins established, the metrics of cooling seasonal total load (CSTL), CSEC and cooling seasonal performance factor (CSPF; also known as ISEER) were calculated as per ISO 16358-1 (2013). The calculation methods vary as per the RAC type (fixed, two-stage, multi-stage and variable); the case-specific test temperatures, characteristics and test requirements are tabulated in Table F in the supplemental data online.
The market survey established that the RACs with inverter technology present in the Indian market are rated only for full capacity and half-capacity. Therefore, the ISEER for the chosen RAC units was calculated as per the method specified for multi-stage capacity units. The calculation of ISEER involves the calculation of CSTL and CSEC; the calculation of these two parameters also required the input of various RAC-associated parameters (Figure 1).
ISO 16358-1 (2013) defines CSPF (ISEER) as the dimensionless ratio of CSTL (Wh) and CSEC (Wh):
As shown in Figure 1, the parameter of cooling load defined at the outdoor temperature tj is represented as CL(tj); it bears an assumption that it will change linearly with the change in outdoor temperature. CL(tj) is calculated for the outdoor temperatures of t0 = 23°C and t100 = 43°C, as the per modified temperature bin calculation for India, where t0 stands for the outdoor temperature at 0% load; and t100 is the outdoor temperature at 100% load. CL(tj) is calculated using:
where Φful(t100) is the cooling capacity at temperature t100 under full-load operating conditions. The cooling capacity of RAC at the outdoor temperature (tj) under full-load operating conditions can be defined by equation (3) using the cooling capacity of the system at 34 and 43°C under full load:
Finally, CSTL is expressed as the sum of CL at each outdoor temperature (tj) multiplied by the bin hours (bj):
where a and b are case-specific parameters based on total bin hours.
Similarly, CSEC is expressed as the sum of cooling energy consumption at each outdoor temperature tj:
The three terms on the right-hand side of equation (5) are described as follows. The first term represents first-stage cyclic operation, subject to the condition:
where Φhaf(tj) is the cooling capacity at half-load, X(tj) is the operation factor, calculated using equation (6); Phaf(tj) is the cooling half-power, calculated using equation (7); and FPL(tj) is the part load factor, calculated using equation (8):
where Phaf(34) and Phaf(43) are the cooling half-powers at 34 and 43°C, respectively; and CD is the degradation coefficient (taken as 0.9).
The second term represents the second-stage cyclic operation, subject to the condition:
where Phf(tj) is the second-stage cyclic operation, calculated using equation (9); and p is a case-specific parameter based on total bin hours.
The third term represents full-capacity operation, subject to the condition:
where Pful(tj) is the total cooling power, calculated using:
where Pful(34) and Pful(43) are the cooling powers at 34 and 43°C, respectively; and Xhf(tj) is the operation factor at half-load, calculated using:
Equations (1) to (11) helped calculate the ISEER and CSEC of the five RACs cases subject to the static setpoints. The environmental impact of the RACs for the same conditions was calculated using TEWI. As mentioned in section 2.3, TEWI calculations in the Indian context necessitate some reasoned assumptions; these assumptions are tabulated in Table 4. TEWI was calculated using (The Australian Institute of Refrigeration Air Conditioning and Heating 2012):
where GWP is the global warming potential of refrigerant relative to CO2; Lannual is the leakage rate (kg/year); n is the system operating life (years); m is the refrigerant charge (kg); αrecovery is the recovery/recycling factor (0–1); Eannual is the energy consumption (kWh/year); and ß is the indirect emission factor (kg CO2/kWh).
|Leakage rate||10%||Chaturvedi et al. (2015)|
|Recovery factor||5%, 10% and 15%||Chaturvedi et al. (2015)|
|15%||Kumar et al. (2018b)|
|System operating life||10 years||Zhao et al. (2015)|
|8–10 years||Wu et al. (2019)|
|Refrigerant charge||0.21 kg/kW||Kumar et al. (2018b)|
|Indirect emission factor||0.82 kg CO2/kWh||Central Electricity Authority (2018)|
Adaptive setpoints change with respect to outdoor temperatures, as detailed in section 2.1. The first step towards calculating RAC parameters with respect to adaptive setpoints was to calculate the variable indoor setpoints with reference to outdoor temperatures ranging from 24 to 43°C for the 55 cities. This was executed using the 90% acceptability range for the IMAC model for AC and MM buildings derived by Manu et al. (2016).
The methodology under ISO 16358-1 (2013) has the inherent limitation of not accounting for the thermal comfort or environmental impact of RACs. In addition to this, the methodology adopted in this study has the following limitations:
RAC performance was assessed on the parameters of energy efficiency through ISEER, annual energy consumption through CSEC (kWh) and total carbon emissions through TEWI (CO2e). First, the three metrics were calculated for the five RAC cases using the standardised method for India stipulated by the BEE. Second, the three metrics were calculated for the five RAC cases using the proposed methodology, which provides unique temperature bin hours for the selected cities. Third, the unique temperature bin hours were modified as per the IMAC model for AC and MM buildings and the three metrics were evaluated and compared.
The nationally standardised metrics for the chosen RAC cases were calculated as per the standard method followed by the BEE. This method stipulates 1600 h as the constant temperature bin hours for the calculation of RAC-associated parameters in India, as described in section 2.2. The standard metrics of ISEER, CSEC and TEWI are given in Table 5. The rated energy performance metrics for cases 1 and 2 were referred from Kumar et al. (2018b), as mentioned in section 3.2.1, while the those for cases 3–5 were referred from Coolselector2 (Danfoss n.d.). TEWI was calculated for the five RAC cases as per the methodology described in section 3.2.1.
|CASE||RATED ENERGY PERFORMANCE||ISEER||CSEC (kWh)||TEWI (CO2e)|
The rated energy performances for cases 1 and 2 were significantly different from the ISEER calculated using the standard methodology since various products use RAC efficiency terms interchangeably and the energy ratings are not entirely accurate.
Indirect emissions generally have a stronger influence on the environmental impact of the RACs in comparison with direct emissions (Figure 2). Case 2 had distinctly low direct emissions since it used refrigerant R32, which has a nearly three times lower GWP (675) than refrigerant R410a (2088). However, the overall TEWI value also demonstrates that a low GWP refrigerant is not sufficient to improve the overall environmental impact of RACs.
CSEC and TEWI were the highest for case 1. The CSEC of case 2 was higher than that of case 3; however, the TEWI of case 2 was marginally lower than that of case 3 due to the reduced direct emissions. The direct emissions of all the cases except case 2 were found to be similar due to identical refrigerant. The variations between these cases were observed due to the energy performance of RACs.
The standard method of estimating RAC performance caters to an average temperature condition representative of all Indian climates; in order to localise the temperature conditions, the outdoor bin temperatures were calculated for the selected cities. The total number of bin hours and their temperature ranges (Figure 3) show that most of the hours requiring RAC operation in India correspond to the temperature range of 24–35°C. The bin hours were divided into four temperature ranges:
ISEER was found to be the highest for Bikaner and lowest for Bengaluru for all the five RAC cases. Case 1 had the lowest ISEER, while case 4 had the highest ISEER; the ascending order of ISEER is given as cases 1 < 2 < 3 < 5 < 4. This implies that case 4 would have proven to be the most energy efficient across all cities, while case 1 would have been the least.
Case 3 had the highest variation in its ISEER at 1.16, while case 1 had the least variation at 0.71. This implies that the energy efficiency of case 3 was the most sensitive to the outdoor climatic conditions, while it was the least sensitive for case 1. The maximum and minimum ISEER for the five cases are compared against the standard ISEER values in Table 6. The standard ISEER was found to be within the range of the ISEER calculated using the static setpoint methodology.
|CASE||STANDARD ISEER||MAXIMUM ISEER||MINIMUM ISEER||ISEER VARIATION|
In practical terms, if an RAC (for instance, case 3) is operated in Bikaner, its energy efficiency (ISEER) will be 17% higher than the standard value, whereas if the same RAC is operated in Bengaluru, its efficiency will be 16% lower than the standard value. The difference between the calculated and standard values arises from the difference in the number of hours of operation as well as outdoor temperature.
A high ISEER translates to low energy consumption for RACs. However, the total amount of energy consumed for cooling, represented by CSEC, also depends on the total number of hours that require RAC operation. Figure 5 shows the variation of CSEC for the five RAC cases across the selected cities.
Even though the ISEER for Bengaluru was identified to be the lowest, its CSEC was found to be the lowest by the virtue of its low bin hours. On the other hand, the CSEC of Nellore was found to be the highest due to the same influencing factor. Corresponding to the inverse relationship between energy efficiency and energy consumption, case 4 had the lowest CSEC while case 1 had the highest CSEC. The ascending order of CSEC is given as cases 4 < 5 < 3 < 2 < 1; implying that case 1 would have had the highest total energy consumption across all cities, while case 4 would have had the least total energy consumption.
Case 1 had the highest variation in its CSEC at 2917 kWh, while case 4 had the least variation at 1920 kWh. This implies that the total energy consumption of case 1 was the most sensitive to the outdoor climatic conditions while it was the least sensitive for case 4. The maximum and minimum CSEC for the five cases across the selected cities are listed in Table 7.
|CASE||MAXIMUM CSEC (kWh)||MINIMUM CSEC (kWh)||CSEC VARIATION (kWh)|
In practical terms, if an RAC (for instance, case 3) is operated in Nellore, its overall energy consumption during the cooling season will be 130% higher than if the same RAC is operated in Bengaluru. Similar to ISEER, the basis of this difference is the variation in the number of cooling hours as well as outdoor temperature.
Just as high ISEER translates to low energy consumption, low CSEC translates to a low environmental impact of the RAC, assessed through TEWI. However, the environmental impact of RACs also relies on the GWP of the refrigerant and its direct environmental impact, as discussed in section 2.3. Figure 6 shows the variation of TEWI for the five RAC cases across the selected cities.
The distribution of TEWI follows a similar trend to CSEC. TEWI accounts for the direct emissions due to the refrigerant and indirect emissions due to energy consumption; however, the effect of indirect emissions is dominant over the direct emissions. This effect has also been discussed for the standardised TEWI score in section 4.1.
Consequently, TEWI was found to be the lowest for Bengaluru and the highest for Nellore. Similarly, case 4 had the lowest TEWI, while case 1 had the highest TEWI. The ascending order of TEWI is given as cases 4 < 5 < 3 < 2 < 1, implying that case 1 would have had the highest environmental impact across all cities, while case 4 would have had the least.
Case 1 had the highest variation in its TEWI across the 55 cities at 23,921 CO2e, while case 4 had the least variation at 15,740 CO2e. This implies that the environmental impact of case 1 was the most sensitive to the outdoor climatic conditions, while it was the least sensitive for case 4. The maximum and minimum TEWI for the five cases across the selected cities are listed in Table 8.
|CASE||MAXIMUM TEWI (CO2e)||MINIMUM TEWI (CO2e)||TEWI VARIATION (CO2e)|
In practical terms, if an RAC (for instance, case 3) is operated in Nellore, its overall environmental impact will be nearly twice that of the same RAC operated in Bengaluru. It is important to note that the metrics of CSEC and TEWI calculated for the static setpoint cannot be compared against the standard values since they correspond to a different number of operational hours. However, this comparison was possible for the case of ISEER since it is a dimensionless metric.
The above calculations regard 24°C as the comfortable indoor temperatures across the cities with varying climatic conditions. However, it is likely for the locally acclimatised occupants to adapt to the local climate and not operate the RACs at this static indoor setpoint. Adaptive comfort models account for this flexibility and recalculate the comfortable temperature setpoints with regard to the local climate and the AC or MM operation type of the building, as described in section 2.1. This expands the comfort band and increases the scope of energy savings due to reduced RAC operation or RAC operation at elevated indoor temperatures. The expanded comfort bands for the 55 cities are shown in Figure 7.
Similar to the static setpoint calculations, the temperature bin hours were calculated for the adaptive setpoints. The total temperature bin hours using the static, adaptive (AC) and adaptive (MM) methods were 4224–8076, 2887–6874 and 1756–5494, respectively. The maximum adaptive setpoint used for AC operation mode was 26°C, while for adaptive (MM) mode it was 28°C. Changing the setpoint methodology from static to adaptive (AC) resulted in a reduction in total temperature bin hours by 9% to 33% across cities; similarly, changing the methodology from static to adaptive (MM) resulted in a reduction by 21% to 58%.
CSEC and TEWI were also calculated using the three setpoint methodologies (Table 9). Changing the setpoint methodology from static to adaptive (AC) resulted in a reduction in CSEC by 4% to 23%; similarly, changing the methodology from static to adaptive (MM) resulted in a reduction by 12% to 50%. This reduction compares well with the findings of another study focusing on the cities of Mumbai and Bengaluru (Angelopoulos et al. 2018). They established that the adaptive setpoint methodology can reduce the cooling energy consumption by 7% to 35% compared with the static setpoint methodology. The results also agree with the findings of a study in the Mediterranean climate (Bienvenido-Huertas et al. 2020). They reported a reduction of 25–45% in the total energy consumption by replacing static setpoints with adaptive setpoints.
|SETPOINT||CSEC (kWh)||REDUCTION||TEWI (CO2e)||REDUCTION|
The trends of TEWI were similar to those of CSEC—a change from static to adaptive (AC) methodology resulted in a reduction in TEWI by 3% to 22%, and a change from static to adaptive (MM) methodology resulted in a reduction by 10% to 47%.
Figure 8 shows the variation of ISEER, CSEC and TEWI of the five RAC cases across 55 Indian cities subject to the static and adaptive (AC and MM) setpoints.
Figure 8(A) compares ISEER with TEWI to highlight the inverse relationship between the two metrics. A lower RAC energy efficiency will have translated to a higher environmental impact, as demonstrated by the high TEWI values corresponding to the low ISEER values. Figure 8(B) compares CSEC with TEWI to highlight the direct relationship between the two metrics. The variation of TEWI with respect to CSEC is found to be linear since the indirect emissions dominate the total carbon emissions. TEWI is also dependent on the emission factor, which is assumed to be constant in the present study. It is also important to note that throughout the two graphs, the CSEC and TEWI corresponding to the static setpoints were found to be the highest, while they were the lowest for the adaptive MM setpoints.
In practical terms, this indicates that an RAC would have a lower environmental impact (TEWI) and energy consumption (CSEC) if the occupants utilise adaptive operation over a static setpoint-based building operation. MM building operation helps further improve RAC performance—usage of low-energy mechanical fans and techniques such as night flushing help maintain indoor thermal comfort.
The average energy efficiency of the RACs worldwide can be doubled by 2050 by adopting strict energy performance S&L measures; these measures are easy to introduce and enforce with minimal interventions. These efforts can be significantly more effective when coupled with a standardised quantification of thermal comfort and environmental impact associated with RACs.
The combination of ISEER, CSEC and TEWI can serve as a near-exhaustive quantification of RAC performance. This methodology will motivate the development and standardisation of novel RAC technologies that may utilise a zero-GWP refrigerant or operate on a non-vapour compression cycle. An equal emphasis on the aspect of comfort within the modified standardisation methods will acquaint end-users with their respective responsibilities of maintaining optimum thermal comfort while causing the least harm to the environment. Moreover, this methodology will help bolster the three-pronged approach of: (1) improving the component-level energy efficiency of RACs; (b) educating end-users about optimising their thermal setpoints for improved performance; and (3) supporting the development of RACs with low/zero-GWP refrigerants.
This study assessed room air-conditioner (RAC) performance from the aspects of energy efficiency, thermal comfort and environmental impact. This was done by calculating and comparing the Indian seasonal energy efficiency ratio (ISEER), cooling seasonal energy consumption (CSEC) and total equivalent warming impact (TEWI) for five RAC cases for 55 Indian cities using three calculation methods based on ISO 16358-1 (2013).
The first method calculated the three metrics using a static predicted mean vote (PMV model-based) indoor operative temperature setpoint of 24°C, subject to the city-specific hourly temperature bin hours. The second and third methods calculated the three metrics using the adaptive indoor operative temperature setpoints for air0conditioned (AC) and mixed-mode (MM) building operation stipulated by India model for adaptive comfort (IMAC). These calculations were subject to the city-specific hourly temperature bin hours and variable outdoor temperatures. The key conclusions from the study are as follows:
The findings of this study are subject to three limitations: the calculated metrics do not account for the variability of the building envelope characteristics and usage patterns; they do not account for the variation of relative humidity (RH); and they utilise outdoor air temperature to derive the setpoints for indoor adaptive setpoints. Future studies can address these gaps for a better understanding of the topic.
This study demonstrates that existing RAC performance standards in India can be improved to account for climatic variability and environmental impact in addition to the existing understanding of energy efficiency. The methodology proposed here can be used to calculate the RAC performance metrics vis-à-vis other refrigerants, RAC devices and thermal comfort models. Moreover, this study will provide a basis for establishing more comprehensive evaluation criteria for unconventional and diverse RAC technologies and help them ultimately to compete in the market for popular adoption.
|BEE||Bureau of Energy Efficiency|
|CDM||Clean Development Mechanism|
|CO2e||carbon dioxide equivalent|
|COP||coefficient of performance|
|CSEC||cooling seasonal energy consumption|
|CSPF||cooling seasonal performance factor|
|CSTL||cooling seasonal total load|
|ECBC||Energy Conservation Building Code|
|EER||energy efficiency ratio|
|EUI||energy use intensity (kWh/m2/year)|
|GWP||global warming potential|
|HVAC||heating, ventilation and air-conditioning|
|IMAC||Indian model for adaptive comfort|
|ISEER||Indian seasonal energy efficiency ratio|
|LCCP||life cycle climate performance|
|MEPS||minimum energy performance standards|
|NBC||National Building Code|
|S&L||standards and labelling|
|SEER||seasonal energy efficiency ratio|
|TEWI||total equivalent warming impact|
|TMY||typical meteorological year|
|TR||tons of refrigerationa|
|TSI||tropical summer index|
Note: a A ton of refrigeration is a unit of power used in India to describe the heat-extraction capacity of air-conditioning equipment. It is defined as the rate of heat transfer that results in the freezing or melting of 2000 lbs, or 907 kg, of pure ice at 0°C in 24 h. It is approximately equivalent to 12,000 BTU/h or 3.5 kW.
The authors thank the Shakti Sustainable Energy Foundation for giving the Centre for Advanced Research in Building Science and Energy (CARBSE), CEPT University, the opportunity to conduct this study with funding support.
Conceptualisation: R.R. and N.J.; methodology: R.R. and N.J.; software: N.J.; validation: R.R. and N.J.; formal analysis: R.R. and N.J.; investigation: R.R. and N.J.; resources: R.R., N.J., V.V. and S.D.; data curation: N.J.; writing—original draft preparation: N.J. and V.V.; writing—review and editing: R.R., N.J., V.V. and S.D.; visualisation: N.J. and R.R.; supervision: R.R.; project administration: R.R.; funding acquisition: R.R. and S.D.
All authors have read and agreed to the published version of the manuscript.
The authors have no competing interests to declare.
The research was funded by the Shakti Sustainable Energy Foundation under a Climate Works Grant (number G18 SSEF-391/471) for the project Advanced Building Energy Efficiency in India 2018–19 Phase II.
Supplemental data for this article can be accessed at: https://doi.org/10.5334/bc.127.s1
Abhyankar, N., Shah, N., Letschert, V., & Phadke, A. (2017). Assessing the cost-effective energy saving potential from top-10 appliances in India. Paper presented at the 9th International Conference on Energy Efficiency in Domestic Appliances and Lighting (EEDAL). https://eta.lbl.gov/sites/default/files/publications/india_appliance_ee_potential_eedal_conference_paper_0.pdf
AEEE. (2015). Evaluating market response to the appliance standards and labelling programme—A status report. Alliance for an Energy Efficient Economy (AEEE). https://aeee.in/wp-content/uploads/2020/07/2015-Evaluating-Market-Response-to-the-Appliance-Standards-and-Labelling-Programme.pdf
Angelopoulos, C., Cook, M. J., Spentzou, E., & Shukla, Y. (2018). Energy saving potential of different setpoint control algorithms in mixed-mode buildings. Paper presented at the BSO 2018: 4th Building Simulation and Optimization Conference, 11–12 September (pp. 76–83). http://www.ibpsa.org/proceedings/BSO2018/1C-1.pdf
ANSI/ASHRAE. (2017). ANSI/ASHRAE 55:2017: Thermal environmental conditions for human occupancy. American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE). https://www.ashrae.org/technical-resources/bookstore/standard-55-thermal-environmental-conditions-for-human-occupancy
Aparicio-Ruiz, P., Barbadilla-Martín, E., Salmerón-Lissén, J. M., & Guadix-Martín, J. (2018). Building automation system with adaptive comfort in mixed mode buildings. Sustainable Cities and Society, 43, 77–85. DOI: https://doi.org/10.1016/j.scs.2018.07.028
BEE. (2017). Energy Conservation Building Code 2017. Ministry of Power, Government of India, for the Bureau of Energy Efficiency (BEE). https://beeindia.gov.in/sites/default/files/BEE_ECBC_2017.pdf
Bienvenido-Huertas, D., Sánchez-García, D., Pérez-Fargallo, A., & Rubio-Bellido, C. (2020). Optimization of energy saving with adaptive setpoint temperatures by calculating the prevailing mean outdoor air temperature. Building and Environment, 170, 106612. DOI: https://doi.org/10.1016/j.buildenv.2019.106612
Bureau of Indian Standards. (1992a). IS 1391-1: Room air conditioners, Part 1: Unitary air conditioners. Bureau of Indian Standards. https://law.resource.org/pub/in/bis/S08/is.1391.1.1992.pdf
Bureau of Indian Standards. (1992b). IS 1391-2: Indian standard for room air conditioners, Part 2: Split air conditioners. Bureau of Indian Standards. https://law.resource.org/pub/in/bis/S08/is.1391.2.1992.pdf
Bureau of Indian Standards. (2016). National Building Code of India 2016.1. mddaonline.in/downloads/MDDALINKS/pdf/nbc.pdf
Carlucci, S., Bai, L., de Dear, R., & Yang, L. (2018). Review of adaptive thermal comfort models in built environmental regulatory documents. Building and Environment, 137, 73–89. DOI: https://doi.org/10.1016/j.buildenv.2018.03.053
Central Electricity Authority. (2018). CO2 baseline database for the Indian power sector: User guide (March). https://cea.nic.in/reports/others/thermal/tpece/cdm_co2/user_guide_ver14.pdf
Chaturvedi, V., Sharma, M., Chattopadhyay, S., & Purohit, P. (2015). India’s long term hydrofluorocarbon emissions. Research Gate. DOI: https://doi.org/10.13140/RG.2.1.1211.1524
Chaturvedi, V., & Shukla, P. R. (2014). Role of energy efficiency in climate change mitigation policy for India: Assessment of co-benefits and opportunities within an integrated assessment modeling framework. Climatic Change, 123, 597–609. DOI: https://doi.org/10.1007/s10584-013-0898-x
CLASP. (2017). AC Challenge Program for India (March). https://storage.googleapis.com/clasp-siteattachments/AC-Challenge-report-Final-24th-March-17.pdf
Danfoss. (n.d.). Coolselector2. https://www.danfoss.com/en-in/service-and-support/downloads/dcs/coolselector-2/#tab-overview
Doctor-Pingel, M., Vardhan, V., Manu, S., Brager, G., & Rawal, R. (2019). A study of indoor thermal parameters for naturally ventilated occupied buildings in the warm-humid climate of southern India. Building and Environment, 151, 1–14. DOI: https://doi.org/10.1016/j.buildenv.2019.01.026
Földváry Ličina, V., Cheung, T., Zhang, H., de Dear, R., Parkinson, T., Arens, E., Chun, C., Schiavon, S., Luo, M., Brager, G., Li, P., Kaam, S., Adebamowo, M. A., Andamon, M. M., Babich, F., Bouden, C., Bukovianska, H., Candido, C., Cao, B., … Zhou, X. (2018). Development of the ASHRAE Global Thermal Comfort Database II. Building and Environment, 142, 502–512. DOI: https://doi.org/10.1016/j.buildenv.2018.06.022
Goetzler, W., Young, J., Fuhrman, J., & Abdelaziz, O. (2016). The future of air conditioning for buildings (July). https://www.energy.gov/eere/buildings/downloads/future-air-conditioning-buildings-report. DOI: https://doi.org/10.2172/1326540
Graham, P., & Rawal, R. (2019). Achieving the 2°C goal: The potential of India’s building sector. Building Research & Information, 47(1), 108–122. DOI: https://doi.org/10.1080/09613218.2018.1495803
IEA. (2017). Insights brief: Space cooling. International Energy Agency (IEA). https://www.iea.org/reports/insights-brief-space-cooling
IEA. (2018). 2018 World energy outlook. International Energy Agency (IEA). https://www.iea.org/reports/world-energy-outlook-2018
IEA. (2020). Tracking buildings 2020. International Energy Agency (IEA). https://www.iea.org/reports/tracking-buildings-2020
Indraganti, M., Ooka, R., Rijal, H. B., & Brager, G. S. (2014). Adaptive model of thermal comfort for offices in hot and humid climates of India. Building and Environment, 74, 39–53. DOI: https://doi.org/10.1016/j.buildenv.2014.01.002
ISO. (2013). ISO 16358-1: Air-cooled air conditioners and air-to-air heat pumps—Testing and calculating methods for seasonal performance factors—Part 1: Cooling seasonal performance factor. International Organization for Standardization (ISO). https://www.iso.org/standard/56467.html
Jain, N. R., & Rawal, R. (2019). Three sides of coin: Connecting cooling capacity, environmental impact and comfort. In International Conference on Energy Research and Development (ASHRAE). https://s1.c-pdf.best/standards/Three-Sides-of-Coin-Connecting-Cooling-Capacity-Environmental-Impact-and-Comfort/
Kumar, S., Sachar, S., Kachhawa, S., Goenka, A., Kasamsetty, S., & George, G. (2018a). Demand analysis for cooling by sector in India in 2027. https://www.energyforum.in/fileadmin/user_upload/india/media_elements/publications/10_Cooling_Demand_AEEE.pdf
Kumar, S., Sachar, S., Kachhawa, S., Singh, M., Goenka, A., Kasamsetty, S., George, G., Rawal, R., & Shukla, Y. (2018b). Projecting national energy saving estimate from the adoption of adaptive thermal comfort standards in 2030. https://aeee.in/projects/projecting-national-energy-saving-estimate-from-the-adoption-of-adaptive-thermal-comfort-standards-in-2030/
Makhnatch, P., & Khodabandeh, R. (2014). The role of environmental metrics (GWP, TEWI, LCCP) in the selection of low GWP refrigerant. Energy Procedia, 61, 2460–2463. DOI: https://doi.org/10.1016/j.egypro.2014.12.023
Makhnatch, P., Mota-Babiloni, A., López-Belchí, A., & Khodabandeh, R. (2019). R450A and R513A as lower GWP mixtures for high ambient temperature countries: Experimental comparison with R134a. Energy, 166, 223–235. DOI: https://doi.org/10.1016/j.energy.2018.09.001
Manu, S., Shukla, Y., Rawal, R., Thomas, L. E., & de Dear, R. (2016). Field studies of thermal comfort across multiple climate zones for the subcontinent: India Model for Adaptive Comfort (IMAC). Building and Environment, 98, 55–70. DOI: https://doi.org/10.1016/j.buildenv.2015.12.019
Ministry of Power. (2017). Room air conditioners—Notification of the Government of India in the Ministry of Power (Vol. 14). www.beestarlabel.com/Content/Files/AC_Notification.pdf
Phadke, A., Abhyankar, N., & Shah, N. (2014). Avoiding 100 new power plants by increasing efficiency of room air conditioners in India: Opportunities and challenges. https://ies.lbl.gov/publications/avoiding-100-new-power-plants. DOI: https://doi.org/10.2172/1136779
Power Knot. (2011). COPs, EERs, and SEERs. https://powerknotsra.com/2011/03/01/cops-eers-and-seers/
Sehar, F., Pipattanasomporn, M., & Rahman, S. (2017). Integrated automation for optimal demand management in commercial buildings considering occupant comfort. Sustainable Cities and Society, 28, 16–29. DOI: https://doi.org/10.1016/j.scs.2016.08.016
Shah, N., Abhyankar, N., Park, W. Y., Phadke, A., Diddi, S., Ahuja, D., Mukherjee, P. K., & Walia, A. (2016). Cost–benefit of improving the efficiency of room air conditioners (inverter and fixed speed) in India (June). https://ies.lbl.gov/publications/cost-benefit-improving-efficiency. DOI: https://doi.org/10.2172/1342227
Shah, N., Park, W. Y., & Gerke, B. (2017). Assessment of commercially available energy-efficient room air conditioners including models with low global warming potential (GWP) refrigerants. https://eta.lbl.gov/sites/default/files/publications/assessment_of_racs_lbnl-_2001047.pdf. DOI: https://doi.org/10.2172/1408468
Sharma, M. R., & Ali, S. (1986). Tropical summer index—A study of thermal comfort of Indian subjects. Building and Environment, 21(1), 11–24. DOI: https://doi.org/10.1016/0360-1323(86)90004-1
Singh, M. K., Mahapatra, S., & Teller, J. (2015). Development of thermal comfort models for various climatic zones of North-East India. Sustainable Cities and Society, 14(1), 133–145. DOI: https://doi.org/10.1016/j.scs.2014.08.011
Singh, M. K., Ooka, R., Rijal, H. B., & Takasu, M. (2017). Adaptive thermal comfort in the offices of North-East India in autumn season. Building and Environment, 124, 14–30. DOI: https://doi.org/10.1016/j.buildenv.2017.07.037
The Australian Institute of Refrigeration Air Conditioning and Heating. (2012). Methods of calculating total equivalent warming impact (TEWI). https://www.airah.org.au/Content_Files/BestPracticeGuides/Best_Practice_Tewi_June2012.pdf
Thirumaran, K., & Subhashini, S. (2014). Energy efficient passive design strategies for buildings in Madurai. American Journal of Sustainable Cities and Society, 1(3), 184–202. http://rspublication.com/ajscs/jan14/jan14.htm
US Energy Information Administration. (n.d.). EIA analysis explores India’s projected energy consumption. Today in Energy. https://www.eia.gov/todayinenergy/detail.php?id=42295
Waide, P., Riviere, P., & Watson, R. (2011). Cooling benchmarking study: Part 2. Benchmarking component report. http://clasponline.org/en/ResourcesTools/Resources/StandardsLabelingResourceLibrary/2012/~/media/F%0Ailes/SLDocuments/2012/CoolingBenchmarkingStudy/RAC_benchmarking_2-%0A_Benchmarking_component.pdf
Wu, J., Xu, Z., & Jiang, F. (2019). Analysis and development trends of Chinese energy efficiency standards for room air conditioners. Energy Policy, 125, 368–383. DOI: https://doi.org/10.1016/j.enpol.2018.10.038
Zhao, L., Zeng, W., & Yuan, Z. (2015). Reduction of potential greenhouse gas emissions of room air-conditioner refrigerants: A life cycle carbon footprint analysis. Journal of Cleaner Production, 100, 262–268. DOI: https://doi.org/10.1016/j.jclepro.2015.03.063