Title: Assessing Energy Performance Improvement Potential of Traditional Housing in North-Eastern India through Building Energy Simulation

doi:10.21203/rs.3.rs-2595026/v1

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Research Article

Title: Assessing Energy Performance Improvement Potential of Traditional Housing in North-Eastern India through Building Energy Simulation

https://doi.org/10.21203/rs.3.rs-2595026/v1

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The building sector is a principal consumer of energy globally, and in India, the residential buildings have second highest energy demands after the industrial sector. Improving energy performance of residential houses will considerably curtail the associated carbon emissions. Compared to energy intensive urban buildings, research on energy modeling receives scant attention for small and moderate size non-engineered, traditional and naturally ventilated residential buildings constructed with low embodied energy local materials using semi-skilled construction techniques. As majority of the residential buildings in the towns and rural areas in the North-Eastern states of India are primarily constructed with such materials, the existing literature review calls for a quantitative assessment of their energy consumption patterns along with contributions of various building envelope parameters. The article aims to assess the improvement potential of the Energy Performance Index (EPI) of traditional houses in three locations viz. Agartala, Jorhat and Shillong and examine the roles of various building parameters and material options in influencing the EPI through whole building energy simulation technique followed by sensitivity analysis. The resulting optimum combinations have shown the EPI improvement (reduction) potential of 19.27 %, 21.40 %, 27.42 % over the worst cases, respectively for Agartala, Jorhat and Shillong buildings. The sensitivity analysis identified building geometry parameters such as Volume, Wall Area, Surface Area, Carpet Area and Building Height to critically influence the energy performance.

Energy Performance Optimization

Energy Simulation

Traditional Housing

North-Eastern India

Sensitivity Analysis

According to the UN Global Status Report, building operations consume a huge proportion of electricity which is about 55% of the total electricity requirement globally. The building sector accounts for contributing 38% of the total global energy related CO₂ emissions. The trend of energy requirement and energy related CO₂ emissions are escalating world-wide (UNEP, 2020). The energy consumption of the building sector in India is also linearly increasing with the rise in construction rate, and is about 8% annually (Dhaka et al. 2012). Globally, residential buildings consume 10% of world’s delivered energy and the rate is increasing at 1.5% per year (Chandel et al. 2016). The residential buildings have second highest energy demands in India after the industrial sector (Dhaka et al. 2012). The growth rate of the construction sector in India is twice that of the world average growth rate. It is growing at a rate of 10% in contrast to 5.2%, the world average growth rate. It is anticipated that, by 2050 this would add 40 million m² of new built-up area (Shukla et al. 2015). It is evident that, improving energy performance of residential houses will considerably create an opportunity to curtail the energy consumption and also to restrict the associated carbon emissions. India has promised to reduce the emission intensity of its GDP by 35% by 2030 from 2005 level, under Paris agreement on climate change (Kumar et al. 2021). It has been estimated that the overall energy conservation potential in India is about 23% (CPWD, 2019).

With the growth of large cities and construction boom in developing countries, energy modeling has been emerging as a major research area. However, the focus is limited primarily to energy intensive engineered structures in cities. Energy modeling receives scant attention for small and moderate size non-engineered, traditional and naturally ventilated residential buildings constructed with low embodied energy local materials like bamboo, cane, mud, wooden plank using semi-skilled construction techniques. As majority of the buildings (especially Assam-type housing) in the towns and rural areas in the North-Eastern states of India are primarily constructed with this type of materials and construction techniques, they contribute significantly in the cumulative energy consumption of this ecologically fragile and access critical region (Kaushik and Babu, 2009). The present article seeks to bridge this research gap. It aims to optimize the Energy Performance Index (EPI) of traditional houses in three locations of North-Eastern India viz. Agartala, Jothat and Shillong and examine the roles of various building parameters in influencing the EPI through whole building energy simulation technique.

1.1 Non-engineered Traditional Housing of North-Eastern India

The North Eastern part of India comprises eight states, which are Assam, Arunachal Pradesh, Tripura, Nagaland, Manipur, Mizoram, Meghalaya and Sikkim. These states have a relatively lower population than rest of India. The population is over 45 million (Dikshit and Dikshit, 2014). However, the population is expanding with time and so is the energy requirement. The per-capita energy consumption of north-eastern states has increased by 2.65% from 2019 to 2020. As per the Central Electricity Authority, currently there exists a gap of 4.7 per cent between the requirement and availability of electricity in the North Eastern region (CEA , 2021).

The traditional housing in North-Eastern India has been influenced by local climate, landform, socio-cultural setup, materials and technology availability. The entire region has very uneven topography and the climate of any place is influenced by the topography of the particular place. Major landforms affecting the climate of the sites are mountains, valleys and plains. The whole region is also heavily vegetated. All these have varying effects on the microclimate of a place and its traditional housing typologies. Predominant form of traditional housing is ‘Assam Type’. This typology is quite simple, satisfies social setup, cultural needs and meets the climatic requirements (Singh et al. 2009).

1.2 Literature Review and Study Framework

Research on energy modeling and other modern predictive methods for engineered buildings (steel, glass and aluminum clad structures accommodating multi-storeyed office complexes, shopping malls, luxury apartments) is picking up globally (Kim and Suh, 2021; Cortiços and Duarte, 2022; Mujeebu and Bano, 2022; Olu-Azayi et al., 2022) and for large cities in India (Biswas et al., 2013; Tulsyan et al. 2013; Chedwal et al. 2015; Chandel et al. 2016; Bano and Sehgal, 2018; Bhatnagar et al., 2019; Gokarakonda et al., 2019; Kumar et al., 2021)and other developing countries for regulating their energy consumption and to access various incentives for adopting energy efficient techniques (CPWD, Ministry of Housing and Urban Affairs, Government of India).

Studies have been undertaken on developing bio-climatic zones (Singh et al. 2007) vernacular architecture and climate responsiveness (Singh et al. 2009), climate responsive building design in North East India (Singh et al. 2010), solar passive features in traditional housing (Singh et al. 2011^a) and adaptive thermal comfort models (Singh et al. 2011^b). The studies revealed a close relationship between traditional housing with climate, socio-economic attributes, cultural setup, use of local materials and sustainability. Large number of solar passive features e.g., building form, orientation, envelope design, shading, use of natural ventilation, internal space arrangements contribute to the thermal comfort of traditional housing. The houses are constructed with materials with low embodied energy and from the same climate zones. They fit into the local environment perfectly and represent examples to achieve sustainability.

Existing literature calls for a systematic and quantitative assessment of energy consumption patterns of non-engineered and traditional housing in the North East and contributions of various building envelope parameters to the EPI. The present study aims for the same following a framework as represented in Figure-1.

2.1 Locations of the Study

The study has been carried out in three different locations of North East India viz. Agartala in the state of Tripura (23.83°N, 91.28°E), Jorhat in the state of Assam (26.75°N, 94.20°E) and Shillong in the state of Meghalaya (25.57°N, 91.88°E).

Agartala is the capital of the Indian state of Tripura and is the second largest city and municipal body in north-eastern India after Guwahati. Agartala is situated on a plain along the Haora River, and extends to the low-lying hills on its northern parts. Jorhat is an administrative district of Assam situated in the central part of the Brahmaputra Valley. On the north of the district, the river Brahmaputra forms the largest riverine island of the world. Shillong lies on the Shillong Plateau, a uplifted structure in the northern Indian shield. The city lies in the centre of the plateau and is surrounded by hills. Figure-2 shows the positions of these three study locations in the climate zone map of India.

2.1.1 Climate of the Study Locations

All of the three cities considered in the present study viz. Agartala, Jorhat and Shillong fall under the Humid Subtropical climate zone as per Köppen-Geiger climate classification system(Kottek et al., 2006). These locations receive high annual average rainfall of 1991 mm, 3636 mm and 2699 mm respectively (Climate-Data.ORG) compared to India’s average yearly rainfall of 1879 mm. The average outdoor temperature of Agartala and Jorhat are 25.72^oC and 23.12^oC respectively. Shillong experiences a comparatively colder climate having an average outdoor temperature 14.29^oC as per Energy Plus weather file for Kolkata (IND_Shillong.425160_ISHRAE.epw). Energy Conservation Building Code (ECBC, 2018) characterizes the climate Agartala and Jorhat as ‘Warm and Humid’ and that of Shillong as ‘Cold and Cloudy’.

2.2 Housing Systems and Choice of the Buildings in the Study Area

Ten existing non engineered typical residential buildings from each of the study locations are chosen for the present study. Description of the location specific housing systems are presented in this section.

2.2.1 Buildings Studied in Agartala

The buildings of Agartala are relatively small consisting of single or double rooms. The rural and semi-rural houses are mainly constructed with mud and brick walls, and the Galvanized Iron (GI) sheets are used for roofs. Most of the houses have a single room with a wide semi-open verandah (average width 1.92 ± 0.37 m covering 42 ± 9% of covered area) to accommodate household activities and also serves as a room during day time. The verandah also provide shade to the walls against solar radiation. The houses also include a separate kitchen and a toilet. To facilitate better ventilation every house in Agartala has provisions of skylight or ventilators. In general, the area used for ventilation is around 23.57%. According to the requirements of warm humid climate the buildings are rectangular in shape. The summarized information for the Agartala buildings is presented in Table-1.

2.2.2 Buildings Studied in Jorhat

The buildings of Jorhat are medium in size with double or triple rooms. These are popularly called as Assam type housing and are constructed mostly with Ikra (made of bamboo frame, Ikra reed and mud/lime/cement plaster) wall and GI sheet roofs. The houses often have a facility of separate kitchen and toilet along with a wide semi open verandah (average width 2.17 ± 1.46 m covering 29 ± 19% of covered area). The semi-open verandah also acts as a room during day time and it also accommodates household activities. This space also provides shade to the walls against the solar radiation. In existing Jorhat buildings due to indiscriminate additions of rooms the existing WFR is quite low To facilitate better ventilation most of the houses in Jorhat have provisions of skylight or ventilators. The area allotted for skylight or ventilators is about 22.20%. According to the requirements of a warm humid climate the buildings are rectangular or ‘L’ shaped. The summarized information for the Agartala buildings is presented in Table-2.

2.2.3 Building Studied in Shillong

The buildings of Shillong are medium in size with the provision for double and triple rooms. Most of the urban and semi-urban houses (known as Assam type housing) are constructed with Ikra (made of bamboo frame, Ikra reed and mud/lime/cement plaster) wall and the roofing is done with GI sheets. Most of the houses have two or three rooms and some of the houses also include a small verandah (average width 0.44 ± 0.76 m covering 29 ± 19% of covered area) and often a separate kitchen and washroom. In existing buildings of Shillong as the rooms are added in a haphazard manner, the prevailing WFR is low. To facilitate better ventilation most of the houses in Shillong have provisions of skylight or ventilators. The skylights are kept open in Summer and closed in Winter. Average skylight or ventilator area is 25.67%. According to the requirements of cold climate the buildings are predominantly square in shape. The summarized information for the Agartala buildings is presented in Table-3.

2.3 Framing the Energy Simulation Models following the Optimization Scheme

Ten existing non engineered typical residential buildings from each of the study locations, were taken as the prototypes for this study as described. The entire study was performed following a four-stage continuous energy performance assessment scheme, where different building properties viz. wall assemblies using different wall materials, roof assemblies using different roof materials, building orientations and the Window Wall Ratio (WWR) were varied at each stage. To assess the energy performance and establish its interconnection with the stated building properties, a whole-building simulation approach was considered and the same was executed with eQUEST 3.65.

For each of the ten buildings for each location forty-two simulation cases (eight wall assembly variations, nine roof assembly variations, sixteen orientation variations and nine WWR variations) were framed using the ‘Design Development Wizard’ of the software. The actual building geometry, orientation and construction parameters of the studied existing buildings were drawn, assessed and collected through field surveys for three different seasons (summer, winter and monsoon) carried out between July, 2018-June, 2019. The stage-wise variation of the stated parameters and utilizing them in framing the energy simulation models are described subsequently. Views of the energy simulation models are shown in Figure-3.

2.3.1 Stage 1: Finding Most Energy Efficient Wall Assembly through Wall Assembly Variations

In the Stage-1 of the energy performance assessment for the considered building variations in the external wall assembly constructions are studied. A total of eight simulation models (Case-1 to Case-8) were framed with eight different wall assemblies typically practiced in the study locations for the free running residential buildings as described in Table-4. The thermal transmittance values (U_wall) for the considered wall constructions are obtained from Energy Conservation Building Codes (ECBC, 2017, ECBC Residential, 2017 and Eco-Niwas Samhita, 2018) and other available literatures.

The roof assembly construction was kept same as Reinforced Cement Concrete Roof (Roof1_RCC i.e. 100 mm RCC + 100 mm Lime Concrete) throughout all the eight simulation cases of Stage-1. For all the buildings, orientations and WWR were kept unaltered at their respective actual values throughout Stage-1. The wall assembly yielding best energy performance (least EPI) is found out from the simulation results for each of the study locations and the same is used unaltered during energy simulations at subsequent stages.

2.3.2 Stage 2: Finding Most Energy Efficient Combination of Wall and Roof Assembly through Roof Assembly Variations

In the Stage-2 of the energy performance assessment variations in the roof assembly constructions are studied keeping the wall assembly same as the most energy efficient choice obtained through Stage-1. A total of nine simulation models (Case-9 to Case-17) were framed with nine different wall assemblies typically practiced in the study locations for the free running residential buildings as described in Table-5. The thermal transmittance values (U_roof) for the considered roof constructions are obtained from Energy Conservation Building Codes (ECBC, 2017, ECBC Residential, 2017 and Eco-Niwas Samhita, 2018) and other available literatures.

For all the buildings, orientations and WWR were kept unaltered at their respective actual values throughout Stage-1 and Stage-2. The wall and roof assembly combination yielding best energy performance (least EPI) is found out from the simulation results for each of the study locations and the same is used unaltered during energy simulations at subsequent stages.

2.3.3 Stage 3: Finding Most Energy Efficient Combination of Wall and Roof Assembly and Building Orientation through Building Orientation Variations

In the Stage-3 of the energy performance assessment variations in the building orientations are studied keeping the roof and wall assembly combination same as the most energy efficient choice obtained through Stage-2. A total of sixteen simulation models (Case-18 to Case-33) were framed by rotating the buildings clockwise 22.5^o at each step starting from Azimuth of the longer axis for each building at 0^o (i.e. aligning the longer axis along North-South line). For all the buildings, WWR were kept unaltered at their respective actual values throughout Stage-1, Stage-2 and Stage-3. The orientation along with wall and roof assembly combination yielding best energy performance (least EPI) is found out from the simulation results for each of the study locations and the same is used unaltered during energy simulations at remaining Stage-4.

2.3.4 Stage 4: Finding Most Energy Efficient Combination of Wall and Roof Assembly, Building Orientation and Window Wall Ratio ( WWR ) through WWR Variations

In the final Stage-4 of the energy performance assessment variations in the WWR are studied keeping the building orientation along with roof and wall assembly combination same as the most energy efficient choice obtained through Stage-3. A total of nine simulation models (Case-34 to Case-42) were framed. For Case-34 the WWR was kept at the actual value. For the remaining cases it was increased by 5% at each step starting from 5% initially (Case-35) and ended at 40% (Case-42). The best combination for WWR, orientation along with wall and roof assembly combination yielding best energy performance (least EPI) is thus found out from the simulation results for each of the study locations.

2.4 Glazing, Heating, Ventilation and Air Conditioning (HVAC), Lighting and Equipment, Occupancy and Schedule

Apart from the four varying parameters as describe in Section-2.3, all the other parameters required in framing the energy simulation models were kept unaltered throughout the forty-two simulation cases for each of the ten studied buildings for three study locations. These are described subsequently. The information is also summarized in Table-6.

2.4.1 Window Glazing

Single glazed units (SGU) with clear float glasses of 6 mm thickness were considered for windows for all the buildings at all locations. Glass properties were obtained from product brochures of leading glass manufacturers viz. Saint Gobain Glass (SGG), Pilkington and Asahi India Glass Limited (AIS) and the values are averaged. A Transmittance (U_glass) value of 5.69 W/m²°K, Visual Light Transmittance (VLT) value of 87.87% and Solar Heat Gain Coefficient (SHGC) value of 0.81 were considered for the glasses.

2.4.2 Heating, Ventilation and Air Conditioning (HVAC)

All the buildings considered in the present study are naturally ventilated free running buildings and no separate HVAC systems are there to actively control the thermal comfort. However, to assess the heating-cooling loads and associated energy efficiency of these buildings, unitary split air conditioners with BEE 3 Star efficiency are considered throughout all the simulation cases for all the buildings at each location. For all the simulation runs the cooling systems are sized by the ‘Auto-Size’ capability of eQUEST 3.65.

To incorporate the effect of adaptive thermal comfort, thermostat set points for heating and cooling were varied for three locations based on the respective average outdoor temperatures (T_{outdoor_avg}) as obtained from the corresponding energy plus weather files. The indoor operative temperatures (T_{indoor_opr}) for each location were calculated using Eq. (1). The corresponding heating and cooling set points were determined as T_{indoor_opr} ± 2.38^oC. The methodology followed is described in detail in Note 5 − 1: Adaptive Thermal Comfort (ECBC, 2017) for naturally ventilated buildings.

$${T}_{indoor\_opr}=0.54\times {T}_{outdoor\_avg}\pm 12.83 Equation \left(1\right)$$

2.4.3 Lighting and Equipment

Detailed field surveys were conducted in each of the study locations and account of lights and equipment was taken. Average interior lighting power density (LPD) of 0.46 W/ft² and average equipment power density (EPD) of 0.30 W/ft² for all the buildings are chosen as per the survey results. No consideration is made for exterior lighting either in any of the simulation cases. To estimate the impact of daylight use on energy performance, a daylight control measure was activated during energy simulation for all cases.

2.4.4 Occupancy and Schedule

All the buildings are considered as 24 hours open residential ones and the occupancy, internal lighting and equipment schedules are chosen accordingly. The occupancy was chosen as 12.50 m²/person as per National Building Code (2016) of India.

2.5 The Energy Simulation

The energy performance of the studied buildings for all the different design cases as discussed in Section-2.3, are determined through whole building energy simulation following the ‘Whole Building Performance Method’ described in ECBC, 2017 using eQUEST 3.65 as the simulation tool. Energy Plus weather files for Jorhat (IND_Jorhat.424230_ISHRAE.epw) and Shillong (IND_Shillong.425160_ISHRAE.epw) are collected and used for the buildings in respective locations. As no Energy Plus weather file for Agartala is available, same for Dhaka (BGD_Dhaka.419230_SWERA.epw), which is nearest city to Agartala with same climatic conditions, is used for Agartala buildings. All the Energy Plus weather files were converted to eQUEST usable format by eQUEST weather processor prior to be used for energy simulation.

Annual energy consumption (for 8760 hours) under different heads were estimated for all the design cases simulating the framed models. The annual energy consumption (kWh) for each building for all the design cases are converted to Energy Performance Index (EPI) using Eq. (2). The energy performances of different design cases are evaluated and analyzed in terms of these EPI values.

$$EPI=\frac{Annual\_Energy\_Consumption}{{A}_{built-up}} Equation \left(2\right)$$

2.6 Validation of the Energy Simulation Models

The buildings considered in the present study at Agartala, are for the poorest of the poor and very small mud houses constructed under the Indira Awas Yojana (IAY) scheme of Government of India by an NGO. The NGO bears the expenditure of electricity for the houses. Jorhat buildings under study are Assam Police quarters for their employees. The police authority bears the expenditure. In Shillong the studied buildings belong to Refugee Colony Association. The association bears the electrical expenses and receives a fixed amount from individual house owners. In either case, electricity bills specific to individual houses are not there to be used for validation of the simulation outputs in terms of energy expenditure.

In order to validate the simulation models, comparison of measured (T_measured) and simulated temperature (T_simulated) is done. The indoor temperatures for all the houses at each of the study locations were monitored using automatic ambient temperature loggers (Make: HTC, Model: Easy Log) with measurement resolution of 0.1^oC for three different seasons (Summer, Winter and Monsoon). Agartala buildings were monitored between 13th July-22nd July, 2018 (Summer), 9th January-18th January, 2019 (Winter) and 21st May-31st May, 2019 (Monsoon). Jorhat buildings were monitored between 12th July-24th July, 2018 (Summer), 26th December, 2018-4th January, 2019 (Winter) and 17th June-26th June, 2019 (Monsoon). Shillong buildings were monitored between 1st September-10th September, 2018 (Summer), 15th December-23rd December, 2018, 2019 (Winter) and 31st May-9th June, 2019 (Monsoon).

The temperature loggers were placed in the bedrooms of the buildings and ambient temperatures (T_measured) were recorded during daytime (10:00 AM – 5:00 PM) of the monitoring days. The ‘hourly reports’ for the ‘zone temperatures’ from eQUEST output files provided simulated temperatures (T_simulated) for the same spaces for same date and time. Results for all the buildings ate a particular location for a particular season are combined and ‘Scatter Plots’ between T_measured and T_simulated are drawn and presented as shown in Figure-4. The coefficient of determination (R²) for each plot is noted which clearly show good association between measured and simulated temperature indicating high simulation efficiency.

3.1 Finding Most Energy Efficient Wall Assembly through Wall Assembly Variations

In Stage-1 of the simulation effect of the variation in external wall assemblies on energy performance is studied for all three of the study locations and the average results for ten buildings in each case are presented in Table-7. Amidst all the wall variations Ikra Wall (Wall5_IW) is found to be the best choice and 125mm Brick Wall (Wall7_BW2) to be the worst choice in terms of EPI for all the locations.

To assess the comparative energy performance of the wall assembly options, the observed percent reductions in the corresponding EPI over the worst case are plotted and presented in Figure-5. It is estimated that 12.08%, 15.51% and 16.08% of operational energy can be saved by replacing 125 mm Brick Walls (Wall7_BW2) with Ikra Walls (Wall5_IW) for Agartala, Jorhat and Shillong respectively. The reduction of energy requirement can be associated with the lower heat retention capacity of Ikra walls. The subsequent energy efficient wall assembly options are wall options are 250 mm Rat Trap Bond Wall (Wall4_RTB) and 400 mm Mud Wall (Wall2_MW).

3.2 Finding Most Energy Efficient Combination of Wall and Roof Assembly through Roof Assembly Variations

In Stage-2 of the simulation, the most energy efficient wall assembly of Ikra Wall (Wall5_IW) was kept constant and the effect of the variation in roof assemblies on energy performance is studied for all three of the study locations and the average results for ten buildings in each case are presented in Table-8. Out of nine such variations, combination of Ikra Wall (Wall5_IW) and GI Roof with Bamboo False Ceiling (Roof3_GIBFC) is found to be the best choice and simple GI Roof (Roof2_GI) to be the worst choice in terms of EPI for all the locations.

To assess the comparative energy performance of the roof assembly options, the observed percent reductions in the corresponding EPI over the worst case are plotted and presented in Figure-6. An average EPI reduction of 5.62%, 6.78% and 13.87% is observed by replacing simple GI Roof (Roof2_GI) with GI Roof with Bamboo False Ceiling (Roof3_GIBFC) for Agartala, Jorhat and Shillong respectively. It can be deduced that the false ceiling was responsible for absorbing the heat thus conserving energy. RCC filler slabs (Roof9_RCCFS) and GI sheets with 40 mm tiles (Roof5_GIT) are observed to be the next two better roof choices in line.

Figure-6 also represents the progressive improvement of energy performance achieved by implementing the combination of Ikra Wall (Wall5_IW) and GI Roof with Bamboo False Ceiling (Roof3_GIBFC) over the best case in Stage-1 (i.e., Wall5_IW and Roof1_RCC). A comparative EPI reduction of 1.60%, 1.73% and 4.62% could be seen for Agartala, Jorhat and Shillong respectively.

3.3 Finding Most Energy Efficient Combination of Wall and Roof Assembly and Building Orientation through Building Orientation Variations

The most energy efficient wall roof combination i.e., Ikra Wall (Wall5_IW) and GI Roof with Bamboo False Ceiling (Roof3_GIBFC)) is maintained in Stage-3 where the variation in building orientation is explored. The longer axis of all the buildings were rotated as described in Section-3.3.3 and the effect on EPI is tabulated as in Table-9. The effect is prominent for the rectangular buildings in Agartala and Jorhat and East-West orientation of the longer axis has shown the least EPI as expected. Highest EPI is observed when the longer axis was in the North North West to North North East (337.5^o-22.5^o azimuth) region. No conclusive effect of orientation could be seen for the square shaped buildings of Shillong as the pattern changed from one building to other. On an average position of the longer axis in the North direction (0^o azimuth) has shown the best result and that in the South East direction (135^o azimuth) has shown the worst.

Figure-7 compares the best caseEPI with the worst case of Stage-3 simulations, and shows a percentage performance improvement of 2.92%, 3.51% and 5.93% respectively for Agartala, Jorhat and Shillong. When compared with the best case of Stage-2 simulations changing building orientation from the actual one shows a progressive improvement of 4.50%, 4.67% and 8.07% in EPI for the respective locations.

3.4 Finding Most Energy Efficient Combination of Wall and Roof Assembly, Building Orientation and Window Wall Ratio ( WWR ) through WWR Variations

Finally, keeping the best combination achieved in the stages from Stage-1 to Stage-3, the final simulations in Stage-4 were done for eight WWR variations from 5–40% with 5% increment at each step. The results are shown in Table-10. For Agartala, the minimum WWR of 5% has shown the best result in terms of EPI. For Jorhat and Shillong, the minimum EPI was observed for 15% WWR. Beyond those WWR values, EPI for all the locations increased consistently with higher WWR with 40% WWR yielding highest energy consumption.

Comparing with the worst case WWR (i.e. 40%), the EPI reduction for the best WWR values for Agartala, Jorhat and Shillong are observed to be 24.68%, 18.30% and 9.77% respectively. The respective progressive improvements over the best case of Stage-3 are 2.28%, 0.69% and 1.36% with optimum WWR selection. The comparisons are shown in Figure-8.

3.4.1 Impact of Optimum Window Wall Ratio ( WWR ) on Window Floor Area Ratio ( WFR ) Values for the Study Locations

The extent of window opening could also be represented as Window Floor Area Ratio (WFR) which is the ratio of the total window area to the floor area of the building. The existing WFR values are found to be 12.43 ± 5.90% for Agartala, 13.94 ± 6.13% for Jorhat and 24.13 ± 11.32% for Shillong. The variations in existing WFR values are high, particularly for Shillong. ECBC, 2018 specifies WFR values of 16.66% for Warm-Humid climate (Agartala and Jorhat) and 8.33% for Cold climate (Shillong). The existing WFR of Shillong buildings are much higher than prescribed.

The optimum WWR observed for the three study locations (5% for Agartala and 15% for Jorhat and Shillong) are converted to the respective WFR values. The average WFR corresponding to optimum WWR values are found to be 14.44 ± 4.30% for Agartala, 35.39 ± 7.95% for Jorhat and 28.58 ± 10.30% for Shillong. It can be seen that, except Agartala, WFR corresponding to optimum WWR as obtained from the study results are also much higher to the code prescribed values. Earlier studies also indicated higher WWR and WFR values for North-Eastern India (Singh et al., 2011^a,b)

Since these buildings are naturally ventilated, and there are cost implications for increasing the window proportion for small residential buildings like the studied ones, existing WFR seems to be more or less appropriate for Agartala and Jorhat buildings. However, reduction in WFR for Shillong buildings seems to be necessary for Shillong buildings to achieve energy efficiency.

3.5 Association of Energy Performance of the Buildings with Different Building Parameters

3.5.1 Correlation Analysis

To assess the statistical association amongst different variables in a multivariate scenario, a multiple correlation analysis is usually performed. The EPI is correlated with different variable building parameters as detailed in Table-1 along with the variables considered for optimization study as discussed previously, using ‘SPSS Statistics’ software (version 25). Pearson Correlation Coefficients (r) are determined and those having significance at 0.01 level and 0.05 are highlighted as presented in Table-11. Strong (± 0.5 < r < ± 1.0) to moderate (± 0.3 < r < ± 0.5) correlations are identified.

For Agartala buildings, EPI is found to be have moderate positive correlation with H_ceiling and moderate negative correlation with A_built−up, A_wall, A_surface and SA_V_ratio. Strong to moderate positive correlations with EPI are observed for A_carpet, A_verandah and U_wallfor Jorhat buildings. Moderate negative correlations are there with SA_V_ratio, R_wall and R_roof. EPI for Shillong buildings seems to have strong to moderate positive correlation with U_wall, U_roof and AZ of the longer building axis and strong negative correlation with the R_wall and R_roof.

3.5.2 Factor Analysis

A factor analysis was also carried out to extract the underlying factors from the variables using ‘SPSS Statistics’ software (version 25). The extraction method chosen is Principal Component Analysis using Varimax Rotation with Kaiser Normalization. The result is shown in Table-12. Variables with higher factor loadings (> 0.40) are highlighted.

The rotation converged after seven iterations and a total of six factors (Eigen Value > 1) are extracted explaining 85.13% of total variance. The top four factors clubbed variables representing building geometry (Factor 1), wall thermal properties (Factor 2), window openings (Factor 3) and roof thermal properties (Factor 4).

3.5.3 Sensitivity Analysis

Different researchers across the globe have widely used uncertainty and sensitivity analysis in identifying critical influencing parameters affecting building energy performance (Hemsath and Bandhosseini 2015; Kristensen and Petersen 2016; Yang et al. 2016; O’Neil and Niu 2017; Liu et al. 2017; Tian et al. 2018; Li et al. 2018; Rivalin et al. 2018; Gagnon et al. 2018; Vivian et al. 2020) and a few studies have been carried out in Indian context as well (Singh et.al., 2016; Gokarakonda et.al, 2019; Kumar et. Al., 2021).

The iterative ‘What If’ kind of sensitivity analysis is adopted here to comparatively examine the influence of different variable building parameters as detailed in Table-1 including the variables considered for optimization study as discussed previously, on the energy performance (EPI) of the buildings for each of the three study locations using the ‘TopRank’ software (version 5.5) of Palisade Decision Tools Suite (2009). The resulting influence of the stated variables on the resultant EPI solutions are presented as ‘Tornado Diagrams’ and ‘Spider Diagrams’ for each of the three locations as presented in Figure-9.

The percent change in EPI for Agartala buildings are found to be most sensitive to building geometry parameters such as Volume (V), Surface Area (A_surface), Carpet Area (A_carpet), Wall Area (A_wall), Verandah Area (A_verandah), Building Height (H_building) and Surface Area to Volume Ration (SA_V_ratio). For Jorhat buildings, the critical influencing variable to EPI remained more or less same though in a slightly different order viz. Surface Area (A_surface), Carpet Area (A_carpet), Volume (V), Wall Area (A_wall), Built-Up Area (A_{built_up}) and Verandah Area (A_verandah). Building Height (H_building) and Surface Area to Volume Ration (SA_V_ratio) are found to show less significant influence in this case. For Shillong buildings Wall Area (A_wall) and Surface Area (A_surface) are the most critical influencing parameters followed by Volume (V), Carpet Area (A_carpet), Ceiling Height (H_ceiling) and Built-Up Area (A_{built_up}). In all the cases variables clustered under the Factor 1 representing building geometry during factor analysis are found to be most influencing.

3.5.4 Different Other Building Parameters for Buildings Showing Lowest EPI for Three Study Locations

The most optimum energy efficient combination of Wall and Roof Assembly, Building Orientation and Window Wall Ratio (WWR) has already been found out for three study locations through progressive optimization and the results are presented in Section-3.1 through Section-3.4.

For each of the study locations, three buildings with lowest EPI for this combination is identified. Other building parameters for these nine buildings (three for each location) are extracted and tabulated in Table-13 to get an insight about the likely choice for the same. The best performing buildings have shown an improvement (reduction) in EPI by 20.88%, 11.64% and 11.54% for Agartala, Jorhat and Shillong respectively.

The study documents in detail the various building envelope and material parameters for the non-engineered, traditional and naturally ventilated buildings in three locations of northeastern India. The progressive whole building energy simulation approach could identify the most suitable and subsequent combinatorial options of wall and roof materials, orientations, window to wall area and window to floor area ratios. Combination of Ikra Wall + GI Roof with Bamboo False Ceiling + East-West Orientation of longer building axis + 5% WWR is found to be the most energy efficient choice for Agartala buildings. The optimum combination for Jorhat buildings is Ikra Wall + GI Roof with Bamboo False Ceiling + East-West Orientation of longer building axis + 15% WWR. Shillong buildings have shown best performance for the combination of Ikra Wall + GI Roof with Bamboo False Ceiling + North-South Orientation of longer building axis + 15% WWR. Through these optimum combinations the EPI could be improved (reduced) over the worst cases by 19.27%, 21.40%, 27.42% respectively for Agartala, Jorhat and Shillong buildings. The study also examines the nature of correlations of energy performance with parameters like wall area, surface area, built-up area, semi-open spaces (verandah area), surface to volume ratios and thermal properties (U and R values of locally available wall and roof materials) of traditional buildings. The sensitivity analysis identified building geometry parameters such as Volume, wall area, surface area, carpet area and building height to critically influence the energy performance. The results will be useful to supplement the existing residential energy conservation building code (Eco-niwas Samhita 2018) in the country with specific guidelines on building envelope parameters for small to moderate size, traditional and naturally ventilated buildings in northeastern region that is quite different in terms of the building practices from rest of the country. The methodology of energy simulations adopted in the study will contribute in bridging the gap in quantitative assessment of energy consumption, its optimization and the role of various building parameters in non-engineered and traditional housing segment in developing countries.

Funding Acknowledgement:

The authors acknowledge the funding support provided by Department of Science and Technology (DST), Government of India (GOI) for the present study conducted in a research project under the National Initiative to Promote Habitat Energy Efficiency (I - PHEE).

Competing Interest Statement:

I as the corresponding author, on behalf of all the authors, declare that the authors have no competing interests as defined by Springer, or other interests that might be perceived to influence the results and/or discussion reported in this paper.

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Tables 1 to 13 are available in the Supplementary Files section.

No competing interests reported.

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Title: Assessing Energy Performance Improvement Potential of Traditional Housing in North-Eastern India through Building Energy Simulation

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Abstract

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1. Introduction

2. Methodology

2.1 Locations of the Study

2.1.1 Climate of the Study Locations

2.2 Housing Systems and Choice of the Buildings in the Study Area

2.2.1 Buildings Studied in Agartala

2.2.2 Buildings Studied in Jorhat

2.2.3 Building Studied in Shillong

2.3 Framing the Energy Simulation Models following the Optimization Scheme

2.3.1 Stage 1: Finding Most Energy Efficient Wall Assembly through Wall Assembly Variations

2.3.2 Stage 2: Finding Most Energy Efficient Combination of Wall and Roof Assembly through Roof Assembly Variations

2.3.3 Stage 3: Finding Most Energy Efficient Combination of Wall and Roof Assembly and Building Orientation through Building Orientation Variations

2.3.4 Stage 4: Finding Most Energy Efficient Combination of Wall and Roof Assembly, Building Orientation and Window Wall Ratio ( WWR ) through WWR Variations

2.4 Glazing, Heating, Ventilation and Air Conditioning (HVAC), Lighting and Equipment, Occupancy and Schedule

2.4.1 Window Glazing

2.4.2 Heating, Ventilation and Air Conditioning (HVAC)

2.4.3 Lighting and Equipment

2.4.4 Occupancy and Schedule

2.5 The Energy Simulation

2.6 Validation of the Energy Simulation Models

3. Results And Discussions

3.1 Finding Most Energy Efficient Wall Assembly through Wall Assembly Variations

3.2 Finding Most Energy Efficient Combination of Wall and Roof Assembly through Roof Assembly Variations

3.3 Finding Most Energy Efficient Combination of Wall and Roof Assembly and Building Orientation through Building Orientation Variations

3.4 Finding Most Energy Efficient Combination of Wall and Roof Assembly, Building Orientation and Window Wall Ratio ( WWR ) through WWR Variations

3.4.1 Impact of Optimum Window Wall Ratio ( WWR ) on Window Floor Area Ratio ( WFR ) Values for the Study Locations

3.5 Association of Energy Performance of the Buildings with Different Building Parameters

3.5.1 Correlation Analysis

3.5.2 Factor Analysis

3.5.3 Sensitivity Analysis

3.5.4 Different Other Building Parameters for Buildings Showing Lowest EPI for Three Study Locations

4. Conclusions

Declarations

References

Tables

Additional Declarations

Supplementary Files

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