Research

This paper attempts a comparative analysis of embodied carbon and thermal performance of new lightweight construction technologies of the GFRG Panel system, EPS core Panel system, AAC blocks, and fly-ash bricks with conventional construction of burnt clay brick walls with RCC roof in the composite climate zone of Delhi, India. To cater to the huge affordable housing demand in urban India by 2030, it is crucial to mainstream speedy alternate construction technologies that are low in embodied carbon and ensure thermal comfort for the occupants. The study involved first creating a data inventory on thermo-physical properties and global warming potential for the identified construction. A typical EWS housing unit is modeled in Design Builder for conducting thermal simulations. On-site air temperature and globe temperature data loggers were put in the worst-case scenario to measure the real-time indoor operative temperature in two affordable housing units made of burnt clay brick walls and AAC walls in Delhi to validate the developed model. Simulations were carried out for all the walling and roofing material combinations on the validated model. All the material combinations have also been examined with 25mm (0.98 in.) thick EPS insulation. The resulting indoor operative temperatures are compared with the comfort bands given by IMAC-R (2022) and ASHRAE-55 (2020) to assess the annual and summer discomfort degree hours for the peak summer month of June. The volume of each walling and roofing material assembly is calculated from the case study flat to calculate the embodied emissions from the GWP potential (Kg CO2 eq.) of each material. The study discourages the use of burnt clay bricks due to high embodied carbon, recommending fly ash bricks, AAC blocks, and EPS core panels. EPS core panels used in walls and the roof together exhibit optimal thermal performance with low embodied emissions. GFRG panels are low on embodied carbon but need insulation for thermal comfort. The study informs decisions on the large-scale adoption of lightweight construction materials and technologies for affordable housing.

With climate change, low carbon space-cooling approaches are becoming more important. Cooling energy demand can be reduced through new interventions, low energy systems, and optimised operation. The adaptive comfort model for mixed mode operation can be a promising approach to the cooling energy challenge. However, adaptive models use indoor operative temperature, which requires the measurement of air temperature, air velocity, and globe temperature in a space. Collecting real-time and long-term data for these is difficult. This paper summarises a study on an affordable cooling approach to develop a machine learning algorithm to predict OT. Field measurements and Energy Plus simulation were used to create large datasets, 75 % of which were used to train the machine learning algorithm to predict operating temperature, and the remaining 25% were used for testing the algorithm. The testing of the OT predicted with the random forest model shows an RMSE of 0.34%. In terms of classification of the thermal environment as being in/out of the adaptive comfort band, 0.88% of values were misclassified. When the predicted OT values were compared with the one-week measured OT values, the RMSE was found to be 3%. The results demonstrate that our algorithm that uses indoor air temperature readings in a space and outdoor weather station data can reliably predict OT. This enables a scalable and affordable approach for accurate and long- term prediction of OT to determine the comfort condition. This will enable control systems to use OT to determine thermal comfort in a space using adaptive comfort models and to account for ceiling fan usage to reduce or eliminate air-conditioning (AC).

With climate change, low carbon space-cooling approaches are becoming more important. The adaptive comfort model for mixed mode operation can be a promising approach to the cooling energy challenge. However, adaptive models use indoor operative temperature, which requires the measurement of air temperature, air velocity, and globe temperature in a space. Collecting real-time and long-term data for these is difficult. We used machine learning to predict operative temperature with minimum measurement equipment, for controls to optimise fan operation and minimise AC energy use. Field measurements and Energy Plus simulation were used to create large datasets, 75 % of which were used to train the machine learning algorithm to predict operating temperature, and the remaining 25% were used for testing the algorithm. The random forest model in R from the tidymodels library using the ranger engine proved successful. The Operative Temperature prediction Root Means Square Error value is 0.090°C. The algorithm classified data for being in/out of the comfort band, and 0.88% of the values are misclassified. While this paper demonstrates the machine learning approach that makes this possible, future work will demonstrate the implementation of the control algorithm and its testing.

This research evaluates the environmental sustainability aspects of India's Smart Cities Mission. The study analyzes the promises made by the initiative and assesses its actual performance in terms of environmental sustainability.

Low-carbon space-cooling technologies are becoming more relevant as a result of climate change. The adaptive comfort model for mixed mode operation has the potential to be a promising solution to the cooling energy dilemma. However, these models have not taken into account the impact of ceiling fans on overall comfort. Several laboratory studies has shown that ceiling fans are very effective for providing thermal comfort to occupants. But, limited field studies assessing higher air circulation supplied by ceiling fans in a real-world educational environment have been done in composite climate conditions. A field investigation of the effect of increased air movement on thermal comfort was performed in an educational building in the composite climate of Delhi during the autumn month (October). This was performed in uncontrolled environmental conditions with 2 scenarios of fans kept ON and OFF. Field measurements and occupant surveys were used to gather data and analysed. The results show the thermal acceptance increased by 34% when the fans are turned ‘ON’. The occupants reported a cooler environment overall with low relative humidity and low MET rate being more comfortable. The fan speed of 0.2 m/s corresponding to operation of 1 was found desirable. A 13% dip in discomfort hours was observed for the whole year when the band shift of 2.5°C due to change in air speed was incorporated from 84% to 71%. A 26% dip in the discomfort hours was observed for the measured data period when the band shift of 2.5°C due to change in air speed was incorporated from 64% to 38%. The same can be found in the survey output and the band shift feature stands true with the survey output. Future work will focus on the study expanded on to different seasons and climatic conditions.

This study investigates the performance of natural, traditional, and recycled insulating materials in current and anticipated climate conditions according to their impact on building energy consumption and thermal comfort. We develop future weather under two climate paths—SSP1-2.6 (low-emissions) and SSP5-8.5 (high-emissions)—for the years 2050 and 2099, using building energy modeling and an ensemble of five Global Climate Model members. Results show a clear shift towards more cooling demands and reduced heating demands, with SSP5 scenarios experiencing greater temperature increase and diurnal ranges than SSP1. Natural materials such as hemp and wood fiber act as well as the traditional ones and maintain thermal performance even in more severe future climatic conditions. Recycled cellulose insulation shows the lowest total energy demand. These findings suggest that climate-resilient insulation materials, especially recycled and bio-based materials, can facilitate energy-efficient and sustainable buildings. Adaptive design solutions must be more urgent with climate change redefining thermal performance.

This study investigates the effectiveness of various insulation materials for energy consumption, mold growth risk, and chemical exposure under future climate scenarios. The research employs building energy modeling simulations using EnergyPlus and Design Builder software, analyzing a hypothetical residential building in Austin, Texas. The study examines 9 insulation types, including conventional (glass wool, rock wool, EPS), natural (flax, hemp, wood fiber), and recycled (cellulose, rubber, polystyrene fibers) materials. Climate projections are based on 5 Global Climate Models (GCMs) and 4 Shared Socioeconomic Pathways (SSPs), spanning from 2035 to 2099. The Finnish mold growth model is utilized to assess mold risk, while chemical exposure is evaluated using intake fraction analysis.

Results indicate that natural and recycled insulation materials perform comparably to conventional options across various climate scenarios. Under the SSP5-2099 scenario, cooling demands increase significantly, with heating requirements becoming minimal. Natural materials like hemp and wood fiber demonstrate resilience in extreme conditions, maintaining efficiency comparable to synthetic insulators. Mold growth risk analysis reveals higher susceptibility in bio-based materials, with flax insulation showing a peak mold index of 1.3 in January. Chemical exposure assessment highlights lower risks for glass wool, while wood fiber insulation shows higher potential exposure due to binder components. The study concludes that natural and recycled insulation materials can provide effective alternatives to conventional options, balancing energy efficiency, sustainability, and health considerations in future climate scenarios.

The project aimed to create an architectural experience simulating a hot, dry desert climate with a unique twist: nights were designed to be hot, and days cool and serene, using Austin, Texas during a typically hot period (August 7–21) as the context. The design featured three distinct thermal zones: a highly glazed greenhouse (90% glazing, south and west orientation), a central habitable space, and a cool room. Passive design strategies were prioritized, relying on high thermal mass materials (limestone walls, thick insulation), thermal lag, and carefully scheduled zone mixing and night ventilation. The greenhouse achieved extreme temperatures (peaking at ~140°C/284°F, remaining above 61.6°C/143°F), serving as a heat source for the living area, while the cool room maintained consistently low temperatures. Through zone mixing and ventilation schedules, the habitable space maintained an average temperature of 27.8°C (82°F) during the day and 46.4°C (115.5°F) at night, closely matching the intended desert-like thermal experience despite Austin’s actual climate.

The analysis of a 48-unit, 2-bedroom apartment building in Austin, Texas, revealed several key findings regarding energy use and design optimization. Implementing Variable Refrigerant Flow (VRF) systems with higher coefficients of performance (CoP) and adaptive setpoint controls based on the ASHRAE 55 adaptive comfort model resulted in significant reductions in energy consumption and improved occupant comfort. Specifically, the VRF system with setpoint control achieved a 52.4% reduction in Energy Performance Index (EPI), an 83.3% reduction in cooling load, and a 38.3% reduction in heating load compared to baseline, highlighting the effectiveness of adaptive strategies in variable climates like Austin. Upgrades to glazing (triple low-emissivity, air-filled windows) and building envelope (R30 insulation in walls and roof) further reduced heating loads by over 70% and cooling loads by over 65%, with envelope improvements yielding the highest overall energy savings. Lighting controls, applied to select spaces, contributed modest additional savings. The inclusion of a rooftop photovoltaic array demonstrated the potential for substantial renewable energy generation, although detailed modeling is needed to fully optimize its impact. Overall, the project demonstrates that a combination of advanced HVAC controls, high-performance building envelope, and targeted lighting and renewable strategies can achieve major reductions in energy use for multi-unit residential buildings.

In the heart of Austin, the "Inverted Desert" is a unique structure designed to invert the typical thermal experiences of a desert environment. Thick limestone walls act as thermal batteries, absorbing and storing the night’s coolness to keep the central living area refreshingly cool during the scorching day, with temperatures averaging around 27.8°C. As night falls, these same walls release stored heat, unexpectedly raising the interior temperature to an average of 46.4°C, creating a warm nocturnal oasis. The building features a greenhouse chamber that intensifies daytime heat to extreme levels (up to 140°C) and a separate cool chamber offering respite from the night’s warmth. This deliberate inversion of day-night thermal cycles challenges occupants’ circadian rhythms and offers a novel, immersive experience inspired by the extremes of desert living.