Elangovan Rajasekar

Global warming is projected to exacerbate thermal stress, critically posing a threat to human well-being and productivity. Among the two forms of thermal stress, overheating is the most critical ch...

Enhancing the energy efficiency of urban housing is crucial for improving energy access in Ethiopia, where a persistent gap exists between energy demand and supply. The nationwide Integrated Housing Development Program (IHDP), implemented across diverse climatic zones with limited attention to local context and building energy performance, has inadvertently produced thermally uncomfortable and energy-inefficient urban dwellings. This study employs a scenario-based intelligent energy simulation framework to address this inefficiency by identifying energy-saving opportunities in urban housing, pioneering the smart meter data-driven ClimateStudio-EnergyPlus workflow-validated through machine-readable consumption analytics (R$^2$ = 0.79 in Addis Ababa; 0.64 in Adama) and on-site surveys across temperate highland Addis Ababa and hot-arid Adama. Appliance usage inventory reveals cooking dominance, accounting for 60-62.9% of total loads. Adama's 20% household fan-based thermal coping behavior signals a latent air-conditioning demand projected to surge 31% above baseline in arid lowlands versus 6% in temperate zones as affordability erodes overheating tolerance. Monthly consumption heatmaps reveal unit-size and seasonal interaction effects diverging sharply between cities, with Adama's studio units peaking at approximately 191~kWh/month in the dry season against a stable 150-190~kWh/month range in Addis Ababa. Envelope diagnostics expose a systemic performance gap, wall U-values of 1.61~W/m$^2$K and glazing SHGC of 0.86-0.87 exceeding best-practice targets by three- to fourfold, with Adama's larger glazed area compounding solar heat gain risk in the climate zone least suited to absorb it. Targeting dominant local cooking appliance upgrades yields immediate operational savings of 18-18.9%. Furthermore, these empirical benchmarks recommend region-tailored building envelopes in conjunction with Minimum Energy Performance Standards (MEPS) for cooking appliances, establishing foundational performance data for Ethiopia's unimplemented building codes and informing climate-affordability demand forecasting to achieve policy-aligned efficiency gains.

Human-centric intelligent environments require reliable sensing mechanisms capable of identifying occupant cognitive states in real time. In adaptive building systems, differentiating between cognitive stress and boredom is particularly important, as these states require contrasting environmental responses. This study presents a systematic cross-modal comparison of facial infrared thermography (fITI) and eye-tracking features for contactless detection of these states. Eight participants completed stress (Montreal Imaging Stress Test) and boredom (sustained visual monitoring) tasks under controlled lighting conditions (150 lux artificial lighting and 300 lux daylight), while synchronized thermal and oculomotor data were recorded. Linear mixed-effects models were applied to 320 observations per modality. Results revealed three key findings. First, spatial thermal heterogeneity within facial regions of interest (ROI), quantified through pixel standard deviation, outperformed conventional mean temperature measures. The strongest thermal effect was observed in the Lower Left Cheek (|t| = 7.92), reflecting asynchronous discharge dynamics of facial arteriovenous anastomoses under sympathetic activation. A lateralized hemispheric asymmetry (|t| = 6.93) further indicated inter-hemispheric thermal divergence between states. Second, eye-tracking measures demonstrated high discriminative capability, with nine out of ten features reaching significance and a 7.2-fold median effect-size advantage over thermal metrics; baseline-corrected pupil diameter showed the strongest separation (|t| = 46.06) with immediate and stable differentiation between conditions. In contrast, thermal responses exhibited dual temporal dynamics, with immediate separation in heterogeneity features and progressive divergence in periorbital maxima over 5–10 minutes. Third, variance decomposition identified periorbital thermal regions (ICC = 0.06–0.09) as calibration-free indicators suitable for practical sensing systems, complementing low-ICC oculomotor features. Subjective validation through NASA-TLX (d = 1.63; per-participant ρ = 0.63) confirmed that physiological markers tracked experienced cognitive states rather than task labels. The resulting feature set is compatible with commercially available sensing hardware and provides a foundation for integrating human-centric physiological monitoring into intelligent adaptive environments.

Humans are exposed to fluctuating thermal settings in their daily lives. Subjective evaluation remains the widely used technique to evaluate the impact of such environmental variations on subjects ...

Buildings are responsible for 39% of world carbon emissions, mostly from heating and cooling driven by the weather. Hence, successfully designing buildings for climate mitigation and adaptation is fundamentally dependent on good quality current and future weather timeseries. Unfortunately, in most of the world and especially in the Global South, where the impacts of climate change are predicted to be greatest, data with sufficient geographic or temporal resolution do not exist. Here we demonstrate a new globally-relevant method to produce high spatial-resolution, carefully calibrated, mutually consistent, hourly, current and future typical weather years using low-cost and high-quality synthetic data. The approach is then applied to India, which currently accounts for 18% of world population and is expected to add around 14% of all new buildings in the world by 2050. This results in an order of magnitude improvement in spatial resolution (∼400 → 4790 locations) for current (1981-2010) climate, with future (2060-2089) climate represented for the first time. Systematic comparison of several methods suggested multivariate kriging as ideal to scale spatio-temporally sparse calibration data to all 4790 locations. By moving a calibrated computer model of a typical home over all 4790 locations, we find a mean increase of 3 K in indoor summer temperatures and a 51% increase in cooling demand by 2060 compared to 2010, and the potential for severe heat stress. Finally, we make these files free-to-use, thus creating the first such large-scale public repository for anywhere in the Global South, with potential use in many other fields such as infrastructure and crop resilience. Practical Application: As already shown, when completing simulations, unless weather data is highly local, the results can be out by a factor of two. This not only risks incorrect design decisions, but potentially undermines the energy simulation industry. In the global south this has the potential to lead to buildings that are a health risk to occupants, or unnecessarily high air conditioning loads placing strain on unstable electricity grids. By demonstrating a mathematically rigorous, validated, low-cost approach, this work demonstrates for the first time the potential for the whole Global South to be represented by data that will allow accurate predictions of temperatures and energy demand.

It is now evident that periods of extreme temperature and humidity can transform buildings from places of shelter to sources of significant morbidity and mortality. Mitigating this risk requires computer representations of such events. Unfortunately, neither an agreed globally consistent representational method nor long-term continuous hourly weather data at a sufficient spatial resolution exist, from which such events can be extracted. Here, we introduce a new, mathematically sound, physically meaningful and consistent method to represent hot-dry, hot-wet and cold-dry extreme weather events of arbitrary length localised to the weather of the location in question, unlike existing methods that use absolute temperature thresholds regardless of locale. Importantly for human survival, this new method includes humidity. Replicable globally, we apply this method to India using carefully calibrated computer-generated weather data localised to 25 km (4790 locations) for now and the future, making India the first Global South country to obtain such an extreme event dataset. A series of tests show high consistency and reliability including low mean bias error against all known long-term (∼100 year) dry-bulb hot extremes of +0.2°C (σ = 2.4°C) and a mean deviation of 4.5°C (σ = 4.9°C) compared to the equivalent extreme period in a typical year. By moving a validated computer model of a typical apartment across all 4790 locations, we find significant, spatially varied, differences in indoor mean-maximum temperature and discomfort degree hours for a range of events, past (1981-2010) and future (2060-2089). Periods where mortality is likely to be significant are found. The reliability of the data, combined with their unprecedented spatiotemporal resolution and timescale, transforms our ability to study a wide range of weather-influenced phenomena, such as indoor and outdoor risks to human health or crop yields, under current and climate-changed weather. Practical Application: There is an urgent need for the public and professionals to fully understand that the impacts of climate change will be most transformative with respect to extreme, not mean, temperatures; with mass mortality becoming common. The method introduced here for the creation of extreme timeseries is general and replicable globally. The files produced for the case of India are freely available and already in use via a public repository. They are helpful to building designers, urban planners, policymakers and professionals in a variety of fields to analyse performance under extreme conditions and the implications for human health.