Sukumar Natarajan

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.

Thermal comfort is influenced by climate, expectations and adaptation opportunities. Hence the emergence of adaptive thermal comfort standards. Unfortunately, international adaptive standards and much of the underlying literature, are based on data collected from middle-income individuals in office or apartment blocks, raising the question of their validity in other contexts. This paper is the first large-scale investigation of the applicability of these standards to populations living in refugee/displacement camps, to establish whether comfort theory needs to be modified for such populations. This was achieved through highly difficult, data collection in camps in Jordan, Djibouti, Ethiopia and Peru. The data collected consists of 1982 rows of personal variables and concurrent environmental measurements. From these, the thermal comfort boundaries of the displaced were found to be wider (13K) than suggested in prevalent standards (8K). A new mathematical model of thermal comfort for such groups is hence developed. The results expand our understanding of comfort theory to include an understudied population and will be useful for those responsible for shelter for the currently 120M displaced, as they now allow rational design standards to be set. A tool based on the new model is currently being applied by aid agencies in camps in Afghanistan. Practical Application: The design of refugee accommodation across aid agencies requires the setting of design standards, as these impact material choice, construction techniques and cost, and hence the number of people that can be housed in any disaster. Highly constraining standards can thus reduce the population that can be housed, with associated implications for mortality rates. The alternative being the failure to set any quantitative target, creating a danger to life. The new thermal comfort model presented here allows for the first time the setting of targeted comfort design standards in camps.

Qatar has the unfortunate reputation of the country with the highest per-capita carbon emissions in the world, 15% of which is due to space cooling demand. We investigate the extent to which this demand is driven by ‘overcooling’ in non-domestic buildings, that is low indoor set-points resulting in wasted energy and cold thermal discomfort. Using a recently developed overcooling metric, ISO 7730 compliant sensors and occupant survey data comprising 2,472 responses from eight morphologically diverse office buildings, we find that 32% of occupants can be classed as uncomfortably cold. Our analysis implicates the application of the ‘international’ ASHRAE 55 thermal comfort standard in the observed overcooling. Using computer models of the studied buildings, we find that this overcooling is responsible for 27% of their cooling energy demand, translating to 4% of national cooling energy demand and 2% of carbon for all non-domestic buildings. Thus, a simple upward adjustment of set-point temperatures by ∼ 2°C in non-domestic buildings would greatly improve comfort and reduce energy consumption and carbon emissions without changing the building or its systems. This suggests an urgent need for a new localised thermal comfort standard, with wider regional applicability due to similar culture and climates. Practical application: Eliminating building overcooling yields in a notable reduction of 8.5% of cooling energy demand for every 1°C increase in the indoor setpoint temperature. This reduction results in an average of 32.3 kW·h/m ² /yr in a typical office building in Qatar. A comfort temperature of 24.9°C is suggested which is on average of 1.5°C warmer than the currently applied indoor temperature setpoint, resulting in substantial cooling reductions and increased thermal comfort. In practice, raising the indoor setpoint temperature by 2°C could prevent building overcooling, improve indoor thermal comfort and reduce cooling energy demand across in warm climates.

There is growing concern that airtight and well-insulated buildings designed to limit heat loss in temperate and cold climates could unintentionally elevate the risk of overheating in summers. Existing literature primarily uses dynamic simulation to investigate this problem due to the difficulty of obtaining large-scale in-performance data. To address this gap, we undertake a meta-analysis of large-scale indoor air temperature data for 195 UK dwellings, as a study of performance in a temperate climate. Of these, 113 are baseline (i.e., typical existing dwellings) and the rest designed to the high-performance Passivhaus standard. Using both Passivhaus and the well-known CIBSE TM59 overheating standards, this study found that there were few overheated cases for any building type. However, the average summer nighttime temperature of Passivhaus bedrooms was 1.6 °C higher than baseline, with 20 out of 31 measured bedrooms exceeding the overheating criterion, and the average overheating hours constituting approximately 19% of the total summertime observation period. These findings suggest that bedrooms in highly insulated dwellings may pose an overheating risk although whole-dwelling overheating risk is low.

Buildings are amongst the world's largest energy consumers and simultaneous peaks in demand from networks of buildings can decrease electricity system stability. Current mitigation measures either entail wasteful supply-side over-specification or complex centralised demand-side control. Here, we investigate a new schema for decentralised, self-organising building-to-building load coordination that requires very little information and no direct intervention. We find that the theoretically optimal size for load-coordination networks can be surprisingly small, analogous to other complex systems such as coordination between flocks of birds. The schema outperforms existing techniques, giving substantial peak-reductions as well as being remarkably robust to changes in other system parameters such as the network topology. This not only demonstrates that significant reductions in network peaks are achievable using remarkably simple control systems but also reveals theoretical results and new insights which which will be of great interest to the complexity and network science communities.