A high-resolution indoor heat-health warning system for dwellings

Climate change projections indicate that the world's most populated regions will experience more frequent, intense and longer-lasting heatwave periods over the coming decades. Such events are likely to result in widespread overheating in the built environment, with a consequential increase in heat-related morbidity and mortality. In order to warn the population of such risks, Heat-Health Warning Systems (HHWSs) are being progressively adopted world-wide. Current HHWSs are, however, based solely on weather observations and forecasts and are unable to identify precisely where, when, or to what extent individual buildings (and their occupants) will be affected. In contrast, AutoRegressive models with eXogenous inputs (ARX) have been demonstrated to reliably forecast indoor temperatures in individual rooms using minimal data. Thus, the large-scale deployment of forecasting models could theoretically enable the development of a high-resolution indoor HHWS (iHHWS). In this study, ARX models were tested over the long-lasting UK heatwave of 2018 using hourly monitored dry-bulb temperature data from 25 rooms (12 living rooms and 13 bedrooms) in 12 dwellings, located within the London Urban Heat Island (UHI). The study investigates different approaches to improving the reliability of room-based heat exposure predictions at longer forecasting horizons. The effectiveness of the iHHWS system was assessed by evaluating the accuracy of predictions (using fixed and adaptive temperature thresholds) at different lead times (1, 3, 6, 12, 24, 48 and 72 h ahead). Compared to forecasted indoor temperatures, a Cumulative Heat Index (CHI) metric was shown to increase the reliability of heat-health warnings up to 24 h ahead

Inhabitant actions and summer overheating risk in London dwellings

<p>An indoor overheating assessment study of 101 London dwellings during summer 2009 is presented. The study included building surveys, indoor dry bulb temperature monitoring and a questionnaire survey on occupant behaviour, including the operation of passive and active ventilation, cooling and shading systems. A theoretical London housing stock comprising 3456 combinations of building geometry, orientations, urban patterns, fabric retrofit and external weather was simulated using the EnergyPlus thermal modelling software. A statistical meta-model of EnergyPlus was then built by regressing the independent variables (simulation input) against the dependent variables (overheating risk). The monitoring and questionnaire data were analysed to explore the relationship between self-reported behaviour and overheating, and to test the meta-model. The monitoring data indicated that London homes and, in particular, bedrooms are already at risk of overheating during hot spells under the current climate. Around 70% of respondents tended to open only one or no windows at night mainly due to security reasons. An improvement in the coefficient of determination (<i>R</i>²) values between measured temperature and meta-model predictions was obtained only for those dwellings where occupants reported actions that were in line with the modelling assumptions, thus highlighting the importance of occupant behaviour for overheating.</p

Ten questions concerning residential overheating in Central and Northern Europe

Rising global temperatures and more frequent heatwaves due to climate change have led to a growing body of research and increased policy focus on how to protect against the adverse effects of heat. In cold and temperate Europe, dwellings have traditionally been designed for cold protection rather than heat mitigation. There is, therefore, a need to understand the mechanisms through which indoor overheating can occur, its effects on occupants and energy consumption, and how we can design, adapt, and operate buildings during warm weather to improve thermal comfort and reduce cooling energy consumption. This paper brings together experts in overheating from across Europe to explore 10 key questions about the causes and risks from overheating in residential settings in Central and Northern Europe, including the way in which we define and measure overheating, its impacts, and its social and policy implications. The focus is not on summarising literature, but rather on identifying the evidence, key challenges and misconceptions, and limitations of current knowledge. Looking ahead, we outline actions needed to adapt, including the (re)design of dwellings, neighbourhoods, and population responses to indoor heat, and the potential shape of these actions. In doing so, we illustrate how heat adaptation is a multi-faceted challenge that requires urgent and coordinated action at multiple levels, but with feasible solutions and clear benefits for health and energy

Development of an England-wide indoor overheating and air pollution model using artificial neural networks

<p>With the UK climate projected to warm in future decades, there is an increased research focus on the risks of indoor overheating. Energy-efficient building adaptations may modify a buildings risk of overheating and the infiltration of air pollution from outdoor sources. This paper presents the development of a national model of indoor overheating and air pollution, capable of modelling the existing and future building stocks, along with changes to the climate, outdoor air pollution levels, and occupant behaviour. The model presented is based on a large number of EnergyPlus simulations run in parallel. A metamodelling approach is used to create a model that estimates the indoor overheating and air pollution risks for the English housing stock. The performance of neural networks (NNs) is compared to a support vector regression (SVR) algorithm when forming the metamodel. NNs are shown to give almost a 50% better overall performance than SVR.</p