A case study on the impact of fixed input parameter values in the modelling of indoor overheating

Global efforts to reduce greenhouse gas emissions from buildings while also improving their environmental resilience have intensified. These efforts are often supported by building stock models which can inform policymakers on the impact of policies on energy consumption, greenhouse gas emissions and the indoor environment. The input values of such models are commonly informed by reference tables, which can result in inaccurate specification and incomplete representation of the distribution of possible values. In this modelling case study of a semi-detached dwelling archetype, the influence of using a reference U-value (2.1 W/(m2K)) for solid walls in England on heat-related mortality rate is compared to a probabilistic specification based on empirical evidence (median = 1.7W/(m2K)). Using the theoretical reference U-value generally resulted in a lower indoor overheating risk compared to the use of the empirically derived U-values pre-retrofit, but a larger increase in heat-related mortality rate following internal wall insulation (1.20%) than the use of the empirical median (0.94%, 95 % Confidence Interval = 0.87–0.99 %). This highlights the potentially significant implications of using fixed reference values. Future work will employ this probabilistic framework on multiple influential parameters

Modelling and monitoring tools to evaluate the Urban Heat Island's contribution to the risk of indoor overheating

The growth of cit ies increases urban surface areas and anthropogenic heat generation, causing an Urban Heat Island (UHI) effect. In the UK , UHI effects may cause positive (winter) and negative (summer) health , comfort and energy consumption consequences . With the increasing focus on climate change - related heat exposure and consequent increased mortality risk, there is a need to better investigate the UHI during hot seasons. This paper reviews the current literature regarding UHI characterisation using monitoring, modelling, and remote sensing approaches, their limitations, and applications in building simulation and population heat exposure models . Ongoing and future research is briefly introduced in which downscaling techniques are proposed that provide higher temporal and spatial information to assess and locate heat - associated health risk in London

Housing as a modifier of air contaminant and temperature exposure in Great Britain: A modelling framework

This paper presents the development of a modelling framework that quantifies the modifying effect of dwelling characteristics on exposure to indoor air pollution and excess temperature. A georeferenced domestic building stock model of Great Britain was created using national housing surveys, historical weather, and local terrain data. Dynamic building performance simulation was applied to estimate indoor air pollution and overheating risk metrics at the individual building level. These metrics were then aggregated at various geographic units and mapped across Britain within a Geographic Information System (GIS) environment to compare spatial trends. Results indicate that flats and newly built properties are characterised by lower indoor air pollution from outdoor sources, but higher air pollution from indoor sources. Flats, bungalows and newly built, more airtight dwellings are found to be more prone to overheating. Consequently, urban populations may experience higher levels of pollution from indoor sources and overheating resulting from the higher prevalence of flats in cities

Using building simulation to model the drying of flooded building archetypes

With a changing climate, London is expected to experience more frequent periods of intense rainfall and tidal surges, leading to an increase in the risk of flooding. This paper describes the simulation of the drying of flooded building archetypes representative of the London building stock using the EnergyPlus-based hygrothermal tool ‘University College London-Heat and Moisture Transfer (UCL-HAMT)’ in order to determine the relative drying rates of different built forms and envelope designs. Three different internal drying scenarios, representative of conditions where no professional remediation equipment is used, are simulated. A mould model is used to predict the duration of mould growth risk following a flood on the internal surfaces of the different building types. Heating properties while keeping windows open dried dwellings fastest, while purpose built flats and buildings with insulated cavity walls were found to dry slowest

Modelling population exposure to high indoor temperatures under changing climates, housing conditions, and urban environments in England

: The exposure of an individual to heat during hot weather depends on several factors including local outdoor temperatures and possible Urban Heat Island (UHI) effects, the thermal performance of the building they inhabit, and any actions that they are able to take in order to modify the indoor thermal conditions. There is an increasing body of research that seeks to understand how housing, UHI, and occupant profiles may alter the risk of mortality during hot weather. Housing overheating models have been of particular interest due to the amount of time spent indoors and the need to improve the energy efficiency of the UK housing stock. A number of housing overheating models have been created in order to understand how changes to the building stock and climate may alter heat exposure and risks of heatrelated mortality. We briefly describe the development of a metamodel – a model derived from the outputs of EnergyPlus dynamic thermal simulation models of building variants – and its application to a housing stock model representative of the West Midlands, UK. We model the stock under a ‘current’ scenario, as described by the 2010-2011 English Housing Survey, and then following a full energy-efficient building fabric retrofit or the installation of external window shutters. Initial results indicate a wide range of overheating risks inside dwelling variants in Birmingham, with flats and bungalows most vulnerable to overheating, and detached dwellings least vulnerable. Modelling of the full retrofit of buildings indicated that the stock would experience an overall increase in overheating, while external shutters were able to decrease overheating significantly

Mapping indoor overheating and air pollution risk modification across Great Britain: A modelling study

Housing has long been thought to play a significant role in population exposure to environmental hazards such as high temperatures and air pollution. However, there is sparse data describing how housing may modify heat and air pollution exposure such that housing's role in poor health and mortality from these hazards may be estimated. This paper describes the development of individual-address level indoor overheating and air pollution risk modifiers for Great Britain, for use alongside historical weather, outdoor air pollution, population socio-economic data, and mortality data in a large-scale epidemiological investigation. A geographically-referenced housing stock database was developed using the Homes Energy Efficiency Database (HEED) and the English Housing Survey (EHS). Simulations of unique combinations of building, fabric, occupation, and environment were run using a modelling framework developed for EnergyPlus 8.0, estimating indoor temperature metrics, indoor/outdoor ratio of pollution from outdoor sources, and indoor air pollution from multiple indoor sources. Results were compiled, matched back to individual properties in HEED, and mapped using Geographical Information Systems (GIS). Results indicate urban areas had higher numbers of buildings prone to overheating, reduced levels indoor air pollution from outdoor sources, and higher air pollution from indoor sources relative to rural areas, driven largely by variations in building types. The results provide the first national-scale quantitative estimate of heat and indoor air pollution modification by dwellings, aggregated at levels suitable for inclusion in health analysis

Modelling Long-Term Urban Temperatures with Less Training Data: A Comparative Study Using Neural Networks in the City of Madrid

In the last decades, urban climate researchers have highlighted the need for a reliable provision of meteorological data in the local urban context. Several efforts have been made in this direction using Artificial Neural Networks (ANN), demonstrating that they are an accurate alternative to numerical approaches when modelling large time series. However, existing approaches are varied, and it is unclear how much data are needed to train them. This study explores whether the need for training data can be reduced without overly compromising model accuracy, and if model reliability can be increased by selecting the UHI intensity as the main model output instead of air temperature. These two approaches were compared using a common ANN configuration and under different data availability scenarios. Results show that reducing the training dataset from 12 to 9 or even 6 months would still produce reliable results, particularly if the UHI intensity is used. The latter proved to be more effective than the temperature approach under most training scenarios, with an average RMSE improvement of 16.4% when using only 3 months of data. These findings have important implications for urban climate research as they can potentially reduce the duration and cost of field measurement campaigns

The modifying effect of the building envelope on population exposure to PM2.5 from outdoor sources.

UNLABELLED: A number of studies have estimated population exposure to PM2.5 by examining modeled or measured outdoor PM2.5 levels. However, few have taken into account the mediating effects of building characteristics on the ingress of PM2.5 from outdoor sources and its impact on population exposure in the indoor domestic environment. This study describes how building simulation can be used to determine the indoor concentration of outdoor-sourced pollution for different housing typologies and how the results can be mapped using building stock models and Geographical Information Systems software to demonstrate the modifying effect of dwellings on occupant exposure to PM2.5 across London. Building archetypes broadly representative of those in the Greater London Authority were simulated for pollution infiltration using EnergyPlus. In addition, the influence of occupant behavior on indoor levels of PM2.5 from outdoor sources was examined using a temperature-dependent window-opening scenario. Results demonstrate a range of I/O ratios of PM2.5 , with detached and semi-detached dwellings most vulnerable to high levels of infiltration. When the results are mapped, central London shows lower I/O ratios of PM2.5 compared with outer London, an apparent inversion of exposure most likely caused by the prevalence of flats rather than detached or semi-detached properties. PRACTICAL IMPLICATIONS: Population exposure to air pollution is typically evaluated using the outdoor concentration of pollutants and does not account for the fact that people in London spend over 80% of their time indoors. In this article, building simulation is used to model the infiltration of outdoor PM2.5 into the domestic indoor environment for dwellings in a London building stock model, and the results mapped. The results show the variation in relative vulnerability of dwellings to pollution infiltration, as well as an estimated absolute indoor concentration across the Greater London Authority (GLA) scaled by local outdoor levels. The practical application of this work is a better understanding of the modifying effect of the building geometry and envelope design on pollution exposure, and how the London building stock may alter exposure. The results will be used to inform population exposure to PM2.5 in future environmental epidemiological studies

Assessing heat vulnerability in London care settings: case studies of adaptation to climate change

This pilot study aims at testing methods to assess heat vulnerability in London care homes and develop overheating reduction strategies to mitigate temperature exposure and the associated negative health impacts under the warming climate, with a view to scaling up the project on a national scale. It undertakes feasibility work to identify possible causes of overheating across a range of care home types and evaluate the current and future potential of indicative passive solutions. The summertime thermal environments of five case study care homes were monitored and their physical, technical and occupancy profiles were established through surveys. The data was inputed in the EnergyPlus V8.9 dynamic thermal simulations via the DesignBuilder Graphical User Interface. Future overheating risks and their reduction potential through the use of passive strategies were tested under a set of representative climate change scenarios, during a five-day heatwave period. The dynamic thermal simulation analysis indicated that older buildings with higher heat loss and thermal mass capacities are likely to benefit more from the application of high albedo materials rather than external shading methods, whereas newer and highly insulated buildings seem to benefit more from higher ventilation rates and appropriate external shading systems. Night ventilation emerged as the single most impactful passive technique for all building types. This feasibility work has developed novel methods, knowledge and insights that will be helpful in understanding how to enable care settings in the UK to become resilient to rising heat stress. This is one of the first systematic attempts to build a set of dynamic thermal models of care homes in the UK

Building characteristics as determinants of propensity to high indoor summer temperatures in London dwellings

Cities are expected to experience an increasing risk of overheating due to climate change and the urban heat island phenomenon. Although external factors, such as urban morphology and greening, may influence the spatio-temporal variation of overheating risk, the individual building characteristics are also likely to be important. This paper presents the results of EnergyPlus dynamic thermal simulations of 3456 combinations of dwelling types and characteristics selected to represent the London domestic stock. Two Design Summer Year weather files were used to represent the current and future climate: the CIBSE 1984–2004 and a UKCP09 future weather file (50th percentile of external temperature, 2050s, medium emissions scenario). Appreciable variation between dwelling types but generally greater variation within dwelling type was found depending on such factors as orientation, surrounding buildings and insulation levels. Under the current climate, the insulation levels had considerable impact on indoor temperatures, with combined retrofitting of roof insulation and window upgrades reducing daytime living room temperatures during the warmest continuous 5-day period of modelling by, on average, 0.76 °C (%95C.I. 0.63, 0.89 °C) for mean temperature and 1.30 °C (%95C.I. 1.05, 1.54 °C) for maximum temperature. On the other hand, internally retrofitted walls and floors tended to increase daytime living room temperatures, with a combined effect of 0.46 °C (%95C.I. 0.33, 0.60 °C) increase in mean temperature and 0.71 °C (%95C.I. 0.47, 0.96) increase in maximum temperature. Within the context of a changing climate, knowledge of insulation characteristics after retrofitting is crucial for the accurate identification of dwellings with greatest overheating potential