Chronic overheating in low carbon urban developments in a temperate climate

Numerous studies have reported on overheating in urban contexts the majority of which have focused on the influences of external factors, such as: heat waves and climatic change. To date very little research has examined the more insidious issue of chronic year-round overheating in temperate climatic zones. The present study begins by reviewing the potential implications of planning and legislative constraints underlying urban residential design. A case study example is then introduced to examine the potential manifestation of such issues in practice. Detailed field monitoring and survey data from a number of newly built flats in a multi-residential block in London, is presented. Typical of a new generation of urban dwellings the development incorporates a high thermal specification together with low carbon building services, such as communal heating systems and mechanical ventilation with heat recovery. Through detailed zonal measurements of a broad range of environmental and building services parameters it has been possible to isolate the key factors underpinning poor overheating performance for these flats. The findings of this case study are part of a larger research project investigating the causes of overheating in high density urban dwellings across Greater London. The results suggest that the causes of chronic overheating in these modern low-energy flats are multiple, but typically share common factors stemming from poorly integrated architectural and MEP design decisions. Conflicts between regional planning policies, UK building regulations, and health and safety legislation appear to be compounding the problem

An investigation into future performance and overheating risks in Passivhaus dwellings

In response to UK government policy mandating the construction of 'zero carbon' homes by 2016 there have been significant changes in the way dwellings are being designed and built. Recent years have seen a rapid uptake in the adoption of the German Passivhaus standard as a template for ultra-low energy and zero carbon buildings in the UK. Despite genuine motivations to mitigate climate change and fuel poverty there is a lack of research investigating the long-term performance of Passivhaus buildings in a rapidly changing UK climate. This paper sets out to investigate whether Passivhaus dwellings will be able to provide high standards of thermal comfort in the future or whether they are inherently vulnerable to overheating risks. Scenario modelling using probabilistic data derived from the UKCP09 weather generator (WG) in conjunction with dynamic simulation and global sensitivity analysis techniques are used to assess the future performance of a range of typical Passivhaus dwellings relative to an identical Fabric Energy Efficiency Standard (FEES) compliant dwelling over its notional future lifespan. The emphasis of this study is to understand what impact climate change will pose to overheating risks for Passivhaus dwellings relative to the de facto (i.e. FEES) alternative, and which design factors play a dominant role in contributing to this risk. The results show that optimization of a small number of design inputs, including glazing ratios and external shading devices, can play a significant role in mitigating future overheating risks. © 2013 Elsevier Ltd

Prediction of internal temperatures during hot summer conditions with time series forecasting models

A novel application using adaptive autoregressive time series forecasting with exogenous inputs (i.e. ARX) has been developed in order to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions (i.e. heatwaves). The study shows that with proper selection of the predictors, based on the Akaike Information Criterion (AIC), the forecasts provide acceptable accuracy for periods up to 72 hours. The hourly results for the analysed dwellings showed a Mean Absolute Error (MAE) below 0.63°C and 0.49°C for the two case study dwellings across the 3-day forecasting period, during the 2015 heatwave. These findings point to the potential for using time series forecasting as part of an overheating warning system in buildings, especially those housing vulnerable occupants

A proposed method for generating high resolution current and future climate data for Passivhaus design

The sensitivity of low energy and passive solar buildings to their climatic context creates a requirement for accurate local climate data. This situation takes on increasing importance in the context of modelling Passivhaus buildings where the absence of conventional oversized heating and cooling systems implies a greater reliance upon fabric and system optimisation. Conversely, future climatic changes may also pose serious implications for super insulated buildings with inadequate solar shading. Currently, many widely used building performance simulation (BPS) tools still rely on very limited sources of climate data. The following research examines the need for regional and, in some cases, micro-regional climatic data when designing ultra-low energy Passivhaus buildings in the UK. The paper proposes a new methodology for generating this data in PHPP format. The data generated is compared to alternative sources, and the implications discussed in the context of three case studies examining a certified Passivhaus dwelling in a mountainous region of Wales as well as two locations, in close proximity, within London. If correctly implemented the use of such data should provide a more robust basis for future cost and performance optimisation in low energy and passive building design

Opening the black box: Enhancing community design and decision making processes with building performance simulation

It is widely acknowledged that faced with diverse future impacts (including climatic changes, economic instability and energy supply vulnerabilities) buildings and communities’ worldwide need to become increasingly resilient. The work presented in this paper investigates how Community Design and Decision Making (CDDM) processes can be enhanced through the use of design thinking techniques involving Building Performance Simulation (BPS). The research presented is based on findings from a real-world case study project involving the design of a mixed-use zero carbon community welcome centre planned for the Findhorn Eco-Community, in Scotland, UK. Throughout the conceptual and early design stages the community played a crucial part in the decision making process. Extensive consultation and community engagement exercises formed the basis from which initial design concepts were produced and evaluated. BPS results and in particular the use of sensitivity analysis (SA) techniques played a major contributing role in establishing a multicriteria evidence base from which to inform the CDDM process

PassivBIM: enhancing interoperability between BIM and low energy design software

The process of building design is currently undergoing some major changes. In an attempt to mitigate climate change, the design of more sustainable buildings is advocated by the UK government. Furthermore, standalone design methods are being replaced with the concept of Building Information Modelling (BIM). The adoption of BIM has been documented to result in many benefits, which range from time to cost savings. During the initial planning stages, building performance simulation (BPS) can be used to inform design decisions. Data can be exchanged between BIM and BPS tools using data transfer schemas such as the Industry Foundation Classes (IFC). The IFC schema lacks an energy domain, and as a result, an extension is proposed in this paper. This contains energy concepts from a BPS tool called Passive House Planning Package (PHPP). The extended schema was developed by way of an externally coupled Java tool, which facilitates the transfer of data, and informs the building design decision-making process. The process of geometry extraction has been validated with several case studies, which are based on certified Passivhaus buildings in Hannover Kronsberg, Germany and Ebbw Vale, Wales. The amount of error is acceptable, and it is mostly due to differences in the initial BIM model setup, not due to the processing of IFC files

Forecasting indoor temperatures during heatwaves using time series models

Early prediction of impending high temperatures in buildings could play a vital role in reducing heat-related morbidity and mortality. A recursive, AutoRegressive time series model using eXogenous inputs (ARX) and a rolling forecasting origin has been developed to provide reliable short-term forecasts of the internal temperatures in dwellings during hot summer conditions, especially heatwaves. The model was tested using monitored data from three case study dwellings recorded during the 2015 heatwave. The predictor variables were selected by minimising the Akaike Information Criterion (AIC), in order to automatically identify a near-optimal model. The model proved capable of performing multi-step-ahead predictions during extreme heat events with an acceptable accuracy for periods up to 72 h, with hourly results achieving a Mean Absolute Error (MAE) below 0.7 °C for every forecast. Comparison between ARX and AutoRegressive Moving Average models with eXogenous inputs (ARMAX) models showed that the ARX models provided consistently more reliable multi-step-ahead predictions. Prediction intervals, at the 95% probability level, were adopted to define a credible interval for the forecast temperatures at different prediction horizons. The results point to the potential for using time series forecasting as part of an overheating early-warning system in buildings housing vulnerable occupants or contents

Can semi-parametric additive models outperform linear models, when forecasting indoor temperatures in free-running buildings?

A novel application combining semi-parametric Generalized Additive Models (GAMs) with logistic GAMs was developed to forecast indoor temperatures and window opening states during prolonged heatwaves. GAM models were compared to AutoRegressive models with eXogenous inputs (ARX) and validated against monitored data from two case study dwellings, located near to Loughborough in the UK, during the 2013 heatwave. Input variables were selected using backward stepwise regressions based on minimisation of the Akaike Information Criterion (AIC) and Mean Absolute Error (MAE), for the ARX and GAM models respectively. Comparison of the models showed that whilst GAMs are capable of improving the forecasting accuracy, the improvements are significant only up to 3-6 hours ahead. During heatwaves and over longer forecasting horizons, GAMs were found to be less reliable and accurate than ARX models. The marginal improvement in forecasting accuracy at shorter horizons did not justify the additional computational time and risk of instability associated with more complex GAMs, at longer forecasting horizons. Whilst, logistic GAMs were shown to adequately predict the window opening state, incorporating knowledge of the window state did not significantly improve the accuracy of the indoor temperature predictions

Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector

Scarcity of affordable energy efficient dwellings is a defining characteristic of the global housing crisis. In many countries this problem has been exacerbated by single objective cost-models which favour the homogeneous development of market tenures at the expense of delivering high-quality affordable homes. Despite the obvious environmental and fuel-poverty alleviation benefits of advanced energy performance standards, such as Passivhaus, they are often dismissed as an affordable housing solution due to elevated build-cost premiums. The present work attempts to reconcile this housing affordability – energy performance nexus by establishing a novel decision support framework for Passivhaus design using genetic multiobjective optimization. The use of constrained genetic algorithms coupled to the Passive House Planning Package software is shown to produce cost optimal designs which are fully compliant with the Passivhaus standard. The findings also reveal that the precise choice of Passivhaus certification criteria has significant impacts on overheating risks using future probabilistic climate data. This means that the design implications of using either the peak heating load or annual heating demand certification criteria must be temporally evaluated to ensure resilient whole-life design outcomes. In a typical UK context, the findings show that affordable Passivhaus dwelling construction costs can be reduced by up to £366/m2 (or 22% of build cost). Use of this evidence-based decision support tool could thereby enable local authorities and developers to make better-informed decisions in relation to cost optimal trade-offs between achieving advanced energy performance standards and the viability of large affordable housing developments

Should current indoor environment and air quality standards be doing more to protect young people in educational buildings?

Indoor environmental quality (IEQ) and indoor air quality (IAQ) were assessed in a recently refurbished educational building at Loughborough University, through a monitoring campaign in accordance with Building Bulletin (BB) 101. A particular focus of this work was on emissions from building materials. Volatile organic compounds (VOCs) were measured using diffusive (passive) methods involving Thermal Desorption (TD), Gas Chromatography (GC) and Mass Spectrometry (MS) techniques. The results show that although the building performs satisfactorily with respect to guidelines for overheating and ventilation performance according to BB101 (2018) the current guidelines only assess Total Volatile Organic Compound (TVOC) limits which fail to identify the source of IAQ problems. The presence of numerous VOCs indicates that quantification of individual compounds is necessary to assess long-term health risks