• Energy Research

Abstract Building-integrated photovoltaic (BIPV) panels are emerging as a useful technology for helping to achieve net-zero energy buildings. At this time, the main drawback with BIPV systems is the cost per kilowatt per hour of electricity generated. Besides cheaper production of photovoltaic panels, increases in their efficiency can be obtained by reducing panel temperatures. This is often achieved by adding a cavity beneath the panels to allow ventilation of the rear of the panel. However, the details of airflow in the cavity and the effect on cooling have not been rigorously researched. Life-time enhancement against degradation is also an effective technique to reduce the cost of electricity generated. Moisture ingress and thermal stresses are among the primary reasons for degradation of BIPVs; these processes are directly affected by air and moisture flow around the panels. The surface temperature thermography and airflow observations performed in this work helps to understand the transport mechanisms above and below the panels. For this purpose, a novel setup was developed consisting of a building model with a mock BIPV panel plus a solar simulator placed inside an atmospheric wind tunnel. Particle image velocimetry (PIV) and infra-red thermography were performed to simultaneously monitor the surface temperature and airflow above and below the panel. The study clearly shows how the accelerated airflow within the cavity increases the heat exchange between the PV and airflow and consequently reduces the PV temperature. It is also shown that the stepped open arrangement of panels is more effective in reducing the temperature comparing to a flat arrangement. This arrangement also has a better resistant against the air and moisture ingress.

Housing Stock Energy Models (HSEMs) play a determinant role in the study of strategies to decarbonise the UK housing stock. Over the past three decades, a range of national HSEMs have been developed and deployed to estimate the energy demand of the 27 million dwellings that comprise the UK housing stock. However, despite ongoing improvements in the fidelity of both modelling strategies and calibration data, their longevity, usability and reliability have been compromised by a lack of modularity and openness in the underlying algorithms and calibration data sets. To address these shortfalls, a new open and modular platform for the dynamic simulation of national (in the first instance, the UK) housing stocks has been developed—the Energy Hub (EnHub). This paper describes EnHub’s architecture, its underlying rationale, the datasets it employs, its current scope, examples of its application, and plans for its further development. In this we pay particular attention to the systematic identification of housing archetypes and their corresponding attributes to represent the stock. The scenarios we analyse in our initial applications of EnHub, based on these archetypes, focus on improvements to housing fabric, the efficiency of lights and appliances and of the related behavioural practices of their users. In this we consider a perfect uptake scenario and a conditional (partial) uptake scenario. Results from the disaggregation of energy use throughout the stock for the baseline case and for our scenarios indicate that improvements to solid wall and loft thermal performance are particularly effective, as are reductions in infiltration. Improvements in lights and appliances and reductions in the intensity of their use are largely counteracted by increases in heating demand. Housing archetypes that offer the greatest potential savings are apartments and detached dwellings, owing to their relatively high surface area to volume ratio; in particular for pre-1919 and inter-war epochs.

The UK housing stock is responsible for some 27% of national energy demand and associated carbon dioxide emissions. 80% of this energy demand is due to heating (60%) and domestic hot water (20%), the former reflecting the poor average thermal integrity of the envelope of the homes comprising this stock. To support the formulation of policies and strategies to decarbonise the UK housing stock, a large number of increasingly sophisticated Housing Stock En- ergy Models (HSEMs) have been developed throughout the past 25 years. After describing the sources of data and the spatio-temporal granularity with which these data are available to represent this stock, as well as the physical and social phenomena that are modelled and the range of strategies employed to do so, this paper evaluates the 29 HSEMs that have been developed and deployed in the UK. In this we consider the models’ predictive accuracy, predictive sensitivity to design parameters, versatility, computational e ciency, the reproducibility of predictions and software usability as well as the models’ transparency (how open they are) and modularity. We also discuss their comprehensiveness. From this evaluation, we conclude that current HSEMs are lacking in transparency and modularity, they are limited in their scope and employ simplistic models that limit their utility; in particular, relating to the modelling of heat flow and in the modelling of household behaviours relating to investment decisions and energy using practices. There is a need for an open-source and modular dynamic housing stock energy modelling platform that addresses current limitations, can be readily updated as new (e.g. housing survey) calibration data is released and be readily extended by the modelling community at large: improving upon the utilisation of scarce developmental resources. This would represent a consid- erable step forward in the formulation of housing stock decarbonisation policy that is informed by sound evidence.

Land use and land cover (LULC) alteration has changed original energy balance and heat fluxes between land and atmosphere, and thus affects the structure characteristics of temperature and humidity fields over urban heterogeneous surfaces in different spatio-temporal scales. Lanzhou is the most typical river valley city of China, it is chosen as the case study. Typical river valley terrain, rapid urbanization and severe air pollution have caused unique urban climate and urban heat island (UHI) effects in Lanzhou. Firstly, the spatial structure characteristics and dynamic evolution of temperature and humidity fields in autumn are simulated by mobile measurement experiment and GIS spatial analysis method. The results show that temperature and humidity fields have significant dynamic change within a day, and have multiple center and multiple intensity level characteristics. Then, LULC and normalized difference vegetation index (NDVI) are extracted from remote sensing images, the distribution patterns of temperature and humidity fields have close relationships with LULC and NDVI. Moreover, there is a significant positive correlation between impervious surface area and thermal field intensity. A positive correlation between NDVI value and humidity field intensity has been found as well as a negative correlation between NDVI value and thermal field intensity. Finally, heat fluxes and energy balance characteristics between ground and atmosphere are analyzed based on the Bowen-ratio System experiments. This study could provide theoretical support and practical guidance for urban planning, urban eco-environment construction and air pollution prevention of river valley city.

Airflow around building-integrated photovoltaics (BIPV) has a significant impact on their hygrothermal behavior and degradation. The potential of reducing the temperature of BIPV using an underneath cavity is experimentally and numerically investigated in literature. Most of the models are oversimplified in terms of modeling the impact of 3D flow over/underneath of PV modules, which can result in a non-uniform surface temperature and consequently a non-homogenous thermal degradation. Moreover, the simultaneous presence of radiation and convection related to upstream wind, in addition to the combined impact of back-ventilation and surface convection, is barely addressed in literature. However, these simplifications can result in the unrealistic loading climate conditions. This paper aims to present a unique experimental setup to provide more realistic climate conditions for investigating the ventilation potential of the underneath. The setup consists of a solar simulator and a building prototype with installed PV, placed inside an atmospheric wind tunnel to control upstream wind velocity. Thermography is performed using an infrared camera to monitor the surface temperature of the BIPV. The potential of an underneath cavity with various cavity heights and PV arrangement is further investigated in this paper. The outcome would be eventually useful in the development of practical guidelines for BIPV installation. Copyright © 2013 John Wiley & Sons, Ltd.

Abstract Despite significant progresses in development of energy-efficient buildings (EEBs), energy demand in building sector is still drastically increasing. This paradox is conceptualized in this study as Inefficiency of Increased Building Energy Efficiency. Marketability failure of EEBs and inefficiency in integrated design approach are the main causes of this paradox. Compared to merely focusing on the energy-efficiency enhancement, increasing the number of EEBs with a better marketability via enhancement of their aesthetic features is proposed as a novel approach for energy demand reduction in the building sector. This article aims to first investigate the current stage of EEBs’ adoption and the associated market barriers, and then to propose a multidisciplinary design approach to scrutinize the role of aesthetic features on buildings’ marketability for development of effective policies. Conducted comprehensive survey among real-estate agencies across 26 UK cities reveals a negative correlation between the energy-efficiency and housing marketability. Moreover, house price and aesthetic features are understood as the most dominant parameters that impact individuals' buying decision. Any extra initial cost of EEBs over 3% is likely to face market resistance. Furthermore, strong empirical evidences have been found to confirm that the proposed approach has a substantial potential to increase the EEBs’ number.

Building-integrated photovoltaic (BIPV) panels are generally expected to operate for over 25 years to be viewed as an economically viable technology. Overheating is known to be one of the major deficiencies in reaching the targeted lifespan goals. Alongside the thermal degradation, the operational efficiency of the silicon-based solar panel drops when the surface temperature exceeds certain thresholds close to 25 °C. Wind-driven cooling, therefore, is widely recommended to decrease the surface temperature of PV panels using cavity cooling through their rear surfaces. Wind-driven flow can predominantly contribute to cavity cooling if a suitable design for the installation of the BIPV systems is considered.In general, various correlations in the form of Nu=CReaNu=CRea are adapted from heat convection of flat-plates to calculate the heat removal from the BIPV surfaces. However, these correlations demonstrate a high discrepancy with realistic conditions due to a more complex flow around BIPVs in comparison with the flat-plate scenarios. This study offers a significantly more reliable correlation using computational fluid dynamics (CFD) technique to visualize and thus investigate the flow characteristics around and beneath BIPVs. The CFD model is comprehensively validated against a particle velocimetry and a thermography study by Mirzaei et al. (2014) and Mirzaei and Carmeliet (2013b). The velocity field shows a very good agreement with the experimental results while the average surface temperature has a 6.0 % discrepancy in comparison with the thermography study. Unlike the former correlations, the coefficients are not constant numbers, but a function of the airflow velocity, in the newly proposed correlation, which is in the form of View the MathML sourceNuL=0.1513ReL0.7065.

Coupled models developed from the building energy simulation (BES) and computational fluid dynamics (CFD) methods are occasionally used for analyzing the buildings thermal performance. Nevertheless, the large uncertainty in the input parameters of BES models and values of the closure coefficients of Reynolds-averaged Navier-Stokes (RANS) turbulence models restrict the accuracy of coupled BES-CFD models for thermal performance prediction in highly dense urban areas.Thus, a systematic framework for improving the accuracy of the coupled BES-CFD models is proposed in this study, consisting of an approximation technique and a stochastic optimization approach. In this framework, at first, a CFD model is improved with a closure coefficients optimization procedure using experimental data. In the second step, the improved CFD model is utilized to conduct a series of CFD simulations for real-geometry buildings in order to calibrate the BES model with the alteration of parameters such as the adaptive discharge coefficient, local wind profile, and convective heat transfer coefficients over the building façades.The developed framework is then applied to a small cross-ventilated office building surrounded by neighboring buildings. Deviations up to 60% are found in the prediction of the energy saving potential of cross-ventilation strategy by the default and calibrated BES models.