Creating sustainable cities one building at a time: towards an integrated urban design framework

One of the tenets of urban sustainability is that more compact urban forms that are more densely occupied are more efficient in their overall use of space and of energy. In many designs this has been translates into high-rise buildings with a focus on energy management at their outer envelopes. However, pursuing this building focused approach alone means that buildings are treated as stand-alone entities with minimal consideration to their impact on the surrounding urban landscape and vice versa. Where urban density is high, individual buildings interact with each other, reducing access to sunshine and daylight, obstructing airflow and raising outdoor air temperature. If/when each building pursues its own sustainability agenda without regard to its urban context, the result will diminish the natural energy resources available to nearby buildings and worsen the outdoor environment generally. This paper examines some of these urban impacts using examples from the City of London where rapid transformation is taking place as very tall buildings with exceptional energy credentials are being inserted into a low-rise city without a plan for the overall impact of urban form. The focus of the paper is on access to sunshine and wind and the wider implications of sustainable strategies that that focuses on individual buildings to the exclusion of the surrounding urban landscape. The work highlights the need for a framework that accounts for the synergistic outcomes that result from the mutual interactions of buildings in urban spaces

A Sociotechnical Perspective on Winter Window Opening and Heating Controls in Purpose-Built Student Accommodation

The auto-generation of UK school building stock models could facilitate non-domestic carbon emissions tracking. However, contextual fabric and building service data are required to differentiate between asset or operational performance, and these may only be available in situ from building users. Engaging such groups through proposed data crowdsourcing would require robust feedback and data gathering mechanisms to be developed to overcome motivational and informational barriers. There has been significant investment in purpose-built student accommodation (PBSA) across the UK. A case study research design was used to investigate the in-use performance of two recently built PBSA developments by monitoring indoor environmental quality, radiator use, and window opening, alongside semi-structured interviews with the building’s residents. The results showed that during the heating season the study participants typically controlled the conditions in their bedrooms by opening their windows regularly, often for long periods, and frequently whilst the heating was on. Five behavioural causes of consistent winter window opening were identified. These were to prevent overheating, inadequate ventilation, poor understanding of the controls, lack of responsiveness of the heating system, and lack of financial implications. Important lessons for the future design of PBSA are identified

Balancing accuracy and computation burden - an evaluation of different sensitivity analysis methods for urban scale building energy models

Urban-scale building energy models capitalise on the increasing accessibility of large-scale urban data sets and allow the rapid evaluation of competing policy options, making them a vital tool for urban responses to the climate emergency. However, the vast number of different inputs required to model a complex urban environment makes it impossible to precisely quantify all inputs and the complex energy flows within models must be simplified to achieve tractable solutions, as a result, the outputs of these models inevitably have a significant range of variation. Without understanding these limits of inference resulting policy advice is inherently defective. Uncertainty Analysis (UA) and Sensitivity Analysis (SA) offer essential tools to determine the limits of inference of a model and explore the factors which have the most effect on the model outputs. Despite a wellestablished body of work applying UA and SA to models of individual buildings, very limited work has been done to apply these tools to urban scale models. This study presents a systematic comparison of three different sensitivity analysis methods for a high resolution, dynamic thermal simulation at the neighbourhood scale. Accuracy, processing time and complexity of application of each method is evaluated to provide guidance which can inform the application of these methods to other urban and large-scale building energy models. The results highlight the importance of considering both model form and input parameter scale when selecting an appropriate method. In this case, the elementary effects method (EER) offers good performance at relatively low simulation cost

Heating, ventilating and air-conditioning system energy demand coupling with building loads for office buildings.

The UK building stock accounts for about half of all energy consumed in the UK. A large portion of the energy is consumed by nondomestic buildings. Offices and retail are the most energy intensive typologies within the nondomestic building sector, typically accounting for over 50% of the nondomestic buildings’ total energy consumption. Heating, ventilating and air conditioning (HVAC) systems are the largest energy end use in the nondomestic sector, with energy consumption close to 50% of total energy consumption. Different HVAC systems have different energy requirements when responding to the same building heating and cooling demands. On the other hand, building heating and cooling demands depend on various parameters such as building fabrics, glazing ratio, building form, occupancy pattern, and many others. HVAC system energy requirements and building energy demands can be determined by mathematical modelling. A widely accepted approach among building professionals is to use building energy simulation tools such as EnergyPlus, IES, DOE2, etc. which can analyse in detail building energy consumption. However, preparing and running simulations in such tools is usually very complicated, time consuming and costly. Their complexity has been identified as the biggest obstacle. Adequate alternatives to complex building energy simulation tools are regression models which can provide results in an easier and faster way. This research deals with the development of regression models that enable the selection of HVAC systems for office buildings. In addition, the models are able to predict annual heating, cooling and auxiliary energy requirements of different HVAC systems as a function of office building heating and cooling demands. For the first part of the data set development used for the regression analysis, a data set of office building simulation archetypes was developed. The four most typical built forms (open plan sidelit, cellular sidelit, artificially lit open plan and composite sidelit cellular around artificially lit open plan built form) were coupled with five types of building fabric and three levels of glazing ratio. Furthermore, two measures of reducing solar heat gains were considered as well as implementation of daylight control. Also, building orientation was included in the analysis. In total 3840 different office buildings were then further coupled with five different HVAC systems: variable air volume system; constant air volume system; fan coil system with dedicated air; chilled ceiling system with embedded pipes, dedicated air and radiator heating; and chilled ceiling system with exposed aluminium panels, dedicated air and radiator heating. The total number of models simulated in EnergyPlus, in order to develop the input database for regression analysis, was 23,040. The results clearly indicate that it is possible to form a reliable judgement about each different HVAC system’s heating, cooling and auxiliary energy requirements based only on office building heating and cooling demands. High coefficients of determination of the proposed regression models show that HVAC system requirements can be predicted with high accuracy. The lowest coefficient of determination among cooling regression models was 0.94 in the case of the CAV system. HVAC system heating energy requirement regression models had a coefficient of determination above 0.96. The auxiliary energy requirement models had a coefficient of determination above 0.95, except in the case of chilled ceiling systems where the coefficient of determination was around 0.87. This research demonstrates that simplified regression models can be used to provide design decisions for the office building HVAC systems studied. Such models allow more rapid determination of HVAC systems energy requirements without the need for time-consuming (hence expensive) reconfigurations and runs of the simulation program.This work was funded by EU-FP6 Marie Curie Actions - Marie Curie Research Training Networks. Contract Number: MRTN-CT-2006-033489 Project: CITYNET – Development of an integrated urban management too

Good neighbours

The energy impact of tall buildings on neighbourhoods should be taken into account when evaluating their carbon emissions, say Julie Futcher, Gerald Mills and Ivan Korolij

Modelling of District Heating Systems: Comparative Evaluation of White-box Modelling Approaches

District heating systems are prevalent in most European countries, and such energy delivery methods can be crucial to decarbonisation objectives. To appropriately size and design the control of such networks, the modelling of district heating networks should have a good representation of the demand-side, which is the set of buildings connected to the network. In-stead of simplified modelling of the demand, whole-building simulation tools can be invoked in this case, like EnergyPlus. More recently, equation-based libraries have been developed in Mod-elica for component-based simulation of HVAC systems. Modelica-based libraries offer easier model composability and are particularly interesting for control fine-tuning; on the downside, the model setup can be more complex, with more validation needed. This paper conducts a comparative study of the Modelica LBNL Buildings library against Ener-gyPlus, based on an archetype-based hypothetical case in the UK with a small-scale district heating system. The methodology resides on models developed in the two tools with the same level of modelling detail. The comparison helps understand software differences in the model-ling procedure, computational time, relative accuracy of energy predictions and heating system variables. The results indicate Modelica Buildings library yields similar accuracy in terms of heat transfer calculation through thermal zones as EnergyPlus, whilst capturing additional en-ergy consumption caused by the dynamic changes at system startup and the realistic controllers used in the Modelica district heating models. Meanwhile, the Modelica Buildings library’s out-puts show the fluctuations of system variables, indicating different operation patterns and con-trol effects against EnergyPlus. This study also proves that the Modelica Buildings library is the better tool for district heating simulation in the context of dynamic performance evaluation and control testing, based on overall capabilities, limitations, and prediction differences

Opportunities for Communal Photovoltaic-Thermal Heating Systems with Storage

With 70% of the world’s population projected to live in urban areas by 2060 (ARUP, 2016) and 67% of current energy related emissions produced in urban areas (IEA, 2008), there is a compelling case to investigate the reduction of CO2 emissions from heating in densely populated areas in the UK or other regions with heating requirements. This paper investigates how a single PVT panel system with thermal and electrical storage could reduce heating emissions for a row of terraced houses. The main findings of the study carried out in Dymola/Modelica were that there is potential for greater thermal and electrical output with larger PVT systems and shared communal electrical and thermal storage. Preheating the mains water which supplies the hot water tank using the PVT both increased the PVT thermal efficiency and utilisation, and the electrical efficiency. In this configuration, the PVT system was able to supply all hot water demands of a row of terraced houses and supply about 91% of electricity demands

Extending building simulation software to include the organic Rankine cycle for factory waste heat recovery

Generators based on the organic Rankine cycle (ORC) are used in some industries to generate electricity from waste heat. The supply of heat is rarely constant since it is linked to the operation of processes whose energy use is determined by the manufacturing schedule. The performance of the ORC depends on many factors including the working fluid, the choice of condenser type and whether or not to use a recuperator. The performance of the condenser is influenced by the climate and therefore the location of the factory. This paper describes an extension of the functions of a commercial building energy modelling software IES to include ORC simulation. Some of the features of IES such as the modelling of energy profiles, the ability to input weather data and the modelling of typical energy system components make it well suited to this task. The model of a typical ORC system includes the evaporator heat exchanger with its thermal oil pump, the condenser with its pumps and fans and the option of a recuperator, as well as the ORC device itself. As well as selecting the configuration of the ORC system, the software user is able to choose from a wide range of working fluids. The auxiliary energy used by the pumps and fans is modelled since this can significantly offset the electricity generated by the ORC and therefore impact the cost benefits. The user may select an air-cooled or water-cooled condenser, and the psychrometric behaviour of the cooling tower is modelled so that the impact of location on annual performance can be analysed. The use of the software is illustrated by its application to the waste heat from an iron foundry, which is typical of industries with significant waste heat

Impact of Traditional Augmentation Methods on Window State Detection

Window state information and changes can help understand ventilation patterns or be used as input in energy models. State identification can be achieved by capturing time-lapse images and processing these through a deep learning model. Deep learning methods have shown reliable performance in object detection tasks such as window and door detection, but have not been applied for window states detection. One of the challenges in setting up such models is to collect a large number of images of window states. In this case, image augmentation can be a critical pre-processing step to enhance the training dataset artificially. Image augmentation has been beneficial in similar contexts and applications. This paper investigates image augmentation methods, adjusting for brightness, scale, and weather. Windows images were used as the starting dataset to demonstrate the proposed methods, and augmented images were artificially generated from the original images. Using the expanded dataset, the Faster R-CNN (faster region-based convolutional neural network) trained a model to detect the binary window states. The augmented dataset model showed better performance than when the original dataset was used. The findings are a testament to the utility of image augmentation methods in the training model of window states detection using deep learning methods

The impact of climate change on cognitive performance of children in English school stock: A simulation study

Children in England spend around 30% of their time in schools to gain knowledge and skills. Climate change could impact schools' thermal environments and children's learning performance by impairing their cognitive ability. This study presents an evaluation approach to investigating and quantifying climate change's impact on the cognitive performance of children across English school stocks. The study also evaluates the potential of possible strategies for mitigating the impacts of climate change. The results show that future climates are projected to increase cognitive performance loss of children in school archetypes representative of school stocks, with variations based on regional climate characteristics. Increasing ventilation rates proves to be an effective means of reducing cognitive performance loss, while its effectiveness diminishes as outdoor temperatures rise in the future. Thus, the introduction of air conditioning becomes a potentially more beneficial strategy, despite the associated increase in cooling energy demand. Moreover, higher ventilation rates in air-conditioned classrooms can further improve children's cognitive performance. The use of cognitive performance loss as a Key Performance Indicator (KPI) allows for better communication and understanding of climate change risks faced by schools among building and non-building experts. The proposed evaluation approach remains adjustable and can be continuously updated and enhanced as new insights from psychological research emerge