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Supplemental material, sj-pdf-1-epb-10.1177_2399808320914313 for Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation by Sameh Zakhary, Julian Rosser, Peer-Olaf Siebers, Yong Mao and Darren Robinson in EPB: Urban Analytics and City Science

Supplemental material, sj-pdf-1-epb-10.1177_2399808320914313 for Using unsupervised learning to partition 3D city scenes for distributed building energy microsimulation by Sameh Zakhary, Julian Rosser, Peer-Olaf Siebers, Yong Mao and Darren Robinson in EPB: Urban Analytics and City Science

This is the first book to directly address the physics of urban sustainability, and how urban sustainability may be modelled and optimized. Starting with an introduction to the importance and key aspects of the topic, it moves on to a detailed consideration of the urban climate and pedestrian comfort. Comprehensive techniques for the modelling and optimization of urban metabolism are then described, together with means for defining sustainability as the fitness function to be optimized. It ends with an eye to the future of sustainable urban design and the means available to urban designers and governors to help them to secure a more sustainable urban future. This book will be invaluable both in informing the next generation of urban planners, architects and engineers, and as a tool for current professionals that will directly contribute to the effectiveness of their work by allowing them to more successfully measure and model urban sustainability.

Around three quarters of global resource consumption takes place in urban settlements, with corresponding adverse environmental consequences. According to population forecasts this situation will be exacerbated during the coming years. It is therefore imperative that we understand how to design less resource intensive urban settlements. To this end we have developed a new methodology for optimising the layout and form of buildings for their exploitation of available solar radiation, using an evolutionary algorithm. In this way a considerably more exhaustive search of the parameter space is facilitated than what is possible with manual trial and error which has been favoured in past studies.

Based on almost 18 months of continuous direct measurements coupled with an electronic questionnaire, we have analysed the influence of indoor temperature and illuminance, outdoor climate parameters, thermal and visual sensation, clothing and season on the perceived productivity of office occupants. Using linear regression analysis, we have identified which of these variables have a significant influence on perceived productivity. From this we noticed that the observed variance in productivity is best expressed by either extreme or aggregated values of our driving variables (including indoor and outdoor temperature or thermal sensation), rather than by instantaneous stimuli. Our analysis also suggests that under warm conditions perceived productivity decreases by around of 10% compared to relatively cooler conditions. An assessment of the existence of a conjugate impact of these variables is also provided.

Using extensive field survey data acquired over the past seven years at the Solar Energy and Building Physics Laboratory at EPFL in Switzerland, models of occupants’ presence, opening and closing of windows and the raising and lowering of blinds have been developed. These new models, together with existing models of lighting and appliance use, have been integrated within a new urban energy modelling tool, called CitySim. In this paper we describe briefly the structure of CitySim together with the means for representing occupants’ presence and behaviour; both deterministic and stochastic. For a hypothetical scenario we then go on to present simulations of the impact that occupants’ behaviour may have on the indoor environment in buildings’ as well as on buildings’ energy demands at the urban scale. We close this paper by discussing a more general framework for the more comprehensive representation of occupants and their activities in building and urban simulation tools.

Based on almost seven years of continuous measurements we have analysed in detail the influence of occupancy patterns, indoor temperature and outdoor climate parameters (temperature, wind speed and direction, relative humidity and rainfall) on window opening and closing behaviour. This paper presents the development and testing of several modelling approaches, including logistic probability distributions, Markov chains and continuous-time random processes. Based on detailed statistical analysis and cross-validation of each variant, we propose a hybrid of these techniques which models stochastic usage behaviour in a comprehensive and efficient way. We conclude by describing an algorithm for implementing this model in dynamic building simulation tools.

Computer modelling at the urban scale is an increasingly vibrant area of research activity which aims to support designers to optimise the performance of new and existing urban developments. But the parameter space of an urban development is infinitely large, so that the probability of identifying an optimal configuration of urban design variables with say energy minimisation as a goal function is correspondingly small. To resolve this we have coupled a micro-simulation model of urban energy flows CitySim with a new evolutionary algorithm (EA): a hybrid of the CMA-ES and HDE algorithms. In this paper we present the means of coupling the EA and CitySim and identify a subset of urban design variables that have been parameterised. We then present results from application of this new methodology to minimise the energy demand of part of a case-study district in the city of Basel, Switzerland. The papers closes by discussing work that is planned to further increase the scope of this new methodology for optimising urban sustainability.

In this paper we describe new software “CitySim” that has been conceived to support the more sustainable planning of urban settlements. This first version focuses on simulating buildings’ energy flows, but work is also under way to model energy embodied in materials as well as the flows of water and waste and inter-relationships between these flows; likewise their dependence on the urban climate. We discuss this as well as progress that has been made to optimise urban resource flows using evolutionary algorithms. But this is only part of the picture. It is also important to take into consideration the transportation of goods and people between buildings. To this end we also discuss work that is underway to couple CitySim with a micro-simulation model of urban transportation: MATSim.

One of the key sources of uncertainty in building simulation relates to the presence and behaviour of occupants. Fortunately, several academic groups have been working to address this uncertainty, particularly in relation to the use of windows and the corresponding impact on building heat and mass transfers. But these models tend to be based on highly restrictive datasets, typically relating to one building, so that their application to other building situations may be uncertain. The model of Haldi and Robinson (2009) is a case in point. It has been rigorously validated, but only using the original training dataset. In this paper we evaluate the applicability of the model to other circumstances. We do this is two steps. Firstly we perform a blind comparison of the model which is calibrated using data acquired from an office building in Switzerland to predict the behaviour of occupants of an office building in Austria. We then use part of the Austrian dataset to derive new calibration parameters for the model and perform a second blind comparison with the remainder of the Austrian dataset. We present the results from these two blinds comparisons to evaluate the robustness of applications of stochastic models beyond their original training datasets.

The radiant external environment may be described by two hemispheres, above and below the horizontal plane, which are discretised into patches of known solid angle. Occlusions to these patches may be combined and represented as some patch fraction for which the radiant characteristics are defined by the dominant occlusion. By solving for radiant exchanges between each surface in a scene and its associated (un)occluded patches, we have a simplified radiosity algorithm (SRA). This paper describes the application of t his SRA to solve for predictions of (i) solar radiation, (ii) interiordaylight and (iii) longwave radiation. Comparisons with a ray tracing program show that accurate results are achieved at a computational cost several orders of magnitude lower.

Real cities are comprised of a diverse, random arrangement of building positions, shapes, and sizes. Yet most of the urban parameterisations thus far developed share the assumption that a city is made up of either a regular array of parallelepipeds or infinitely long canopies. The inputs to these models, which include street width, building width, building density and a statistical representation of building heights, are generally obtained through quantitative field surveys (which are very slow and time consuming to perform) or qualitative estimates from Digital Elevation Model. But in performing this geometric abstraction there is no way to ensure that the total built surfaces and volumes of the simplified geometry match those of the actual city, or more importantly, that the energy and momentum exchanges are equivalent. In this paper, we aim to test the central hypothesis that cities can be accurately represented by a regular array of parallelepipeds or canopies. For this, we investigate, for a particular scenario, the effects of complexity in urban geometry on the spatially averaged drag forces and shortwave radiation exchange. For drag computation, we used the Immersed Surface Technique, while for computing the incident radiation we used the Simplified Radiosity Algorithm. After testing the above hypothesis, we propose a new approach for fitting an array of cubes to any complex (realistic) geometry, so that new or existing urban parameterisation schemes can be used with confidence. Copyright. (C) 2010 Royal Meteorological Society

The consumption of resources within an urban neighbourhood depends on the behaviour of its occupants. To understand the randomness of this behaviour and its effect on the demand of resources calls for the development of a set of stochastic models which are discussed here. This includes models for presence and interactions with lights, blinds and electrical and water appliances as well as solid waste production. The theoretical approach of each model has been chosen based on a priori hypotheses. Data has been collected to check their consistency with reality and to further refine them. The purpose of these models is to offer a reliable prediction of occupant behaviour at the community scale to more accurately predict the thermal and electrical energy demands of buildings as well as the consumption of water and the production of liquid and solid waste resources; the latter potentially being fed back. The development of the stochastic models is part of project SUNtool [1], a project funded by the EU that, after its completion next year, will provide a software capable of simulating the resource flows of an urban neighbourhood entered by its user.

Climate Modelling is a complex task. One of the most important reasons is the presence of a large variety of spatio-temporal scales. There are climatic changes that take place over a time period of a few months and then there are gusts which might last only a few seconds. Similarly there can be a strong influence on the weather of a city due to the presence of a large water body like a sea or of a mountain having a dimension of a few tens or hundreds of kilometres and then there can be a local influence due to the presence of urban structures like buildings and canopies having dimensions of the order of a few meters only. It is not computationally tractable to handle all of these scales in a single climate model. However, we can solve this issue by an integration of a global, meso and micro scale model capable of handling each of the different scales of interest. In this paper we describe in detail one such approach, along with some sample results demonstrating the capabilities of this tool.

Urban morphology is one of the main parameters influencing directly and indirectly buildings’ energy needs. Despite an increasing number of urban energy and environmental modelling tools addressing these issues, the complexity of physical relationships at this level often constrain urban energy modelers to simplify the problems by considering only parts of the phenomena, thus leading to diverging findings and recommendations related to the relevance of urban morphology. A systematic review of published research works in the field of urban energy and environmental modelling is performed. This involves characterising the research approach, evaluating the physical effects taken into consideration in the models applied, the types of models applied, identifying whether the effect of urban morphology is isolated from other effects, whether the urban scene considered is real or theoretical and parametrised and recognising the performance indicators used for assessment. Last but not least, the type of result and the robustness of the ensuing recommendations in terms of sustainable urban design are critically evaluated according to clearly defined assessment criteria. The main findings related to the impact of urban morphology on energy needs in the built environment are summarised and put in relation to the physical effects taken into consideration, showing that there is no common basis allowing for a generalisation of the knowledge available. Beyond an attempt to cluster the different approaches, the authors conclude that there is a clear need to further develop more comprehensive tools, but also to propose a minimal set of requirements for computational modelling activities in the urban energy field.

Based on six years of continuous measurements, we have analysed in detail the occupancy, thermal and visual parameters influencing blind usage behaviour. This paper begins by presenting some of the key findings from these analyses. Informed by other developments in the literature, we go on to propose an approach for a comprehensive stochastic model for simulating blind usage.

In this paper we briefly describe three alternative techniques for modelling solar radiation in the urban context. These include the use of: (i) simple morphological parameters as indicators of radiation availability, (ii) a ray-tracing program in conjunction with a cumulative sky radiance distribution model for accurate calculation of annual incident solar irradiation, (iii) a simplified radiosity algorithm (SRA) for efficient calculation of (sub-)hourly incident solar irradiance. Although each of these techniques has advantages, we suggest that the SRA should be used in preference to the two alternatives, either for reasons of accuracy or efficiency