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Journal articles on the topic 'Urban Energy Modelling (UEM)'

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Author: Grafiati

Published: 14 March 2023

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1

Bukovszki, Viktor, Ábel Magyari, Marina Kristina Braun, Kitti Párdi, and András Reith. "Energy Modelling as a Trigger for Energy Communities: A Joint Socio-Technical Perspective." Energies 13, no. 9 (May 5, 2020): 2274. http://dx.doi.org/10.3390/en13092274.

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Mainstreaming energy communities has been one of the main challenges in the low-carbon transition of cities. In this sense, urban building energy modelling (UBEM) has an untapped role in enabling energy communities, as simulations on urban models provide evidence-based decision support to reduce risks, engage, motivate and guide actors, assert wider policy goals and regulatory requirements. This accelerating role and the potential of UBEM is not sufficiently understood, as research into energy community focuses on its barriers and impacts, while the research of UBEM is mainly technologically oriented. This review takes a sociotechnical approach to explore whether UBEM is a technological trigger for energy communities, furthering the conceptual framework of transition management. factors influencing energy community progression in different use-cases and stages of their lifecycle are compiled to assess the affordances of distinct capabilities of prevalent UBEM tools. The study provides a guide for energy community planners to UBEM. It matches different tool capabilities to the various stages of the project lifecycle for the different use-cases, equipping them with the means to accelerate the low-carbon transition of cities from the bottom-up. Finally, the study defines a development trajectory oriented towards application in urban sustainability to a rather new UBEM field.

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2

Zygmunt, Marcin, and Dariusz Gawin. "Application of the Renewable Energy Sources at District Scale—A Case Study of the Suburban Area." Energies 15, no. 2 (January 10, 2022): 473. http://dx.doi.org/10.3390/en15020473.

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The protection of the natural environment and countering global warming are crucial worldwide issues. The residential sector has a significant impact on overall energy consumption and associated greenhouse gas emissions. Therefore, it is extremely important to focus on all of the activities that can result in more energy efficient and sustainable city scale areas, preventing global warming. The highest improvement in the energy efficiency of existing buildings is possible by combining their deep refurbishment and the use of renewable energy sources (RES), where solar energy appears to be the best for application in buildings. Modernizations that provide full electrification seem to be a trend towards providing modern, energy efficient and environmentally friendly, smart buildings. Moreover, switching from an analysis at the single building level to the district scale allows us to develop more sustainable neighborhoods, following the urban energy modelling (UEM) paradigm. Then, it is possible to use the energy cluster (EC) concept, focusing on energy-, environmental- and economic-related aspects of an examined region. In this paper, an actual Polish suburban district is examined using the home-developed TEAC software. The software is briefly described and compared with other computer codes applied for UEM. In this study, the examined suburban area is modernized, assuming buildings’ deep retrofitting, the application of RES and energy storage systems, as well as usage of smart metering techniques. The proposed modernizations assumed full electrification of the cluster. Moreover, the examined scenarios show potential electricity savings up to approximately 60%, as well as GHG emission reduction by 90% on average. It is demonstrated that the proposed approach is a valid method to estimate various energy- and environment-related issues of modernization for actual residential clusters.

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3

Zygmunt, Marcin, and Dariusz Gawin. "Application of Artificial Neural Networks in the Urban Building Energy Modelling of Polish Residential Building Stock." Energies 14, no. 24 (December 9, 2021): 8285. http://dx.doi.org/10.3390/en14248285.

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The development of energy-efficient buildings and sustainable energy supply systems is an obligatory undertaking towards a more sustainable future. To protect the natural environment, the modernization of urban infrastructure is indisputably important, possible to achieve considering numerous buildings as a group, i.e., Building Energy Cluster (BEC). The urban planning process evaluates multiple complex criteria to select the most profitable scenario in terms of energy consumption, environmental protection, or financial profitability. Thus, Urban Building Energy Modelling (UBEM) is presently a popular approach applied for studies towards the development of sustainable cities. Today’s UBEM tools use various calculation methods and approaches, as well as include different assumptions and limitations. While there are several popular and valuable software for UBEM, there is still no such tool for analyses of the Polish residential stock. In this work an overview on the home-developed tool called TEAC, focusing on its’ mathematical model and use of Artificial Neural Networks (ANN). An exemplary application of the TEAC software is also presented.

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4

Nageler, P., A. Koch, F. Mauthner, I. Leusbrock, T. Mach, C. Hochenauer, and R. Heimrath. "Comparison of dynamic urban building energy models (UBEM): Sigmoid energy signature and physical modelling approach." Energy and Buildings 179 (November 2018): 333–43. http://dx.doi.org/10.1016/j.enbuild.2018.09.034.

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5

Faure, Xavier, Tim Johansson, and Oleksii Pasichnyi. "The Impact of Detail, Shadowing and Thermal Zoning Levels on Urban Building Energy Modelling (UBEM) on a District Scale." Energies 15, no. 4 (February 18, 2022): 1525. http://dx.doi.org/10.3390/en15041525.

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New modelling tools are required to accelerate the decarbonisation of the building sector. Urban building energy modelling (UBEM) has recently emerged as an attractive paradigm for analysing building energy performance at district and urban scales. The balance between the fidelity and accuracy of created UBEMs is known to be the cornerstone of the model’s applicability. This study aimed to analyse the impact of traditionally implicit modeller choices that can greatly affect the overall UBEM performance, namely, (1) the level of detail (LoD) of the buildings’ geometry; (2) thermal zoning; and (3) the surrounding shadowing environment. The analysis was conducted for two urban areas in Stockholm (Sweden) using MUBES—the newly developed UBEM. It is a bottom-up physics-based open-source tool based on Python and EnergyPlus, allowing for calibration and co-simulation. At the building scale, significant impact was detected for all three factors. At the district scale, smaller effects (<2%) were observed for the level of detail and thermal zoning. However, up to 10% difference may be due to the surrounding shadowing environment, so it is recommended that this is considered when using UBEMs even for district scale analyses. Hence, assumptions embedded in UBEMs and the scale of analysis make a difference.

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6

Tsirantonakis, Dimitris, and Nektarios Chrysoulakis. "Earth Observation Data Exploitation in Urban Surface Modelling: The Urban Energy Balance Response to a Suburban Park Development." Remote Sensing 14, no. 6 (March 18, 2022): 1473. http://dx.doi.org/10.3390/rs14061473.

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Cities are developing rapidly as an increasing percentage of the global population resides in urban areas. In the face of climate change, the sustainable development of cities is crucial for the well-being and safety of urban populations. The potential of planning interventions towards improving of urban resilience can be evaluated based on methodological approaches used in the domain of urban climate. In this study, we present how Earth Observation (EO) can be systematically used to evaluate urban planning interventions, based on Urban Surface Models (USM) simulations. More specifically, the impact of a suburban park development in Heraklion, Crete, was assessed based on simulations of the USM SUEWS (Surface Urban Energy and Water Balance Scheme), which was forced by EO data. Multi-source satellite data were analyzed to provide information on urban form, highlighting the importance of EO data in evaluating the environmental sustainability potential of urban planning interventions. The modifications caused by this planning intervention to surface energy fluxes were simulated. The scale (102 m) and the type (no-use vegetated area changed to recreational vegetated) of the intervention triggered minor responses in the Urban Energy Balance (UEB) at neighborhood scale, since the change of the relevant surface fluxes was not greater than 10 W m−2, on average, assuming no irrigation and no important changes in soil moisture. However, the planned substitution of grass and bare soil with paved surfaces and trees was found to increase the overall net change in heat storage, therefore contributing to the urban heat island development.

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7

Buckley, Niall, Gerald Mills, Samuel Letellier-Duchesne, and Khadija Benis. "Designing an Energy-Resilient Neighbourhood Using an Urban Building Energy Model." Energies 14, no. 15 (July 23, 2021): 4445. http://dx.doi.org/10.3390/en14154445.

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A climate resilient city, perforce, has an efficient and robust energy infrastructure that can harvest local energy resources and match energy sources and sinks that vary over space and time. This paper explores the use of an urban building energy model (UBEM) to examine the potential for creating a near-zero carbon neighbourhood in Dublin (Ireland) that is characterised by diverse land-uses and old and new building stock. UBEMs are a relatively new tool that allows the simulation of building energy demand across an urbanised landscape and can account for building layout, including the effects of overshadowing and the potential for facade retrofits and energy generation. In this research, a novel geographic database of buildings is created using archetypes, and the associated information on dimensions, fabric and energy systems is integrated into the Urban Modelling Interface (UMI). The model is used to simulate current and future energy demand based on climate change projections and to test scenarios that apply retrofits to the existing stock and that link proximate land-uses and land-covers. The latter allows a significant decoupling of the neighbourhood from an offsite electricity generation station with a high carbon output. The findings of this paper demonstrate that treating neighbourhoods as single energy entities rather than collections of individual sectors allows the development of bespoke carbon reducing scenarios that are geographically situated. The work shows the value of a neighbourhood-based approach to energy management using UBEMs.

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Bhavana, M., K. Gupta, and P. K. Pal. "URBAN MICRO CLIMATE MODELLING USING DIFFERENT URBAN PHYISCS SCHEMES AND HIGH RESOLUTION LULC WITH WRF MODEL." ISPRS - International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLII-5 (November 19, 2018): 491–98. http://dx.doi.org/10.5194/isprs-archives-xlii-5-491-2018.

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<p><strong>Abstract.</strong> Urban areas are treated as a single entity by mesoscale urban canopy models (UCM) for assessing the influence of urban morphology on climate. Weather Research and Forecasting Model (WRF) coupled with UCM along with urban physics options to describe the urban features such as Single Layer Urban Canopy Model (SLUCM), Building Energy Parameterization (BEP) and Building Energy Model (BEM) which enumerates the influence of urban features on the local scale other than the bulk parameterization (no urban physics option), which is generally used in most of the operational forecasting models. Besides, WRF model also enables to integrate multi-class Urban Land Use Land Cover (LULC) whereas most of the globally available LULC depict urban area as single urban built-up class. This study aims to analyze performance of high resolution urban LULC and urban physics options for Chandigarh area by downscaling climatic variables up to 1km and its validation with the ground observation data. The inner domain (1<span class="thinspace"></span>km resolution) was configured with default LULC for one set of simulations and multi-class urban LULC for other set of simulations. All the simulations were carried out for 3 days (August 19&ndash;21, 2017) due to computational restrictions by employing all the four urban physics options. It has been found that multi-class urban LULC yielded better results than single class urban built –up simulation when validated with respect to ground observation. The RMSE values for multi-class urban LULC provided less RMSE than single class urban LULC, those are in terms of temperature at 2<span class="thinspace"></span>m, relative humidity and wind speed are 0.91<span class="thinspace"></span>&deg;C, 2.63% and 1.82<span class="thinspace"></span>m/s respectively. Similarly, BEP+BEM urban physics option provided reduced RMSE values than the SLUCM and BEP scheme. The RMSE values in terms of temperature at 2<span class="thinspace"></span>m, relative humidity and wind speed are 1.11<span class="thinspace"></span>&deg;C, 4.39% and 2.62<span class="thinspace"></span>m/s respectively.</p>

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9

Malhotra, Avichal, Simon Raming, Jérôme Frisch, and Christoph van Treeck. "Open-Source Tool for Transforming CityGML Levels of Detail." Energies 14, no. 24 (December 8, 2021): 8250. http://dx.doi.org/10.3390/en14248250.

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Urban Building Energy Modelling (UBEM) requires adequate geometrical information to represent buildings in a 3D digital form. However, open data models usually lack essential information, such as building geometries, due to a lower granularity in available data. For heating demand simulations, this scarcity impacts the energy predictions and, thereby, questioning existing simulation workflows. In this paper, the authors present an open-source CityGML LoD Transformation (CityLDT) tool for upscaling or downscaling geometries of 3D spatial CityGML building models. With the current support of LoD0–2, this paper presents the adapted methodology and developed algorithms for transformations. Using the presented tool, the authors transform open CityGML datasets and conduct heating demand simulations in Modelica to validate the geometric processing of transformed building models.

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10

Buckley, Niall, Gerald Mills, Christoph Reinhart, and Zachary Michael Berzolla. "Using urban building energy modelling (UBEM) to support the new European Union’s Green Deal: Case study of Dublin Ireland." Energy and Buildings 247 (September 2021): 111115. http://dx.doi.org/10.1016/j.enbuild.2021.111115.

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11

Schoetter, Robert, Yu Ting Kwok, Cécile de Munck, Kevin Ka Lun Lau, Wai Kin Wong, and Valéry Masson. "Multi-layer coupling between SURFEX-TEB-v9.0 and Meso-NH-v5.3 for modelling the urban climate of high-rise cities." Geoscientific Model Development 13, no. 11 (November 18, 2020): 5609–43. http://dx.doi.org/10.5194/gmd-13-5609-2020.

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Abstract. Urban canopy models (UCMs) represent the exchange of momentum, heat, and moisture between cities and the atmosphere. Single-layer UCMs interact with the lowest atmospheric model level and are suited for low- to mid-rise cities, whereas multi-layer UCMs interact with multiple levels and can also be employed for high-rise cities. The present study describes the multi-layer coupling between the Town Energy Balance (TEB) UCM included in the Surface Externalisée (SURFEX) land surface model and the Meso-NH mesoscale atmospheric model. This is a step towards better high-resolution weather prediction for urban areas in the future and studies quantifying the impact of climate change adaptation measures in high-rise cities. The effect of the buildings on the wind is considered using a drag force and a production term in the prognostic equation for turbulent kinetic energy. The heat and moisture fluxes from the walls and the roofs to the atmosphere are released at the model levels intersecting these urban facets. No variety of building height at grid-point scale is considered to remain the consistency between the modification of the Meso-NH equations and the geometric assumptions of TEB. The multi-layer coupling is evaluated for the heterogeneous high-rise, high-density city of Hong Kong. It leads to a strong improvement of model results for near-surface air temperature and relative humidity, which is due to better consideration of the process of horizontal advection in the urban canopy layer. For wind speed, model results are improved on average by the multi-layer coupling but not for all stations. Future developments of the multi-layer SURFEX-TEB will focus on improving the calculation of radiative exchanges, which will allow a variety of building heights at grid-point scale to be taken into account.

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12

Chiri, Giovanni M., Maddalena Achenza, Anselmo Canì, Leonardo Neves, Luca Tendas, and Simone Ferrari. "The Microclimate Design Process in Current African Development: The UEM Campus in Maputo, Mozambique." Energies 13, no. 9 (May 7, 2020): 2316. http://dx.doi.org/10.3390/en13092316.

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Even if current action towards sustainability in architecture mainly concerns single buildings, the responsibility of the urban shape on local microclimate has largely been ascertained. In fact, it heavily affects the energy performances of the buildings and their environmental behaviour. This produces the necessity to broaden the field of intervention toward the urban scale, involving in the process different disciplines, from architecture to fluid dynamics and physics. Following these ideas, the Masterplan for the Campus of the University Eduardo Mondlane in Maputo (Mozambique) develops a methodology that integrates microclimatic data and analyses from the initial design model. The already validated software ENVI-met (Version 4.4, ENVI_MET GmbH, Essen, Germany) acts as a useful ‘feedback’ tool that is able to assess the microclimatic behaviour of the design concept, also in terms of outdoor comfort. In particular, the analysis focused on the microclimatic performances of a ‘C’ block typology east oriented in relation to the existing buildings, in Maputo’s specific climatic characteristics. The initial urban proposal was gradually evaluated and modified in relation to the main critical aspects highlighted by the microclimatic analyses, in a sort of circular process that ended with a proposed solution ensuring better outdoor comfort than the existing buildings, and which provided an acceptable balance between spatial and climatic instances.

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13

TUGRUL, Ayse Zelal. "URBAN ENERGY MODELLING APPROACHES: A LITERATURE REVIEW." International Journal of Energy and Smart Grid 4, no. 2 (May 27, 2020): 57–64. http://dx.doi.org/10.23884/ijesg.2019.4.2.02.

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14

Bahu, J. M., A. Koch, E. Kremers, and S. M. Murshed. "TOWARDS A 3D SPATIAL URBAN ENERGY MODELLING APPROACH." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences II-2/W1 (September 13, 2013): 33–41. http://dx.doi.org/10.5194/isprsannals-ii-2-w1-33-2013.

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15

Oraiopoulos, A., and B. Howard. "On the accuracy of Urban Building Energy Modelling." Renewable and Sustainable Energy Reviews 158 (April 2022): 111976. http://dx.doi.org/10.1016/j.rser.2021.111976.

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16

Bahu, Jean-Marie, Andreas Koch, Enrique Kremers, and Syed Monjur Murshed. "Towards a 3D Spatial Urban Energy Modelling Approach." International Journal of 3-D Information Modeling 3, no. 3 (July 2014): 1–16. http://dx.doi.org/10.4018/ij3dim.2014070101.

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Today's needs to reduce the environmental impact of energy use impose dramatic changes for energy infrastructure and existing demand patterns (e.g. buildings) corresponding to their specific context. In addition, future energy systems are expected to integrate a considerable share of fluctuating power sources and equally a high share of distributed generation of electricity. Energy system models capable of describing such future systems and allowing the simulation of the impact of these developments thus require a spatial representation in order to reflect the local context and the boundary conditions. This paper describes two recent research approaches developed at EIFER in the fields of (a) geo-localised simulation of heat energy demand in cities based on 3D morphological data and (b) spatially explicit Agent-Based Models (ABM) for the simulation of smart grids. 3D city models were used to assess solar potential and heat energy demand of residential buildings which enable cities to target the building refurbishment potentials. Distributed energy systems require innovative modelling techniques where individual components are represented and can interact. With this approach, several smart grid demonstrators were simulated, where heterogeneous models are spatially represented. Coupling 3D geodata with energy system ABMs holds different advantages for both approaches. On one hand, energy system models can be enhanced with high resolution data from 3D city models and their semantic relations. Furthermore, they allow for spatial analysis and visualisation of the results, with emphasis on spatially and structurally correlations among the different layers (e.g. infrastructure, buildings, administrative zones) to provide an integrated approach. On the other hand, 3D models can benefit from more detailed system description of energy infrastructure, representing dynamic phenomena and high resolution models for energy use at component level. The proposed modelling strategies conceptually and practically integrate urban spatial and energy planning approaches. The combined modelling approach that will be developed based on the described sectorial models holds the potential to represent hybrid energy systems coupling distributed generation of electricity with thermal conversion systems.

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17

Huang, Bin, Ke Xing, and Stephen Pullen. "Life-cycle energy modelling for urban precinct systems." Journal of Cleaner Production 142 (January 2017): 3254–68. http://dx.doi.org/10.1016/j.jclepro.2016.10.144.

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18

Brownsword, R. A., P. D. Fleming, J. C. Powell, and N. Pearsall. "Sustainable cities – modelling urban energy supply and demand." Applied Energy 82, no. 2 (October 2005): 167–80. http://dx.doi.org/10.1016/j.apenergy.2004.10.005.

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19

Gholami, Mansoureh, Majid Mofidi Shemirani, and Rima Fayaz. "A modelling methodology for a solar energy-efficient neighbourhood." Smart and Sustainable Built Environment 7, no. 1 (April 3, 2018): 117–32. http://dx.doi.org/10.1108/sasbe-10-2017-0044.

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Purpose The purpose of this paper is to present a methodology to quantify the solar energy potential for applying photovoltaic systems and find an efficient geometry for urban blocks to obtain a better quality of daylighting in terms of continuous daylight autonomy (DA) and spatial DA with less energy consumption. Design/methodology/approach The paper is based on a complete simulation of the topography and micro-climate of the area under study. Simulations were performed using ArcGIS and Rhinoceros and urban daylight (UD) and urban modeling interface plugin for a neighborhood in the region of Narmak in Tehran, Iran. Five configurations of a neighborhood were compared using simulations. Findings It was found that the impact of the geometrical form on daylight gain and energy consumption is significant and the terraced model is the most suitable form for obtaining a constant floor area ratio. Furthermore, it is an optimal form of urban blocks to gain the most energy through photovoltaic systems in the neighborhood as it would be able to satisfy about 42 percent of the energy needs. Originality/value Planning to achieve sufficient energy factors in cities is a difficult task, since urban planners often do not have adequate technical knowledge to measure the contribution of solar energy in urban plans and this paper aims to introduce a comprehensive modeling methodology by which the urban energy planning can be used and understood in the urban context to make it completely clear as a strategy of implementation.

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20

Dagoumas, Athanasios. "Modelling socio-economic and energy aspects of urban systems." Sustainable Cities and Society 13 (October 2014): 192–206. http://dx.doi.org/10.1016/j.scs.2013.11.003.

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21

Sola, Alaia, Cristina Corchero, Jaume Salom, and Manel Sanmarti. "Multi-domain urban-scale energy modelling tools: A review." Sustainable Cities and Society 54 (March 2020): 101872. http://dx.doi.org/10.1016/j.scs.2019.101872.

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22

Pasichnyi, Oleksii, Jörgen Wallin, and Olga Kordas. "Data-driven building archetypes for urban building energy modelling." Energy 181 (August 2019): 360–77. http://dx.doi.org/10.1016/j.energy.2019.04.197.

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23

Taylor, Simon, Denis Fan, and Mark Rylatt. "Enabling urban-scale energy modelling: a new spatial approach." Building Research & Information 42, no. 1 (August 2013): 4–16. http://dx.doi.org/10.1080/09613218.2013.813169.

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24

Arrizabalaga, Eneko, Iñigo Muñoz, Nekane Hermoso, Irantzu Urcola, José Luis Izkara, Iñaki Prieto, Juan Pedrero, Patxi Hernandez, and Lara Mabe. "Methodology for the Advanced Integrated Urban Energy Planning." Proceedings 20, no. 1 (July 25, 2019): 17. http://dx.doi.org/10.3390/proceedings2019020017.

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The holistic modelling approach required for the long-term integrated urban energy planning is becoming a big challenge since the complexity of cities, as well as their commitments are increasing rapidly. Many municipalities require technical support during the definition of the direction of their long-term energy transition plans. Innovative modelling approaches and the ex-ante impact assessment are necessary steps of the process since the direction adopted by the city will have many long-lasting implications not only in the energy and climate dimensions but also in their social and economic development. This paper presents the overall methodological and modelling approach and the initial results of the developed Advanced Integrated Urban Planning process that has been validated by its application in the cities of Helsinki, Hamburg and Nantes.

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25

Yang, Feng, and Zhidian Jiang. "Urban building energy modelling and urban design for sustainable neighbourhood development-A China perspective." IOP Conference Series: Earth and Environmental Science 329 (October 11, 2019): 012016. http://dx.doi.org/10.1088/1755-1315/329/1/012016.

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26

Agugiaro, G., A. Zwamborn, C. Tigchelaar, E. Matthijssen, C. León-Sánchez, F. van der Molen, and J. Stoter. "ON THE INFLUENCE OF PARTY WALLS FOR URBAN ENERGY MODELLING." International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences XLVIII-4/W5-2022 (October 14, 2022): 9–16. http://dx.doi.org/10.5194/isprs-archives-xlviii-4-w5-2022-9-2022.

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Abstract. In the last 15 years semantic 3D city models have seen a steady growth in terms of creation and adoption. Many cities world-wide have now at least one city model which can be used for several applications. Energy- and sustainability-related topics are among those that have experienced a noteworthy increase of interest from the Geomatics community. 3D city models have become a steady component of Urban Energy Modelling, in which bottom-up approaches are developed to assess, for example, the energy efficiency of the building stock and to explore different scenarios of building refurbishment. Within this context, this paper focuses on investigating how much party walls can contribute to the energy demand estimation of a building. For this reason, two approaches to compute party walls are described and compared. The nature and the magnitude of their differences, as well as their possible impact on downstream applications, are considered in order to shed light on whether discrepancies in the amount of computed party wall area might lead to significant differences in terms energy demand of the residential building stock. The case study area is located in the Netherlands and encompasses the municipality of Rijssen-Holten.

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Malhotra, Avichal, Julian Bischof, Alexandru Nichersu, Karl-Heinz Häfele, Johannes Exenberger, Divyanshu Sood, James Allan, et al. "Information modelling for urban building energy simulation—A taxonomic review." Building and Environment 208 (January 2022): 108552. http://dx.doi.org/10.1016/j.buildenv.2021.108552.

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28

Chow, T. T., K. F. Fong, A. L. S. Chan, R. Yau, W. H. Au, and V. Cheng. "Energy modelling of district cooling system for new urban development." Energy and Buildings 36, no. 11 (November 2004): 1153–62. http://dx.doi.org/10.1016/j.enbuild.2004.04.002.

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29

Alhamwi, Alaa, Wided Medjroubi, Thomas Vogt, and Carsten Agert. "Modelling urban energy requirements using open source data and models." Applied Energy 231 (December 2018): 1100–1108. http://dx.doi.org/10.1016/j.apenergy.2018.09.164.

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30

Pye, Steve, and Hannah Daly. "Modelling sustainable urban travel in a whole systems energy model." Applied Energy 159 (December 2015): 97–107. http://dx.doi.org/10.1016/j.apenergy.2015.08.127.

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31

Crabtree, R. W., P. Dempsey, I. T. Clifforde, S. Quinn, B. Henderson, and A. Wilson. "Integrated modelling of urban watercourses." Proceedings of the Institution of Civil Engineers - Water and Maritime Engineering 156, no. 3 (September 2003): 265–74. http://dx.doi.org/10.1680/wame.2003.156.3.265.

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32

Maiullari, D., A. Palm, H. Wallbaum, and L. Thuvander. "Matching energy targets, stakeholders’ needs and modelling choices in developing urban energy scenarios." IOP Conference Series: Earth and Environmental Science 1078, no. 1 (September 1, 2022): 012087. http://dx.doi.org/10.1088/1755-1315/1078/1/012087.

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Abstract In order to meet greenhouse gas reduction goals, cities need to develop robust energy transition strategies relying both on the local capacity of combining social, economic and environmental perspectives in the decision-making process and on the collaboration between different actors to achieve knowledge and data integration. Scenarios are well-established methodological instruments to guide decisions in energy and spatial planning and have been employed to compare possible future pathways and envision the consequences of implementing decarbonization measures. However, qualitative and quantitative scenarios approaches are often disconnected. With the primary goal of supporting the implementation of the energy plan, this study develops for the City of Gothenburg a participatory method to support the alignment of qualitative and quantitative scenarios approaches. Decarbonization actions and drivers of change were discussed and prioritized in workshop sessions with representatives from the energy supplier(s), municipal administrations (city planners, environmental department), and researchers to develop relevant qualitative scenarios descriptions. Based on this, a list of requirements for quantitative scenarios analysis is developed to be, in a next step, translated and integrated into urban building energy models. Findings indicate the importance of early knowledge integration from different fields and highlight the lines of advancement in urban energy modelling to facilitate decision-making towards successful implementation of decarbonization targets.

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33

Gupta, Rajat, and Matt Gregg. "Targeting and modelling urban energy retrofits using a city-scale energy mapping approach." Journal of Cleaner Production 174 (February 2018): 401–12. http://dx.doi.org/10.1016/j.jclepro.2017.10.262.

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Sandvall, Akram, Martin Hagberg, and Kristina Lygnerud. "Modelling of urban excess heat use in district heating systems." Energy Strategy Reviews 33 (January 2021): 100594. http://dx.doi.org/10.1016/j.esr.2020.100594.

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Orozco-Messana, Javier, Milagro Iborra-Lucas, and Raimon Calabuig-Moreno. "Neighbourhood Modelling for Urban Sustainability Assessment." Sustainability 13, no. 9 (April 22, 2021): 4654. http://dx.doi.org/10.3390/su13094654.

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Climate change is becoming a dominant concern for advanced countries. The Paris Agreement sets out a global framework whose implementation relates to all human activities and is commonly guided by the United Nations Sustainable Development Goals (UN SDGs), which set the scene for sustainable development performance configuring all climate action related policies. Fast control of CO2 emissions necessarily involves cities since they are responsible for 70 percent of greenhouse gas emissions. SDG 11 (Sustainable cities and communities) is clearly involved in the deployment of SDG 13 (Climate Action). European Sustainability policies are financially guided by the European Green Deal for a climate neutral urban environment. In turn, a common framework for urban policy impact assessment must be based on architectural design tools, such as building certification, and common data repositories for standard digital building models. Many Neighbourhood Sustainability Assessment (NSA) tools have been developed but the growing availability of open data repositories for cities, together with big-data sources (provided through Internet of Things repositories), allow accurate neighbourhood simulations, or in other words, digital twins of neighbourhoods. These digital twins are excellent tools for policy impact assessment. After a careful analysis of current scientific literature, this paper provides a generic approach for a simple neighbourhood model developed from building physical parameters which meets relevant assessment requirements, while simultaneously being updated (and tested) against real open data repositories, and how this assessment is related to building certification tools. The proposal is validated by real data on energy consumption and on its application to the Benicalap neighbourhood in Valencia (Spain).

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Rosser, Julian F., Gavin Long, Sameh Zakhary, Doreen S. Boyd, Yong Mao, and Darren Robinson. "Modelling Urban Housing Stocks for Building Energy Simulation using CityGML EnergyADE." ISPRS International Journal of Geo-Information 8, no. 4 (March 29, 2019): 163. http://dx.doi.org/10.3390/ijgi8040163.

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Understanding the energy demand of a city’s housing stock is an important focus for local and national administrations to identify strategies for reducing carbon emissions. Building energy simulation offers a promising approach to understand energy use and test plans to improve the efficiency of residential properties. As part of this, models of the urban stock must be created that accurately reflect its size, shape and composition. However, substantial effort is required in order to generate detailed urban scenes with the appropriate level of attribution suitable for spatially explicit simulation of large areas. Furthermore, the computational complexity of microsimulation of building energy necessitates consideration of approaches that reduce this processing overhead. We present a workflow to automatically generate 2.5D urban scenes for residential building energy simulation from UK mapping datasets. We describe modelling the geometry, the assignment of energy characteristics based upon a statistical model and adopt the CityGML EnergyADE schema which forms an important new and open standard for defining energy model information at the city-scale. We then demonstrate use of the resulting urban scenes for estimating heating demand using a spatially explicit building energy microsimulation tool, called CitySim+, and evaluate the effects of an off-the-shelf geometric simplification routine to reduce simulation computational complexity.

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Remmen, Peter, Moritz Lauster, Michael Mans, Marcus Fuchs, Tanja Osterhage, and Dirk Müller. "TEASER: an open tool for urban energy modelling of building stocks." Journal of Building Performance Simulation 11, no. 1 (February 7, 2017): 84–98. http://dx.doi.org/10.1080/19401493.2017.1283539.

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Monteiro, Claudia Sousa, André Pina, Carlos Cerezo, Christoph Reinhart, and Paulo Ferrão. "The Use of Multi-detail Building Archetypes in Urban Energy Modelling." Energy Procedia 111 (March 2017): 817–25. http://dx.doi.org/10.1016/j.egypro.2017.03.244.

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Baur, Peter. "Alternative energy: Modelling resource conflict within an energy environment." Journal of Economic and Financial Sciences 5, no. 2 (October 31, 2012): 323–50. http://dx.doi.org/10.4102/jef.v5i2.288.

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With growing infrastructural pressure induced by urban densification combined with rural development and the increasing demands of industrialisation, South Africa is facing two related challenges. The first is a lack of sufficient energy to satisfactorily fulfil the needs of the expanding economy. The second is that South Africa has limited access to water. Electricity generation using the traditional coal-burning power stations requires vast amounts of water, for amongst other things, steam generation to drive the turbines and water is also used in the cooling process. Thus, as the demand for electricity grows, so too does the pressure on the country's strained water supplies. The growing demand for electricity favours the building of new traditional coal-burning power stations, which emit vast amounts of pollutants into the atmosphere, negatively affecting the environment. This leads to a degree of conflict between stakeholders, namely the energy producers, government bodies, and environmentalists. This paper uses Hirshleifer’s Conflict Success Function to highlight the ‘urgency’ of replacing traditional fuel-based power stations with alternative renewable energy generators, using South Africa as a case study.

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Fan, Jing-Li, Ling-Si Kong, Hang Wang, and Xian Zhang. "A water-energy nexus review from the perspective of urban metabolism." Ecological Modelling 392 (January 2019): 128–36. http://dx.doi.org/10.1016/j.ecolmodel.2018.11.019.

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Ji, Qunfeng, Yangbo Bi, Mehdi Makvandi, Qinli Deng, Xilin Zhou, and Chuancheng Li. "Modelling Building Stock Energy Consumption at the Urban Level from an Empirical Study." Buildings 12, no. 3 (March 21, 2022): 385. http://dx.doi.org/10.3390/buildings12030385.

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Quantifying the energy consumption of buildings is a complex and multi-scale task, with the entire process dependent on input data and urban surroundings. However, most urban energy models do not account for the urban environment. This paper employs a physical-based, bottom-up method to predict urban building operating energy consumption, using imported topography to consider shading effects on buildings. This method has proven to be feasible and aligned well with the benchmark. Research also suggests that commercial and transport buildings have the highest energy use intensity, significantly more than residential and office buildings. Specifically, cooling demands far outweigh heating demands for these building types. Therefore, buildings in the commercial and transportation sectors would receive greater consideration for energy efficiency and improvements to the cooling system would be a priority. Additionally, the method developed for predicting building energy demand at an urban scale can also be replicated in practice.

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Wong, Cyrus Ho Hin, Meng Cai, Chao Ren, Ying Huang, Cuiping Liao, and Shi Yin. "Modelling building energy use at urban scale: A review on their account for the urban environment." Building and Environment 205 (November 2021): 108235. http://dx.doi.org/10.1016/j.buildenv.2021.108235.

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Beşikci, Doğancan, Egemen Sulukan, and Tanay Sıdkı Uyar. "An urban techno-economic analysis and modelling for Turkey." Renewable Energy Focus 38 (September 2021): 1–8. http://dx.doi.org/10.1016/j.ref.2021.05.003.

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Widl, E., C. Agugiaro, and P. Puerto. "FIRST STEPS TOWARDS LINKING SEMANTIC 3D CITY MODELLING AND MULTI-DOMAIN CO-SIMULATION FOR ENERGY MODELLING AT URBAN SCALE." ISPRS Annals of Photogrammetry, Remote Sensing and Spatial Information Sciences IV-4 (September 19, 2018): 227–34. http://dx.doi.org/10.5194/isprs-annals-iv-4-227-2018.

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<p><strong>Abstract.</strong> An important prerequisite for the simulation-based assessment of energy systems at urban scale is the availability of high-quality, well-formatted and semantically structured data. Unfortunately, best practices and state-of-the-art approaches for urban data modelling are hardly applied in the context of energy-related simulations, such that data management and data access often become tedious and cumbersome tasks. This paper presents the so-called Simulation Package, i.e., a data model extending the 3D City Database for CityGML, and its derived data access layer, both aiming to bridge this gap between semantic 3D city modelling and simulation in the context of urban energy systems. The feasibility of this approach is demonstrated with the help of a concrete example, where the proposed extension has been implemented and integrated into a simulation toolchain. The aim is that the availability of a common, shared data model and the proof-of-concept implementation will contribute and foster adoption and further improvement in the future.</p>

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Hu, Guangwen, and Xianzhong Mu. "Dominants in evolution of urban energy metabolism: A case study of Beijing." Ecological Modelling 385 (October 2018): 26–34. http://dx.doi.org/10.1016/j.ecolmodel.2018.07.008.

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Jennings, Mark, David Fisk, and Nilay Shah. "Modelling and optimization of retrofitting residential energy systems at the urban scale." Energy 64 (January 2014): 220–33. http://dx.doi.org/10.1016/j.energy.2013.10.076.

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Ronzino, Amos, Anna Osello, Edoardo Patti, Lorenzo Bottaccioli, Chiara Danna, Andrea Lingua, Andrea Acquaviva, et al. "The Energy Efficiency Management at Urban Scale by Means of Integrated Modelling." Energy Procedia 83 (December 2015): 258–68. http://dx.doi.org/10.1016/j.egypro.2015.12.180.

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Salim, Flora D., Bing Dong, Mohamed Ouf, Qi Wang, Ilaria Pigliautile, Xuyuan Kang, Tianzhen Hong, et al. "Modelling urban-scale occupant behaviour, mobility, and energy in buildings: A survey." Building and Environment 183 (October 2020): 106964. http://dx.doi.org/10.1016/j.buildenv.2020.106964.

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Haider, S., A. Paquier, R. Morel, and J. Y. Champagne. "Urban flood modelling using computational fluid dynamics." Proceedings of the Institution of Civil Engineers - Water and Maritime Engineering 156, no. 2 (June 2003): 129–35. http://dx.doi.org/10.1680/wame.2003.156.2.129.

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Kubilay, Aytaç, Jonas Allegrini, Dominik Strebel, Yongling Zhao, Dominique Derome, and Jan Carmeliet. "Advancement in Urban Climate Modelling at Local Scale: Urban Heat Island Mitigation and Building Cooling Demand." Atmosphere 11, no. 12 (December 4, 2020): 1313. http://dx.doi.org/10.3390/atmos11121313.

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As cities and their population are subjected to climate change and urban heat islands, it is paramount to have the means to understand the local urban climate and propose mitigation measures, especially at neighbourhood, local and building scales. A framework is presented, where the urban climate is studied by coupling a meteorological model to a building-resolved local urban climate model, and where an urban climate model is coupled to a building energy simulation model. The urban climate model allows for studies at local scale, combining modelling of wind and buoyancy with computational fluid dynamics, radiative exchange and heat and mass transport in porous materials including evaporative cooling at street canyon and neighbourhood scale. This coupled model takes into account the hygrothermal behaviour of porous materials and vegetation subjected to variations of wetting, sun, wind, humidity and temperature. The model is driven by climate predictions from a mesoscale meteorological model including urban parametrisation. Building energy demand, such as cooling demand during heat waves, can be evaluated. This integrated approach not only allows for the design of adapted buildings, but also urban environments that can mitigate the negative effects of future climate change and increased urban heat islands. Mitigation solutions for urban heat island effect and heat waves, including vegetation, evaporative cooling pavements and neighbourhood morphology, are assessed in terms of pedestrian comfort and building (cooling) energy consumption.

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