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@article{claude_evaluating_2019,

abstract = {Historical dwellings make up a significant fraction of the French building stock and require substantial retrofitting to reduce their energy consumption and improve their thermal comfort. In the city center of Cahors, France, the old medieval dwellings are considered as valuable cultural heritage and internal insulation is often the only insulation technique that can be used when the architectural value of the exterior fa{\c c}ade is to be preserved. However, internal insulation may have an impact upon the hygrothermal performance of the wall, leading to lowered drying capacity, with possible interstitial condensation and mold growth. Hygrothermal models may be used to assess the risk of failure, but the accuracy of the results depends on how reliable the input data is, including external boundary conditions, which may vary significantly in dense medieval cities such as Cahors. In this study, a Geographical Information System model of Cahors is used to develop EnergyPlus models of individual dwellings. The boundary conditions output by these models are, in turn, used to model the hygrothermal performance of fa{\c c}ades with different internal insulations, using the hygrothermal tool Delphin. The Delphin outputs are then analyzed with the VTT model, a mold growth assessment model. Results highlight a quantitative correlation between some urban morphology characteristics and the hygrothermal performance of refurbished walls, with some configurations raising the risk of damage patterns. We find that bio-based insulation presents a better hygrothermal performance than mineral wool in most of the configurations.},

author = {Claude, Sophie and Ginestet, Stephane and Bonhomme, Marion and Escadeillas, Gilles and Taylor, Jonathon and Marincioni, Valentina and Korolija, Ivan and Altamirano, Hector},

doi = {10.1016/j.enbuild.2018.10.026},

file = {Claude et al. - 2019 - Evaluating retrofit options in a historical city c.pdf:/Users/pamelafennell/Zotero/storage/TLJ3N52A/Claude et al. - 2019 - Evaluating retrofit options in a historical city c.pdf:application/pdf},

issn = {03787788},

journal = {Energy and Buildings},

language = {en},

month = jan,

pages = {196--204},

shorttitle = {Evaluating retrofit options in a historical city center},

title = {Evaluating retrofit options in a historical city center: {Relevance} of bio-based insulation and the need to consider complex urban form in decision-making},

url = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818319595},

urldate = {2019-02-27},

volume = {182},

year = {2019},

bdsk-url-1 = {https://linkinghub.elsevier.com/retrieve/pii/S0378778818319595},

bdsk-url-2 = {https://doi.org/10.1016/j.enbuild.2018.10.026}}

@inproceedings{crawley_energyplus_2001,

address = {Washington, DC},

author = {Crawley, Drury B. and Lawrie, Linda and Winkelman, Frederick C. and Pedersen, Curtis O.},

booktitle = {Forum 2001 {Solar} {Energy}: {The} {Power} to choose},

file = {Juve - 2011 - Lawrence Berkeley National LaboratoryBerkeley, Cal.pdf:/Users/pamelafennell/Zotero/storage/J937PMAB/Juve - 2011 - Lawrence Berkeley National LaboratoryBerkeley, Cal.pdf:application/pdf;Juve - 2011 - Lawrence Berkeley National LaboratoryBerkeley, Cal.pdf:/Users/pamelafennell/Zotero/storage/CULCFUE8/Juve - 2011 - Lawrence Berkeley National LaboratoryBerkeley, Cal.pdf:application/pdf},

language = {en},

month = apr,

title = {{EnergyPlus}: {A} {New}-{Generation} {Building} {Energy} {Simulation} {Program}},

urldate = {2021-04-06},

year = {2001}}

@article{mathur_assessing_2021,

author = {Mathur, Anmol and Fennell, Pamela and Rawal, Rajan and Korolija, Ivan},

doi = {10.1080/23744731.2021.1941248},

issn = {2374-4731},

journal = {STBE},

month = jun,

pages = {1--29},

publisher = {Taylor \& Francis},

title = {Assessing a fit-for-purpose urban building energy modelling framework with reference to {Ahmedabad}},

url = {https://doi.org/10.1080/23744731.2021.1941248},

year = {2021},

bdsk-url-1 = {https://doi.org/10.1080/23744731.2021.1941248}}

@inproceedings{fennell_comparison_2021,

author = {Fennell, P and Korolija, I and Ruyssevelt, P},

publisher = {IBPSA},

title = {A comparison of performance of three variance-based sensitivity analysis methods on an urban-scale building energy model},

year = {2021}}

@article{remmen_teaser:_2018,

author = {Remmen, Peter and Lauster, Moritz and Mans, Michael and Fuchs, Marcus and Osterhage, Tanja and M{\"u}ller, Dirk},

doi = {10.1080/19401493.2017.1283539},

file = {Remmen et al. - 2018 - TEASER an open tool for urban energy modelling of.pdf:/Users/pamelafennell/Zotero/storage/GB3NRY2V/Remmen et al. - 2018 - TEASER an open tool for urban energy modelling of.pdf:application/pdf},

issn = {1940-1493, 1940-1507},

journal = {Journal of Building Performance Simulation},

keywords = {R-C model},

language = {en},

month = jan,

number = {1},

pages = {84--98},

shorttitle = {{TEASER}},

title = {{TEASER}: an open tool for urban energy modelling of building stocks},

url = {https://www.tandfonline.com/doi/full/10.1080/19401493.2017.1283539},

urldate = {2018-05-28},

volume = {11},

year = {2018},

bdsk-url-1 = {https://www.tandfonline.com/doi/full/10.1080/19401493.2017.1283539},

bdsk-url-2 = {https://doi.org/10.1080/19401493.2017.1283539}}

@article{fonseca_city_2016,

abstract = {This paper describes the City Energy Analyst (CEA), a computational framework for the analysis and optimization of energy systems in neighborhoods and city districts. The framework allows analyzing the energy, carbon and financial benefits of multiple urban design scenarios in conjunction to optimal schemes of distributed generation. For this, the framework integrates time-dependent methods for building energy performance simulation, conversion and storage technologies simulation, assessment of local energy potentials, bi-level energy systems optimization and multi-criteria analysis. Based on past research, the framework introduces a novel interface to facilitate the spatiotemporal analysis of patterns of demand and potential infrastructure solutions. The model was programmed in Python v2.7 and built as an extension of the Geographic Information System ArcGIS v10.3, which serves as a platform for the allocation and future dissemination of spatiotemporal data. We present an application of the model for a downtown area in Switzerland where we evaluated four trajectories of development and found optimum infrastructure solutions for their operation. For a more holistic approach we used the 2000-W/1-t CO2 society vision concept to compare the environmental performance of these solutions with that of embodied energy in buildings and transportation systems. From the optimization process, most infrastructure solutions showed an average integration of 50\% to 80\% of buildings in thermal micro-grids, 50 to 100\% of the available solar potential, and a resource mix consisting of photovoltaic electricity and sources of waste and ambient heat. For a balanced distribution of social, environmental and economic criteria, the results showed potential relative savings in the area from 45\% to 60\% in emissions and from 25\% to 50\% in primary energy at an annualized cost between 14\% and 44\% higher than today. For an economic-driven distribution, the results showed savings of up to 23\% in emissions, 36\% in primary energy and 11\% in costs. We identified close to 15\% in emissions and 20\% in primary energy savings with variable costs between −2\% and 23\% in the area are strongly related to the urban design option rather than to its optimal energy system. In comparison to local benchmarks, the environmental impact of buildings during operation lies between that of embodied energy in buildings and mobility in the service sector (business flights). We estimated that an increase in close to 4\% of today's average efficiency of photovoltaic technology would allow the area to comply with those local benchmarks. On the other hand, we concluded the suitability of the City Energy Analyst (CEA) to assist urban planning authorities looking for both design and engineering options to increase the performance of their neighborhoods and city districts.},

author = {Fonseca, Jimeno A. and Nguyen, Thuy-An and Schlueter, Arno and Marechal, Francois},

doi = {10.1016/j.enbuild.2015.11.055},

file = {Fonseca et al. - 2016 - City Energy Analyst (CEA) Integrated framework fo.pdf:/Users/pamelafennell/Zotero/storage/4DRJEYN8/Fonseca et al. - 2016 - City Energy Analyst (CEA) Integrated framework fo.pdf:application/pdf},

issn = {03787788},

journal = {Energy and Buildings},

keywords = {ROM},

language = {en},

month = feb,

pages = {202--226},

shorttitle = {City {Energy} {Analyst} ({CEA})},

title = {City {Energy} {Analyst} ({CEA}): {Integrated} framework for analysis and optimization of building energy systems in neighborhoods and city districts},

url = {http://linkinghub.elsevier.com/retrieve/pii/S0378778815304199},

urldate = {2018-05-17},

volume = {113},

year = {2016},

bdsk-url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0378778815304199},

bdsk-url-2 = {https://doi.org/10.1016/j.enbuild.2015.11.055}}

@article{chen_automatic_2017,

abstract = {Buildings in cities consume 30--70\% of total primary energy, and improving building energy efficiency is one of the key strategies towards sustainable urbanization. Urban building energy models (UBEM) can support city managers to evaluate and prioritize energy conservation measures (ECMs) for investment and the design of incentive and rebate programs. This paper presents the retrofit analysis feature of City Building Energy Saver (CityBES) to automatically generate and simulate UBEM using EnergyPlus based on cities' building datasets and user-selected ECMs. CityBES is a new open web-based tool to support city-scale building energy efficiency strategic plans and programs. The technical details of using CityBES for UBEM generation and simulation are introduced, including the workflow, key assumptions, and major databases. Also presented is a case study that analyzes the potential retrofit energy use and energy cost savings of five individual ECMs and two measure packages for 940 office and retail buildings in six city districts in northeast San Francisco, United States. The results show that: (1) all five measures together can save 23--38\% of site energy per building; (2) replacing lighting with light-emitting diode lamps and adding air economizers to existing heating, ventilation and airconditioning (HVAC) systems are most cost-effective with an average payback of 2.0 and 4.3 years, respectively; and (3) it is not economical to upgrade HVAC systems or replace windows in San Francisco due to the city's mild climate and minimal cooling and heating loads. The CityBES retrofit analysis feature does not require users to have deep knowledge of building systems or technologies for the generation and simulation of building energy models, which helps overcome major technical barriers for city managers and their consultants to adopt UBEM.},

author = {Chen, Yixing and Hong, Tianzhen and Piette, Mary Ann},

doi = {10.1016/j.apenergy.2017.07.128},

file = {Chen et al. - 2017 - Automatic generation and simulation of urban build.pdf:/Users/pamelafennell/Zotero/storage/DYPDKQMC/Chen et al. - 2017 - Automatic generation and simulation of urban build.pdf:application/pdf},

issn = {03062619},

journal = {Applied Energy},

keywords = {ECM, retrofit analysis, shading multiplier, shading simplification},

language = {en},

month = nov,

pages = {323--335},

title = {Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis},

url = {http://linkinghub.elsevier.com/retrieve/pii/S0306261917310024},

urldate = {2018-05-11},

volume = {205},

year = {2017},

bdsk-url-1 = {http://linkinghub.elsevier.com/retrieve/pii/S0306261917310024},

bdsk-url-2 = {https://doi.org/10.1016/j.apenergy.2017.07.128}}

@article{robinson_citysim:_2009,

abstract = {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.},

author = {Robinson, Darren and Haldi, F and K{\"a}mpf, J and Leroux, P and Perez, D and Rasheed, A and Wilke, U},

file = {Robinson et al. - CITYSIM COMPREHENSIVE MICRO-SIMULATION OF RESOURC.pdf:/Users/pamelafennell/Zotero/storage/SKBHBH3Y/Robinson et al. - CITYSIM COMPREHENSIVE MICRO-SIMULATION OF RESOURC.pdf:application/pdf},

language = {en},

month = jul,

pages = {8},

title = {{CITYSIM}: {COMPREHENSIVE} {MICRO}-{SIMULATION} {OF} {RESOURCE} {FLOWS} {FOR} {SUSTAINABLE} {URBAN} {PLANNING}},

year = {2009}}

@article{hong_ten_2020,

abstract = {Buildings in cities consume up to 70\% of all primary energy. To achieve cities' energy and climate goals, it is necessary to reduce energy use and associated greenhouse gas emissions in buildings through energy conservation and efficiency improvements. Computational tools empowered with rich urban datasets can model performance of buildings at the urban scale to provide quantitative insights for stakeholders and inform their decision making on urban energy planning, as well as building energy retrofits at scale, to achieve efficiency, sustainability, and resilience of urban buildings. Designing and operating urban buildings as a group (from a city block to a district to an entire city) rather than as single individuals requires simulation and optimization to account for interactions among buildings and between buildings and their surrounding urban environment, and for district energy systems serving multiple buildings with diverse thermal loads across space and time. When hundreds or more buildings are involved in typical urban building energy modeling (UBEM) to estimate annual energy demand, evaluate design or retrofit options, and quantify impacts of extreme weather events or climate change, it is crucial to integrate urban datasets and UBEM tools in a seamless automatic workflow with cloud or high-performance computing for users including urban planners, designers and researchers. This paper presents ten questions that highlight significant UBEM research and applications. The proposed answers aim to stimulate discussion and provide insights into the current and future research on UBEM, and more importantly, to inspire new and important questions from young researchers in the field.},

author = {Hong, Tianzhen and Chen, Yixing and Luo, Xuan and Luo, Na and Lee, Sang Hoon},

doi = {10.1016/j.buildenv.2019.106508},

file = {ScienceDirect Full Text PDF:/Users/pamelafennell/Zotero/storage/A5WXCIPS/Hong et al. - 2020 - Ten questions on urban building energy modeling.pdf:application/pdf;ScienceDirect Snapshot:/Users/pamelafennell/Zotero/storage/VBTKUKKZ/S0360132319307206.html:text/html},

issn = {0360-1323},

journal = {Building and Environment},

keywords = {Building energy use, Building performance simulation, Energy efficiency, Urban building energy modeling (UBEM), Urban energy planning, Urban systems},

language = {en},

month = jan,

pages = {106508},

title = {Ten questions on urban building energy modeling},

url = {https://www.sciencedirect.com/science/article/pii/S0360132319307206},

urldate = {2023-03-25},

volume = {168},

year = {2020},

bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0360132319307206},

bdsk-url-2 = {https://doi.org/10.1016/j.buildenv.2019.106508}}

@article{reinhart_umi_2013,

abstract = {One widely recognized opportunity to reduce global carbon emissions is to make urban neighborhoods more resource efficient. Significant effort has hence gone into developing computer-based design tools to ensure that individual buildings use less energy. While these tools are increasingly used in practice, they currently do not allow design teams to model groups of dozens or hundreds of buildings effectively, which is why a growing number of research teams are working on dedicated urban modeling tools.},

author = {Reinhart, Christoph F. and Dogan, Timur and Jakubiec, J. Alstan and Rakha, Tarek and Sang, Andrew},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/ZSAUYR8C/Reinhart et al. - 2013 - UMI - an urban simulation environment for building.pdf:application/pdf},

language = {en},

month = dec,

note = {Last Modified: 2014-12-18T09:24+01:00},

title = {{UMI} - an urban simulation environment for building energy use, daylighting and walkability},

url = {https://www.aivc.org/resource/umi-urban-simulation-environment-building-energy-use-daylighting-and-walkability},

urldate = {2023-08-16},

year = {2013},

bdsk-url-1 = {https://www.aivc.org/resource/umi-urban-simulation-environment-building-energy-use-daylighting-and-walkability}}

@article{amrith_optimising_2025,

abstract = {Optimisation, as applied to physics-based urban building energy models, is vastly under-researched despite its immense potential in providing vital decision support to local authorities facing the challenge of decarbonising their building stocks. One primary barrier is the vast parameter space that arises at the stock level; this challenges all optimisation algorithms, and ineffective parameter space exploration ultimately leads to poor quality results. A parameter decomposition methodology is presented, which can significantly reduce the parameter space of a stock-level optimisation problem without changing the structure. We show that the separability of a stock-level optimisation problem depends on physical and input factors, including restrictions imposed by the real-world project. The methodology is demonstrated in optimising 53 buildings using dynamic thermal simulation. This split approach yielded a vast improvement in the quality of results compared to a na{\"\i}ve approach, with final hypervolume values of 0.630 and 0.525, respectively.},

author = {Amrith, Shyam and , Ivan, Korolija and , Pamela, Fennell and , Dimitrios, Rovas and and Ruyssevelt, Paul},

doi = {10.1080/19401493.2025.2472306},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/68YIKX3U/Amrith et al. - Optimising building stock retrofits for urban-scal.pdf:application/pdf},

issn = {1940-1493},

journal = {Journal of Building Performance Simulation},

keywords = {Building stock, building stock modelling, building stock optimisation, multi-objective optimisation, retrofit, urban building energy modelling},

month = mar,

note = {\_eprint: https://doi.org/10.1080/19401493.2025.2472306},

number = {0},

pages = {1--16},

publisher = {Taylor \& Francis},

shorttitle = {Optimising building stock retrofits for urban-scale action planning},

title = {Optimising building stock retrofits for urban-scale action planning: improving the feasibility without losing information},

url = {https://doi.org/10.1080/19401493.2025.2472306},

urldate = {2025-05-12},

volume = {0},

year = {2025},

bdsk-url-1 = {https://doi.org/10.1080/19401493.2025.2472306}}

@inproceedings{oraiopoulos_reducing_2024,

address = {Ahmedabad, Gujarat, India},

author = {Oraiopoulos, Argyris and Wieser Rey, Martin and Verdiere, Marion and Fennell, P. J. and Ruyssevelt, Paul},

month = dec,

title = {Reducing extreme discomfort in the global {South} -- {Comparison} of a calibrated model and locally measured data from informal housing in {Peru}},

url = {https://discovery.ucl.ac.uk/id/eprint/10188055/1/CATE23_149_v1.pdf},

year = {2024},

bdsk-url-1 = {https://discovery.ucl.ac.uk/id/eprint/10188055/1/CATE23_149_v1.pdf}}

@inproceedings{baetens_openideas_2015,

abstract = {Contemporary research focuses on net-zero energy buildings and their integration in larger energy systems. By consequence, a vast set of research questions become increasingly multi-domain and multiscale. With this increasing complexity, the need for more elaborate building energy simulation tools rises. The presented paper reviews the development of the OpenIDEAS framework, an open framework developed for integrated district energy simulations consisting of IDEAS, StROBe, FastBuildings and GreyBox to answer the new research questions rising in the multidisciplinary building energy domain.},

author = {Baetens, Ruben and De Coninck, Roel and Jorissen, Filip and Picard, Damien and Helsen, Lieve and Saelens, Dirk},

doi = {10.26868/25222708.2015.2243},

file = {PDF:/Users/pamelafennell/Zotero/storage/8C2ILCTE/Baetens et al. - 2015 - OpenIDEAS -- An Open Framework for integrated District Energy Simulations.pdf:application/pdf},

language = {en},

month = dec,

title = {{OpenIDEAS} -- {An} {Open} {Framework} for integrated {District} {Energy} {Simulations}},

url = {https://publications.ibpsa.org/conference/paper/?id=bs2015_2243},

urldate = {2026-03-30},

year = {2015},

bdsk-url-1 = {https://publications.ibpsa.org/conference/paper/?id=bs2015_2243},

bdsk-url-2 = {https://doi.org/10.26868/25222708.2015.2243}}

@inproceedings{kontar_urbanopt_2020,

author = {Kontar, Rawad El and Polly, Benjamin and Charan, Tanushree and Fleming, Katherine and Moore, Nathan and Long, Nicholas and Goldwasser, David},

file = {Snapshot:/Users/pamelafennell/Zotero/storage/QHPRY2KU/urbanopt-an-open-source-software-development-kit-for-community-an.html:text/html},

language = {American English},

shorttitle = {{URBANopt}},

title = {{URBANopt}: {An} {Open}-{Source} {Software} {Development} {Kit} for {Community} and {Urban} {District} {Energy} {Modeling}: {Preprint}},

url = {https://research-hub.nlr.gov/en/publications/urbanopt-an-open-source-software-development-kit-for-community-an/},

urldate = {2026-03-30},

year = {2020},

bdsk-url-1 = {https://research-hub.nlr.gov/en/publications/urbanopt-an-open-source-software-development-kit-for-community-an/}}

@article{schwartz_modelling_2022,

abstract = {The UK Government has recently committed to achieve net zero carbon status by year 2050. Schools are responsible for around 2\% of the UK's total energy consumption, and around 15\% of the UK public sector's carbon emissions. A detailed analysis of the English school building stock's performance can help policymakers improve its energy efficiency and indoor environmental quality. Building stock modelling is a technique commonly used to quantify current and future energy demand or indoor environmental quality performance of large numbers of buildings at the neighbourhood, city, regional or national level. `Building-by-building' stock modelling is a modelling technique whereby individual buildings within the stock are modelled and simulated, and performance results are aggregated and analysed at stock level. This paper presents the development of the Modelling Platform for Schools (MPS) -- an automated generation of one-by-one thermal models of schools in England through the analysis and integration of a range of data (geometry, size, number of buildings within a school premises etc.) from multiple databases and tools (Edubase/Get Information About Schools, Property Data Survey Programme, Ordanance Survey and others). The study then presents an initial assessment and evaluation of the modelling procedure of the proposed platform. The model evaluation has shown that out of 15,245 schools for which sufficient data were available, nearly 50\% can be modelled in an automated manner having a high level of confidence of similarity with the actual buildings. Visual comparison between automatically-generated models and actual buildings has shown that around 70\% of the models were, indeed, geometrically accurate.},

author = {Schwartz, Yair and Korolija, Ivan and Godoy-Shimizu, Daniel and Hong, Sung Min and Dong, Jie and Grassie, Duncan and Mavrogianni, Anna and Mumovic, Dejan},

doi = {10.1016/j.enbuild.2021.111566},

file = {ScienceDirect Snapshot:/Users/pamelafennell/Zotero/storage/5MD3HTK2/S0378778821008501.html:text/html},

issn = {0378-7788},

journal = {Energy and Buildings},

keywords = {Generative design, Schools stock, Stock modelling, Thermal simulation},

month = jan,

pages = {111566},

shorttitle = {Modelling platform for schools ({MPS})},

title = {Modelling platform for schools ({MPS}): {The} development of an automated {One}-{By}-{One} framework for the generation of dynamic thermal simulation models of schools},

url = {https://www.sciencedirect.com/science/article/pii/S0378778821008501},

urldate = {2026-03-30},

volume = {254},

year = {2022},

bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0378778821008501},

bdsk-url-2 = {https://doi.org/10.1016/j.enbuild.2021.111566}}

@article{schwartz_school_2024,

abstract = {Indoor environmental quality (IEQ) in classrooms is crucial for students' health, wellbeing, and academic success. Rising outdoor temperatures due to climate change pose a significant risk -- overheating in schools can negatively affect cognitive performance and health for both students and staff.This study examines the risk of overheating in primary and secondary schools in London, focusing on the impact of current and future climate scenarios. Using the UK Department for Education's Building Bulletin 101 (BB101) as a framework, the study integrates data from various sources, including GIS form data and a national school survey, to develop a `one-by-one' school stock thermal model called the Modelling Platform for Schools (MPS). This model, built using EnergyPlus, allows for the analysis of school thermal performance at national, regional, and individual levels.The results show that by the 2050s, under a medium emissions scenario, average summer temperatures in schools could reach nearly 30$\,^{\circ}$C -- 7\% higher than current levels. The study also developed an overheating-risk regression model for London schools and explored potential mitigation strategies, such as extending summer holidays or adjusting school activities to times when overheating risk is lower.},

author = {Schwartz, Yair and Korolija, Ivan and Godoy-Shimizu, Daniel and Hong, Sung-Min and Mavrogianni, Anna and Mumovic, Dejan},

doi = {10.1080/17508975.2024.2410804},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/ZCDHCBK2/Schwartz et al. - 2024 - School building-stock climate resilience evaluating London's school stock overheating performance.pdf:application/pdf},

issn = {1750-8975},

journal = {Intelligent Buildings International},

keywords = {buildings stock modelling, climate resilience, future climate scenarios, Overheating, school building},

month = may,

note = {\_eprint: https://doi.org/10.1080/17508975.2024.2410804},

number = {3},

pages = {107--128},

publisher = {Taylor \& Francis},

shorttitle = {School building-stock climate resilience},

title = {School building-stock climate resilience: evaluating {London}'s school stock overheating performance},

url = {https://doi.org/10.1080/17508975.2024.2410804},

urldate = {2026-03-30},

volume = {16},

year = {2024},

bdsk-url-1 = {https://doi.org/10.1080/17508975.2024.2410804}}

@inproceedings{fennell_developing_2023,

abstract = {Urban Building Energy Models (UBEMs) are an increasingly important tool for national and local authorities seeking to understand and manage their carbon emissions. As such tools move from the preserve of research into more general application, interest in learning about their application is increasing. The quantity of data inherent in a UBEM and their complexity mean that training students in the underpinning principles and their application requires teaching wide body of knowledge. This presents significant challenges for educators required to fit within pre-defined limits for teaching courses. The authors have now taught urban-scale building energy modelling to 4 cohorts of students in India, Peru and the UK using two different approaches. This paper summarises the contents of the courses and the approach taken to delivering the required learning in the limited time available. It details student feedback, outcomes and lessons learned for the different approaches and the challenges which remain.},

author = {Fennell, Pamela Jane and Korolija, Ivan and Rawal, Rajan and Wieser, Martin and Oraiopoulos, Argyrios},

doi = {10.26868/25222708.2023.1468},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/26HXVUJK/Fennell et al. - 2023 - Developing teaching in urban building energy modelling.pdf:application/pdf},

language = {en-US},

pages = {1070--1077},

publisher = {IBPSA},

series = {Building {Simulation}},

title = {Developing teaching in urban building energy modelling},

url = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1468},

urldate = {2026-03-30},

volume = {18},

year = {2023},

bdsk-url-1 = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1468},

bdsk-url-2 = {https://doi.org/10.26868/25222708.2023.1468}}

@inproceedings{kourgiozou_development_2023,

abstract = {University campuses present a unique opportunity for decarbonisation through intelligence integration for smart-energy campuses. So far, the evidence-base for smart energy campuses focuses on building-level demonstrations or archetypal approaches and the university campus stock lacks a common assessment framework to characterise and evaluate smart-energy transition pathways.This paper presents a methodological framework that leverages automated computational methods (3DStock, SimStock) to produce building-by-building dynamic thermal models. The modelling method can benefit the evaluation of smart-energy campus and decarbonisation strategies and simulate the dynamics of complex HVAC under demand-response where data availability is more granular. Instead of using archetypal approaches to represent the heterogeneity of building stocks, this work developed an automated building-by-building stock modelling approach based on a case study. HVAC systems are also modelled based on information from Display Energy Certificates. Model calibration is performed at stock level against actual data from Building Monitoring Systems and operational energy performance data following the CIBSE TM63 protocol. Geometry checks showed that 63\% of the models matched actual geometry sufficiently, whereas energy use intensity was overestimated by around 35\% across the campus in the baseline partially calibrated building models. For a typology, initial comparisons with a fully calibrated model signified lighting, cooling and heating setpoints as potential factors. A major advantage of the method is that it can be flexibly used depending on the data granularity available and, therefore, eliminates a significant barrier that Urban Energy Modelling presents in terms of data availability.},

author = {Kourgiozou, Vasiliki and Al-Saegh, Salam and Korolija, Ivan and Dowson, Mark and Commin, Andrew and Tang, Rui and Rovas, Dimitrios and Mumovic, Dejan},

doi = {10.26868/25222708.2023.1686},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/DVT2AMAX/Kourgiozou et al. - 2023 - Development of a dynamic building stock model for smart energy transition decision support universi.pdf:application/pdf},

language = {en-US},

pages = {3759--3767},

publisher = {IBPSA},

series = {Building {Simulation}},

shorttitle = {Development of a dynamic building stock model for smart energy transition decision support},

title = {Development of a dynamic building stock model for smart energy transition decision support: university campus stock case study},

url = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1686},

urldate = {2026-03-30},

volume = {18},

year = {2023},

bdsk-url-1 = {https://publications.ibpsa.org/conference/paper/?id=bs2023_1686},

bdsk-url-2 = {https://doi.org/10.26868/25222708.2023.1686}}

@article{al-saegh_investigating_2025,

abstract = {The urgency of decarbonizing the built environment requires precise modeling of building stock energy performance for effective large-scale planning and retrofitting. Despite advancements in data and modeling techniques, uncertainties persist in balancing model complexity and accuracy, especially in representing occupancy patterns and their impact on energy demand at district and urban scales. This study examines various approaches to building stock energy simulation and occupancy modeling for district-level heating and cooling energy demand, using 19 buildings at a Central London campus as a case study. Five scenarios were evaluated: Scenario A employs THERMOS, a data-driven approach; Scenario B uses a single dynamic thermal simulation model for the entire inventory; Scenario C applies a thermal model with a uniform occupancy schedule across all buildings; Scenario D uses a thermal model with five distinct occupancy profiles; and Scenario E assigns unique occupancy profiles based on energy use data. Results showed that Scenario E's annual heating demand estimation closely matched metered data (12 \% difference), while Scenario A underestimated by 44 \%. Complex occupancy models improved peak heating load predictions, with Scenario E showing only a 4 \% difference from metered data, though it may not always be feasible due to data and computational constraints. Scenario D emerged as a promising balance between accuracy and efficiency. For cooling demand, significant differences among scenarios (56.43 to 6.1 kWh/m2/Y) underscored the importance of accurate occupancy modeling. This research identifies the optimal balance between model complexity and prediction accuracy, introduces the Energy Data-Driven Occupancy Schedule (EDDOS) method, and highlights the potential of data-driven approaches to enhance energy demand assessments.},

author = {Al-Saegh, Salam and Kourgiozou, Vasiliki and Korolija, Ivan and Tang, Rui and Tahmasebi, Farhang and Mumovic, Dejan},

doi = {10.1016/j.enbuild.2024.115269},

file = {ScienceDirect Snapshot:/Users/pamelafennell/Zotero/storage/EZW6TSSS/S0378778824013859.html:text/html},

issn = {0378-7788},

journal = {Energy and Buildings},

keywords = {Building Stock Energy Simulation, District Energy Systems (DES), District-Level Energy Demand, Dynamic Thermal Simulation, Energy Data-Driven Occupancy Schedule (EDDOS), Energy Demand Assessment, Energy Performance, Heating and Cooling Energy Demand, Occupancy Modelling, Urban Scale Energy Modelling},

month = feb,

pages = {115269},

title = {Investigating building stock energy and occupancy modelling approaches for district-level heating and cooling energy demands estimation in a university campus},

url = {https://www.sciencedirect.com/science/article/pii/S0378778824013859},

urldate = {2026-03-30},

volume = {329},

year = {2025},

bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0378778824013859},

bdsk-url-2 = {https://doi.org/10.1016/j.enbuild.2024.115269}}

@inproceedings{barone_planning_2024,

abstract = {This paper addresses the critical need to reduce CO2 emissions and fossil fuel dependence by promoting renewable energy communities (RECs). A comprehensive methodology for planning, modelling, and evaluating RECs, with a focus on urban contexts, has been developed. The framework utilizes GIS for spatial analysis, urban building energy modelling, and a Monte Carlo method for aggregating RECs. Key steps include selecting district buildings, modelling energy consumption based on archetypal profiles, and assessing integration of renewable energy technologies and storage systems. The framework assesses different virtual building aggregations within the enabling framework of the renewable energy communities (RECs), optimising for self-consumption, self-sufficiency, and life cycle costs. Applied to a case study, the results indicate that urban areas benefit more from multiple smaller RECs rather than a few large ones, enhancing renewable energy usage and reducing life cycle costs. These findings highlight the potential for more efficient and sustainable energy solutions in urban environments through well-planned and optimized RECs.},

author = {Barone, Giovanni and Buonomano, Annamaria and Papa, Gianluca Del and Forzano, Cesare and Giuzio, Giovanni Francesco and Maka, Robert and Palombo, Adolfo and Russo, Giuseppe},

booktitle = {2024 3rd {International} {Conference} on {Energy} {Transition} in the {Mediterranean} {Area} ({SyNERGY} {MED})},

doi = {10.1109/SyNERGYMED62435.2024.10799449},

file = {Snapshot:/Users/pamelafennell/Zotero/storage/YEI7FIAB/10799449.html:text/html},

keywords = {Biological system modeling, Buildings, CO2 Emissions Reduction, Costs, Energy consumption, Energy Demand aggregation, Fossil fuels, Geographic Information System, Monte Carlo methods, Photovoltaic systems, Planning, Renewable Energy Communities, Renewable energy sources, RES and energy storage systems, Urban areas, Urban Building Energy Modeling},

month = oct,

pages = {1--5},

shorttitle = {Planning deep integration of energy communities in urban context},

title = {Planning deep integration of energy communities in urban context: {A} {GIS} approach to optimise renewables, storage systems and demand aggregation},

url = {https://ieeexplore.ieee.org/document/10799449},

urldate = {2026-03-30},

year = {2024},

bdsk-url-1 = {https://ieeexplore.ieee.org/document/10799449},

bdsk-url-2 = {https://doi.org/10.1109/SyNERGYMED62435.2024.10799449}}

@article{deng_simulation-based_2023,

abstract = {Due to the high energy consumption in the building sector and the ever-increasing urbanisation rate, the demand for retrofitting old buildings in urban areas is increasing in China, especially in metropolitan cities like Beijing. However, despite national and local policies calling for extensive building retrofitting, it is a challenge to determine a cost-effective retrofit strategy. Considering this, the study establishes a novel approach that analyses the sensitivity of building energy consumption on parameters defining the materials used on the building envelope, as well as the solar shading and airtightness of the building. This research builds EnergyPlus models using geometric data captured from the map, building fabric data from local design standards, and a set of varying activity schedules, and carries out simulations to calculate the building energy consumption of a residential neighbourhood in Beijing, China. The energy consumption data is then used for a sensitivity analysis using the Morris Method on 14 building envelope parameters in total. For different building shapes, the sensitivity analysis results highlight that the energy is most sensitive to infiltration, followed by window U-value and window SHGC. The solar absorptances and U-values of external walls and roofs are also found to have a moderate influence on total energy consumption. By using predicted weather files, this research further discusses the changing influences of these parameters considering climate change over the next few decades. The approach of this research is instructive for the analysis of buildings in other cities in cold climate regions due to the generalisability of the studied neighbourhood, and the result has the potential to inform the building management teams and policymakers to determine suitable retrofit strategies.},

author = {Deng, Yingqi and Zhou, Yinan and Wang, Hong and Xu, Chen and Wang, Weixiang and Zhou, Tiantian and Liu, Xuan and Liang, Huaqing and Yu, Diran},

doi = {10.1016/j.enbuild.2023.113284},

issn = {0378-7788},

journal = {Energy and Buildings},

keywords = {Building retrofitting, Climate change prediction, Energy simulation, Morris Method, Sensitivity analysis},

month = sep,

pages = {113284},

title = {Simulation-based sensitivity analysis of energy performance applied to an old {Beijing} residential neighbourhood for retrofit strategy optimisation with climate change prediction},

url = {https://www.sciencedirect.com/science/article/pii/S0378778823005145},

urldate = {2026-03-30},

volume = {294},

year = {2023},

bdsk-url-1 = {https://www.sciencedirect.com/science/article/pii/S0378778823005145},

bdsk-url-2 = {https://doi.org/10.1016/j.enbuild.2023.113284}}

@article{matthew_time-use_2023,

abstract = {Achieving emissions reduction targets requires improved energy efficiency to avoid an oversized and excessively expensive electricity network. This can be analysed using hourly demand modelling that captures behaviour profiles, technology types, weather factors and building typologies. Numerous domestic models exist, but whole systems energy modelling, including commercial and industrial demand, are limited by data availability. Time-use survey data has typically been used to model domestic demand- in this work is expanded to also model commercial and industrial electricity-heating for the Scottish islands at an hourly and individual building level. This method is widely applicable for modelling whole system energy demand wherever time-use survey data are available. Combinatorial optimisation has been applied to generate a synthetic population, match individuals to properties and apply construction types to building polygons. SimStock is used for heating and lighting modelling. Validation of the model with 2016 data shows that it reflects longer term trends, with a monthly mean absolute percentage error (MAPE) of 1.6\% and an R2 of 0.99. At the hourly level, the MAPE of 6.2\% and R2 of 0.87 show the model captures variability needed to combine it with a supply-side model. Dataset accuracy, variability in the date recorded, missing data and unknown data correlations are discussed as causes for error. The model can be adapted for other regions and used to analyse the costs and benefits of energy efficiency measures with a supply-side generation model.},

author = {Matthew, Chris and Spataru, Catalina},

copyright = {http://creativecommons.org/licenses/by/3.0/},

doi = {10.3390/en16135057},

file = {Full Text PDF:/Users/pamelafennell/Zotero/storage/4RUQPYNS/Matthew and Spataru - 2023 - Time-Use Data Modelling of Domestic, Commercial and Industrial Electricity Demand for the Scottish I.pdf:application/pdf},

issn = {1996-1073},

journal = {Energies},

keywords = {commercial and industrial, demand forecasting, domestic, electricity demand modelling, hourly demand, Scottish Islands, time-use data},

language = {en},

month = jan,

number = {13},

pages = {5057},

publisher = {Multidisciplinary Digital Publishing Institute},

title = {Time-{Use} {Data} {Modelling} of {Domestic}, {Commercial} and {Industrial} {Electricity} {Demand} for the {Scottish} {Islands}},

url = {https://www.mdpi.com/1996-1073/16/13/5057},

urldate = {2026-03-30},

volume = {16},

year = {2023},

bdsk-url-1 = {https://www.mdpi.com/1996-1073/16/13/5057},

bdsk-url-2 = {https://doi.org/10.3390/en16135057}}