Research Literature

Here you can find papers/reports/websites used or referenced the jEPlus and jEPlus+EA tools. The collection is by no means exhaustive, as it mainly consists of papers from a small number of publishers and dissertation archives. You are much welcome to send us your publications if the tools have benefited your study.

Last updated: 1 Feb 2023

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Talami, R. (2022) The sequential design optimization of building performance. Loughborough University. doi: 10.26174/THESIS.LBORO.21547701.V1. Available at: https://repository.lboro.ac.uk/articles/thesis/The_sequential_design_optimization_of_building_performance/21547701

Bana, A. P. (2022) Reducing simulation performance gap from hempcrete using multi objective optimisation. University of Hertfordshire. doi: 10.18745/TH.25857. Available at: https://doi.org/10.18745/th.25857

Pajek, L. (2022) Energy efficiency of single-family bioclimatic buildings in relation to climate change. University of Ljubljana. Available at: https://repozitorij.uni-lj.si/IzpisGradiva.php?id=136717&lang=slv

Lopes, F. da S. D. (2020) Use of genetic algorithms for optimization of thermal energy performance in buildings in early stage design. Universidade Estadual de Campinas. Available at: https://www.repositorio.unicamp.br/Resultado/Listar?guid=1675119742535.

Botti, A. (2019) The Development of an Early Stage Design Tool to Assess the Risk of Overheating for UK Residential Buildings. University of Surrey. doi: 10.15126/thesis.00850959.

Nelson, J. (2019) Evaluation of the Passive Cooling Potential of Mass Inherent in Medium to Large Commercial Buildings, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2240095551?accountid=14511.

Miao, L. L. (2019) Net Zero Energy Potential and Parametric Analysis for Multiunit Residential Buildings in Toronto, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2323168859?accountid=14511.

Baniassadi, A. (2019) Vulnerability of U.S. Residential Building Stock to Heat: Status Quo, Trends, Mitigation Strategies, and the Role of Energy Efficiency, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2309942211?accountid=14511.

Alothaimeen, I. (2018) Multi-Objective Optimization for LEED: New Construction Using Genetic Algorithms, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2189752285?accountid=14511.

Duran, Ö. (2018) Evaluation of retrofitting strategies for post-war office buildings. Loughborough University. Available at: https://repository.lboro.ac.uk/articles/thesis/Evaluation_of_retrofitting_strategies_for_post-war_office_buildings/9456431.

Hendricken, L. (2018) Regional Energy Simulation Methods: Identifying, Evaluating, and Comparing Methods to Support the Generation of Virtual Building Stocks at the Sub-national Level, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2153854033?accountid=14511.

Schwartz, Y. (2018) An Integrated Thermal Simulation & Generative Design Decision Support Framework for the Refurbishment or Replacement of Buildings: A Life Cycle Performance Optimisation Approach. UCL (University College London). Available at: https://discovery.ucl.ac.uk/id/eprint/10064687 (Accessed: 31 January 2020).

Lim, H. (2017) Prediction of Urban-Scale Building Energy Performance with a Stochastic-Deterministic-Coupled Approach, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1904921523?accountid=14511.

Kamal, R. (2017) Optimization and Performance Study of Select Heating Ventilation and Air Conditioning Technologies for Commercial Buildings, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1889540271?accountid=14511.

Lozinsky, C. (2017) Improving the Characterization of Infiltration and Natural Ventilation Parameters in Whole-Building Energy Models of Multi-Unit Residential Buildings, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1992172731?accountid=14511.

Zhang, L. (2017) Occupant-aware Energy Management: Energy Saving and Comfort Outcomes Achievable Through Application of Cooling Setpoint Adjustments, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2112752927?accountid=14511.

Bingham, R. D. (2017) Optimization of Residential Buildings and Renewable Energy Integration in Small Island Developing States: The Bahamas as a Case Study, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2014467700?accountid=14511.

Garcia, A. O. (2016) EFFECTS OF ARCHITECTURAL DESIGN VARIABLES ON ENERGY AND ENVIRONMENTAL PERFORMANCE OF OFFICE BUILDINGS. Universitat Rovira i Virgili. Available at: https://www.tdx.cat/handle/10803/395212 (Accessed: 30 January 2020).

Rosado, P. J. (2016) Evaluating Cool Impervious Surfaces: Application to an Energy-Efficient Residential Roof and to City Pavements, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1847567942?accountid=14511.

Bae, N. R. (2016) Influence of Uncertainty in User Behaviors on the Simulation-Based Building Energy Optimization Process and Robust Decision-Making, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1874468446?accountid=14511.

Rahmani Asl, M. (2015) A building information model (BIM) based framework for performance optimization, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1739209354?accountid=14511.

Sun, S. (2015) Energy Efficient Buildings: A Method of Probabilistic Risk Assessment Using Building Energy Simulation, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/2066832910?accountid=14511.

Poudel, N. (2014) Towards the development of performance based guidelines for using Phase Change Materials in lightweight buildings, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1552714918?accountid=14511.

Tresidder, E. (2014) Accelerated optimisation methods for low-carbon building design. De Montfort University. Available at: http://hdl.handle.net/2086/10512 (Accessed: 31 January 2020).

Wang, J. (2014) Integrating Acclimated Kinetic Envelopes into sustainable building design, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1650238889?accountid=14511.

DeLarm-Neri, R. (2013) Energy Modeling of Low-Cost Houses in Colder Climates of South Africa. M.Sc Thesis. Appalachian State University. Available at: http://libres.uncg.edu/ir/asu/listing.aspx?id=10129.

Daily, D. R. (2013) TRADE-OFF BASED DESIGN AND IMPLEMENTATION OF ENERGY EFFICIENCY RETROFITS IN RESIDENTIAL HOMES, University of Maryland. Available at: https://search.proquest.com/docview/1560894132?accountid=14511.

Karaguzel, O. T. (2013) Simulation-Based Parametric Analysis of Building Systems Integrative Solar Photovoltaics, ProQuest Dissertations and Theses. Available at: https://search.proquest.com/docview/1460261840?accountid=14511.

Porritt, S. M. (2012) Adapting UK dwellings for heat waves. De Montfort University. Available at: https://dora.dmu.ac.uk/handle/2086/6327 (Accessed: 31 January 2020).

Dingwall, A. (2012) Testing the impact of using cumulative data with genetic algorithms for the analysis of building energy performance and material cost. M.Sc Thesis. Georgia Institute of Technology. Available at: http://hdl.handle.net/1853/45952.

Moret, S. (2012) Energy efficiency in lighting: daylight harvesting optimization and wireless sensor networks. M.Sc Thesis. University of Padova. Available at: http://tesi.cab.unipd.it/40510.

Korolija, I. (2011) Heating, Ventilating and Air-conditioning System Energy Demand Coupling with Building Loads for Office Buildings. Ph.D. Thesis. De Montfort University. Available at: http://hdl.handle.net/2086/5501 (Accessed: 31 January 2020).

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Aghamolaei, R. and Ghaani, M. R. M. R. (2020) ‘Balancing the impacts of energy efficiency strategies on comfort quality of interior places: Application of optimization algorithms in domestic housing’, Journal of Building Engineering, 29, p. 101174. doi: https://doi.org/10.1016/j.jobe.2020.101174.

Alajmi, A., Abou-Ziyan, H. and Al-Mutairi, H. H. (2022) ‘Reassessment of fenestration characteristics for residential buildings in hot climates: energy and economic analysis’, Frontiers in Energy. Higher Education Press Limited Company, 16(4), pp. 629–650. doi: 10.1007/S11708-021-0799-Z/METRICS.

Alkaabi, N. et al. (2020) ‘A data-driven modeling and analysis approach to test the resilience of green buildings to uncertainty in operation patterns’, Energy Science and Engineering, 8(12). doi: 10.1002/ese3.808.

Allesina, G. et al. (2018) ‘A calibration methodology for building dynamic models based on data collected through survey and billings’, Energy and Buildings. Elsevier Ltd, 158, pp. 406–416. doi: 10.1016/j.enbuild.2017.09.089.

Alsharif, R. et al. (2022) ‘Machine learning-based analysis of occupant-centric aspects: Critical elements in the energy consumption of residential buildings’, Journal of Building Engineering, 46, p. 103846. doi: https://doi.org/10.1016/j.jobe.2021.103846.

Azar, E. et al. (2021) ‘Drivers of energy consumption in Kuwaiti buildings: Insights from a hybrid statistical and building performance simulation approach’, Energy Policy, 150, p. 112154. doi: https://doi.org/10.1016/j.enpol.2021.112154.

Azarnejad, A. and Mahdavi, A. (2018) ‘Implications of façades’ visual reflectance for buildings’ thermal performance’, Journal of Building Physics. SAGE Publications Ltd, 42(2), pp. 125–141. Available at: http://journals.sagepub.com/doi/10.1177/1744259117731287 (Accessed: 24 January 2020).

Baba, F. M., Ge, H., Zmeureanu, R., et al. (2022) ‘Calibration of building model based on indoor temperature for overheating assessment using genetic algorithm: Methodology, evaluation criteria, and case study’, Building and Environment, 207, p. 108518. doi: https://doi.org/10.1016/j.buildenv.2021.108518.

Baba, F. M., Ge, H., Wang, L. (Leon), et al. (2022) ‘Do high energy-efficient buildings increase overheating risk in cold climates? Causes and mitigation measures required under recent and future climates’, Building and Environment, 219, p. 109230. doi: https://doi.org/10.1016/j.buildenv.2022.109230.

Baghoolizadeh, M., Rostamzadeh-Renani, M., et al. (2022) ‘A prediction model for CO2 concentration and multi-objective optimization of CO2 concentration and annual electricity consumption cost in residential buildings using ANN and GA’, Journal of Cleaner Production, 379, p. 134753. doi: https://doi.org/10.1016/j.jclepro.2022.134753.

Baghoolizadeh, M., Nadooshan, A. A., et al. (2022) ‘The effect of photovoltaic shading with ideal tilt angle on the energy cost optimization of a building model in European cities’, Energy for Sustainable Development, 71, pp. 505–516. doi: https://doi.org/10.1016/j.esd.2022.10.016.

Baghoolizadeh, M. et al. (2023) ‘Multi-objective optimization of Venetian blinds in office buildings to reduce electricity consumption and improve visual and thermal comfort by NSGA-II’, Energy and Buildings, 278, p. 112639. doi: https://doi.org/10.1016/j.enbuild.2022.112639.

Bandera, C. F. et al. (2020) ‘Photovoltaic Plant Optimization to Leverage Electric Self Consumption by Harnessing Building Thermal Mass’, Sustainability, 12(2), p. 553. doi: 10.3390/su12020553.

Banoczy, E. (2015) ‘Development of simulation-based methodology for Energy Performance Certification of Buildings’, in 2015 5th International Youth Conference on Energy (IYCE). Pisa, Italy: IEEE, pp. 27–30. doi: 10.1109/IYCE.2015.7180733.

Bánóczy, E., Szemes, P. T. and Korondi, P. (2014) ‘Simulation of building renovation’s return in Energy plus’, Environmental Engineering and Management Journal, 13(11), pp. 2743–2748.

Bao, Y., Lee, W. L. and Jia, J. (2021) ‘Probabilistic assessment of overcooling risk for a novel extra-low temperature dedicated outdoor air system for Hong Kong office buildings’, Building Simulation. Tsinghua University, 14(3), pp. 633–648. Available at: https://link.springer.com/10.1007/s12273-020-0684-4 (Accessed: 31 January 2023).

Basurra, S. and Jankovic, L. (2015) ‘Bringing building simulation to a wider audience - A web based simulation and optimisation system’, in 14th International Conference of IBPSA - Building Simulation 2015, BS 2015, Conference Proceedings, pp. 1962–1969.

Becq, A. and Chèze, D. (2021) ‘Influence of mixing valve dynamics and recirculation loop connection to solar tank on large hot water system performances’, Solar Energy, 218, pp. 211–225. doi: https://doi.org/10.1016/j.solener.2021.02.012.

Belleri, A. et al. (2013) ‘A Sensitivity Analysis of Natural Ventilation Design Parameters for Non Residential Buildings’, in BS 2013 - 13th Int. IBPSA Conference. Chambery, France, pp. 3300–3307. Available at: https://www.researchgate.net/publication/256396401_A_sensitivity_analysis_of_natural_ventilation_design_parameters_for_non_residential_buildings.

Belleri, A. and Lollini, R. (2012) ‘Uncertainties in airflow network modelling to support natural ventilation early stage design’, in 33rd AIVC - 2nd Tightvent conference: Optimising Ventilative Cooling and Airtightness for [Nearly] Zero-Energy Buildings, IAQ and Comfort. Copenhagen, Denmark.

Belleri, A., Lollini, R. and Dutton, S. M. (2014) ‘Natural ventilation design: An analysis of predicted and measured performance’, Building and Environment. Elsevier Ltd, 81, pp. 123–138. doi: 10.1016/j.buildenv.2014.06.009.

Bengoetxea, A. et al. (2020) ‘Control strategy optimization of a Stirling based residential hybrid system through multi-objective optimization’, Energy Conversion and Management, 208, p. 112549. doi: https://doi.org/10.1016/j.enconman.2020.112549.

Bingham, R., Agelin-Chaab, M. and Rosen, M. A. (2017) ‘Multi-objective optimization of a residential building envelope in the Bahamas’, in 2017 5th IEEE International Conference on Smart Energy Grid Engineering, SEGE 2017. Institute of Electrical and Electronics Engineers Inc., pp. 294–301. doi: 10.1109/SEGE.2017.8052815.

Boafo, F. E., Kim, J.-T. and Kim, J.-H. (2017) ‘Evaluating the impact of green roof evapotranspiration on annual building energy performance’, International Journal of Green Energy. Taylor & Francis, 14(5), pp. 479–489. doi: 10.1080/15435075.2016.1278375.

Bodach, S., Lang, W. and Auer, T. (2016) ‘Design guidelines for energy-efficient hotels in Nepal’, International Journal of Sustainable Built Environment. Elsevier B.V., 5(2), pp. 411–434. doi: 10.1016/j.ijsbe.2016.05.008.

Bordbari, M. J., Rastegar, M. and Seifi, A. R. (2020) ‘Probabilistic Energy Efficiency Analysis in Buildings Using Statistical Methods’, Iranian Journal of Science and Technology - Transactions of Electrical Engineering. Springer, 44(3), pp. 1133–1145. doi: 10.1007/S40998-019-00288-2/TABLES/9.

Botti, A. et al. (2022) ‘Developing a meta-model for early-stage overheating risk assessment for new apartments in London’, Energy and Buildings, 254, p. 111586. doi: https://doi.org/10.1016/j.enbuild.2021.111586.

Bull, J. et al. (2014) ‘Life cycle cost and carbon footprint of energy efficient refurbishments to 20th century UK school buildings’, International Journal of Sustainable Built Environment. Elsevier B.V., 3(1), pp. 1–17. doi: 10.1016/j.ijsbe.2014.07.002.

Bull, J., Kimpian, J. and Mumovic, D. (2013) ‘Parametric models for predicting life cycle energy, cost, and carbon implications of refurbishment in schools and offices’, in CIBSE Technical Symposium 2013. Liverpool, UK.

Calama-González, C. M. et al. (2021) ‘Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring’, Applied Energy, 282, p. 116118. doi: https://doi.org/10.1016/j.apenergy.2020.116118.

Calama-González, C. M. et al. (2022) ‘Optimal retrofit solutions considering thermal comfort and intervention costs for the Mediterranean social housing stock’, Energy and Buildings, 259, p. 111915. doi: https://doi.org/10.1016/j.enbuild.2022.111915.

Calama-González, C. M., León-Rodríguez, Á. L. and Suárez, R. (2022) ‘Climate change mitigation: thermal comfort improvement in Mediterranean social dwellings through dynamic test cells modelling’, International Journal of Energy and Environmental Engineering. Springer Science and Business Media Deutschland GmbH, pp. 1–14. doi: 10.1007/S40095-022-00498-1/FIGURES/11.

Calama-González, C. M., Suárez, R. and León-Rodríguez, Á. L. (2022) ‘Thermal comfort prediction of the existing housing stock in southern Spain through calibrated and validated parameterized simulation models’, Energy and Buildings, 254, p. 111562. doi: https://doi.org/10.1016/j.enbuild.2021.111562.

Carlucci, S. et al. (2021) ‘On the impact of stochastic modeling of occupant behavior on the energy use of office buildings’, Energy and Buildings, 246, p. 111049. doi: https://doi.org/10.1016/j.enbuild.2021.111049.

Carlucci, S., Pagliano, L. and Sangalli, A. (2014) ‘Statistical analysis of the ranking capability of long-term thermal discomfort indices and their adoption in optimization processes to support building design’, Building and Environment, 75, pp. 114–131. doi: 10.1016/j.buildenv.2013.12.017.

Carreras, J. et al. (2015) ‘Multi-objective optimization of thermal modelled cubicles considering the total cost and life cycle environmental impact’, Energy and Buildings. Elsevier Ltd, 88, pp. 335–346. doi: 10.1016/j.enbuild.2014.12.007.

Carreras, J. et al. (2016) ‘Eco-costs evaluation for the optimal design of buildings with lower environmental impact’, Energy and Buildings. Elsevier Ltd, 119, pp. 189–199. doi: 10.1016/j.enbuild.2016.03.034.

Chen, R. and Tsay, Y.-S. (2022) ‘Carbon emission and thermal comfort prediction model for an office building considering the contribution rate of design parameters’, Energy Reports, 8, pp. 8093–8107. doi: https://doi.org/10.1016/j.egyr.2022.06.012.

Chen, R., Tsay, Y.-S. and Ni, S. (2022) ‘An integrated framework for multi-objective optimization of building performance: Carbon emissions, thermal comfort, and global cost’, Journal of Cleaner Production, 359, p. 131978. doi: https://doi.org/10.1016/j.jclepro.2022.131978.

Chen, X., Yang, H. and Wang, T. (2017) ‘Developing a robust assessment system for the passive design approach in the green building rating scheme of Hong Kong’, Journal of Cleaner Production. Elsevier Ltd, 153, pp. 176–194. doi: 10.1016/j.jclepro.2017.03.191.

Cipriano, J. et al. (2015) ‘Evaluation of a multi-stage guided search approach for the calibration of building energy simulation models’, Energy and Buildings. Elsevier Ltd, 87, pp. 370–385. doi: 10.1016/j.enbuild.2014.08.052.

Cipriano, J. et al. (2016) ‘Development of a dynamic model for natural ventilated photovoltaic components and of a data driven approach to validate and identify the model parameters’, Solar Energy. Elsevier Ltd, 129, pp. 310–331. doi: 10.1016/j.solener.2016.01.039.

Coakley, D., Raftery, P. and Molloy, P. (2012) ‘Calibration of whole building energy simulation models: Detailed case study of a naturally ventilated building using hourly measured data’, in BSO12 - Building Simulation and Optimization Conference. Loughborough, UK.

Costa-Carrapiço, I. et al. (2022) ‘Hygrothermal calibration and validation of vernacular dwellings: A genetic algorithm-based optimisation methodology’, Journal of Building Engineering, 55, p. 104717. doi: https://doi.org/10.1016/j.jobe.2022.104717.

Cruz, A. S. and Cunha, E. G. da (2022) ‘The impact of climate change on the thermal-energy performance of the SCIP and ICF wall systems for social housing in Brazil’, Indoor and Built Environment. SAGE Publications Ltd, 31(3), pp. 838–852. doi: 10.1177/1420326×211038047/ASSET/IMAGES/10.1177_1420326X211038047-IMG2.PNG.

Delač, B. et al. (2022) ‘Integrated optimization of the building envelope and the HVAC system in nZEB refurbishment’, Applied Thermal Engineering, 211, p. 118442. doi: https://doi.org/10.1016/j.applthermaleng.2022.118442.

Delgarm, Navid et al. (2016) ‘A novel approach for the simulation-based optimization of the buildings energy consumption using NSGA-II: Case study in Iran’, Energy and Buildings. Elsevier Ltd, 127, pp. 552–560. doi: 10.1016/j.enbuild.2016.05.052.

Delgarm, N. et al. (2016) ‘Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)’, Applied Energy. Elsevier Ltd, 170, pp. 293–303. doi: 10.1016/j.apenergy.2016.02.141.

Delgarm, N., Sajadi, B. and Delgarm, S. (2016) ‘Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC)’, Energy and Buildings. Elsevier Ltd, 131, pp. 42–53. doi: 10.1016/j.enbuild.2016.09.003.

Djemame, K. et al. (2017) ‘Energy efficiency support through intra-layer cloud stack adaptation’, in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Verlag, pp. 129–143. Available at: https://link.springer.com/chapter/10.1007/978-3-319-61920-0_10 (Accessed: 24 January 2020).

Du, Y., Zhou, Z. and Zhao, J. (2022) ‘Multi-regional building energy efficiency intelligent regulation strategy based on multi-objective optimization and model predictive control’, Journal of Cleaner Production, 349, p. 131264. doi: https://doi.org/10.1016/j.jclepro.2022.131264.

Duran, Ö., Taylor, S. C. and Lomas, K. J. (2015) ‘Evaluation of refurbishment strategies for Post-War office buildings’, in 14th International Conference of IBPSA - Building Simulation 2015, BS 2015, Conference Proceedings, pp. 138–145.

Duran, Özlem, Taylor, S. and Lomas, K. (2015) ‘The Impact of Refurbishment on Thermal Comfort in Post-war Office Buildings’, Energy Procedia, 78, pp. 877–882. doi: 10.1016/j.egypro.2015.11.011.

Faramarzi, A. et al. (2020) ‘Marine Predators Algorithm: A nature-inspired metaheuristic’, Expert Systems with Applications, 152, p. 113377. doi: https://doi.org/10.1016/j.eswa.2020.113377.

Fennell, P., Ruyssevelt, P. and Smith, A. (2016) ‘Energy Performance Contracting - Is it time to check the small print?’, In: Proceedings of the 4th European Conference on Behaviour and Energy Efficiency (BEHAVE 2016). European Conference on Behaviour and Energy Efficiency: Coimbra, Portugal. (2016). European Conference on Behaviour and Energy Efficiency. Available at: https://discovery.ucl.ac.uk/id/eprint/1542172/ (Accessed: 31 January 2020).

Fernández Bandera, C. et al. (2018) ‘Exergy As a Measure of Sustainable Retrofitting of Buildings’, Energies, 11(11), p. 3139. doi: 10.3390/en11113139.

Fernández Bandera, C. and Ramos Ruiz, G. (2017) ‘Towards a New Generation of Building Envelope Calibration’, Energies, 10(12), p. 2102. doi: 10.3390/en10122102.

Gallardo, A. and Berardi, U. (2021) ‘Design and control of radiant ceiling panels incorporating phase change materials for cooling applications’, Applied Energy, 304, p. 117736. doi: https://doi.org/10.1016/j.apenergy.2021.117736.

Gao, B. et al. (2023) ‘Multi-objective optimization of energy-saving measures and operation parameters for a newly retrofitted building in future climate conditions: A case study of an office building in Chengdu’, Energy Reports, 9, pp. 2269–2285. doi: https://doi.org/10.1016/j.egyr.2023.01.049.

García Kerdan, I. et al. (2017a) ‘ExRET-Opt: An automated exergy/exergoeconomic simulation framework for building energy retrofit analysis and design optimisation’, Applied Energy. Elsevier Ltd, 192, pp. 33–58. doi: 10.1016/j.apenergy.2017.02.006.

García Kerdan, I. et al. (2017b) ‘The role of an exergy-based building stock model for exploration of future decarbonisation scenarios and policy making’, Energy Policy. Elsevier Ltd, 105, pp. 467–483. doi: 10.1016/j.enpol.2017.03.020.

de Gastines, M. and Pattini, A. E. A. E. A. E. (2020) ‘Window energy efficiency in Argentina - Determining factors and energy savings strategies’, Journal of Cleaner Production, 247, p. 119104. doi: https://doi.org/10.1016/j.jclepro.2019.119104.

Giannakis, G; Kontes, G; Korolija, I; Rovas, D. (2017) ‘Simulation-time reduction techniques for a retrofit planning tool’, in 15th International Conference of IBPSA. San Francisco, USA. doi: 10.26868/25222708.2017.554.

Gilan, S. S., Goyal, N. and Dilkina, B. (2016) ‘Active learning in multi-objective evolutionary algorithms for sustainable building design’, in GECCO 2016 - Proceedings of the 2016 Genetic and Evolutionary Computation Conference, pp. 589–596. doi: 10.1145/2908812.2908947.

Gilan, S. S. S. S. and Dilkina, B. (2015) Sustainable Building Design: A Challenge at the Intersection of Machine Learning and Design Optimization, AAAI Workshop - Technical Report. Available at: http://www.aaai.org/ocs/index.php/WS/AAAIW15/paper/view/10203/10183.

Di Giuseppe, E. (2019) ‘A parametric building design tool for assessing energy savings and life cycle costs’, Proceedings of the Institution of Civil Engineers - Engineering Sustainability, 172(6), pp. 283–292. doi: 10.1680/jensu.17.00062.

Giuseppe, E. Di, Massi, A. and D’Orazio, M. (2017) ‘Impacts of Uncertainties in Life Cycle Cost Analysis of Buildings Energy Efficiency Measures: Application to a Case Study’, in Energy Procedia. Elsevier Ltd, pp. 442–451. doi: 10.1016/j.egypro.2017.03.206.

Gokarakonda, S., van Treeck, C. and Rawal, R. (2019) ‘Influence of building design and control parameters on the potential of mixed-mode buildings in India’, Building and Environment. Elsevier Ltd, 148, pp. 157–172. doi: 10.1016/j.buildenv.2018.10.043.

Gomes, R. et al. (2021) ‘Retrofit measures evaluation considering thermal comfort using building energy simulation: two Lisbon households’, Advances in Building Energy Research. Taylor & Francis, 15(3), pp. 291–314. doi: 10.1080/17512549.2018.1520646.

Goncalves, V., Ogunjimi, Y. and Heo, Y. (2021) ‘Scrutinizing modeling and analysis methods for evaluating overheating risks in passive houses’, Energy and Buildings, 234, p. 110701. doi: https://doi.org/10.1016/j.enbuild.2020.110701.

González, V. G. and Bandera, C. F. (2022) ‘A building energy models calibration methodology based on inverse modelling approach’, Building Simulation. Tsinghua University, 15(11), pp. 1883–1898. doi: 10.1007/S12273-022-0900-5/METRICS.

Gou, S. et al. (2018) ‘Passive design optimization of newly-built residential buildings in Shanghai for improving indoor thermal comfort while reducing building energy demand’, Energy and Buildings. Elsevier Ltd, 169, pp. 484–506. doi: 10.1016/j.enbuild.2017.09.095.

Green, A. et al. (2020) ‘Above-roof air temperature effects on HVAC and cool roof performance: Experiments and development of a predictive model’, Energy and Buildings, 222, p. 110071. doi: https://doi.org/10.1016/j.enbuild.2020.110071.

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