Research Publications where jEPlus Tools Were Referenced/Reviewed

Here you can find publications that referenced or reviewed jEPlus and jEPlus+EA tools. You are welcome to send us your publications if these tools were referenced.

Last updated: 1 Feb 2023

Abdelrahman, M. M. et al. (2021) ‘Data science for building energy efficiency: A comprehensive text-mining driven review of scientific literature’, Energy and Buildings, 242, p. 110885. doi: https://doi.org/10.1016/j.enbuild.2021.110885.

Abdou, N. et al. (2021) ‘Multi-objective optimization of passive energy efficiency measures for net-zero energy building in Morocco’, Building and Environment, 204, p. 108141. doi: https://doi.org/10.1016/j.buildenv.2021.108141.

Abdou, N. et al. (2022) ‘Prediction and optimization of heating and cooling loads for low energy buildings in Morocco: An application of hybrid machine learning methods’, Journal of Building Engineering, 61, p. 105332. doi: https://doi.org/10.1016/j.jobe.2022.105332.

Acar, U., Kaska, O. and Tokgoz, N. (2021) ‘Multi-objective optimization of building envelope components at the preliminary design stage for residential buildings in Turkey’, Journal of Building Engineering, 42, p. 102499. doi: https://doi.org/10.1016/j.jobe.2021.102499.

Agdas, D. and Srinivasan, R. S. (2015) ‘Building energy simulation and parallel computing: Opportunities and challenges’, in Proceedings - Winter Simulation Conference, pp. 3167–3175. doi: 10.1109/WSC.2014.7020153.

Ali, U. et al. (2019) ‘A data-driven approach for multi-scale building archetypes development’, Energy and Buildings. Elsevier Ltd, 202, p. 109364. doi: 10.1016/j.enbuild.2019.109364.

de Almeida Rocha, A. P. et al. (2020) ‘A pixel counting based method for designing shading devices in buildings considering energy efficiency, daylight use and fading protection’, Applied Energy, 262, p. 114497. doi: https://doi.org/10.1016/j.apenergy.2020.114497.

Alsagri, A. S., Alrobaian, A. A. and Nejlaoui, M. (2021) ‘Techno-economic evaluation of an off-grid health clinic considering the current and future energy challenges: A rural case study’, Renewable Energy, 169, pp. 34–52. doi: https://doi.org/10.1016/j.renene.2021.01.017.

Annibaldi, V. et al. (2020) ‘An integrated sustainable and profitable approach of energy efficiency in heritage buildings’, Journal of Cleaner Production, 251, p. 119516. doi: https://doi.org/10.1016/j.jclepro.2019.119516.

Asl, M. R., Zarrinmehr, S. and Yan, W. (2013) ‘Towards BIM-based parametric building energy performance optimization’, in ACADIA 2013: Adaptive Architecture - Proceedings of the 33rd Annual Conference of the Association for Computer Aided Design in Architecture, pp. 101–108. Available at: https://www.researchgate.net/publication/283351549_Towards_BIM-based_Parametric_Building_Energy_Performance_Optimization.

Attia, S. et al. (2012) ‘Simulation-based decision support tool for early stages of zero-energy building design’, Energy and Buildings, 49(0), pp. 2–15. doi: http://dx.doi.org/10.1016/j.enbuild.2012.01.028.

Attia, Shady, De Herde, A., et al. (2013) ‘Achieving informed decision-making for net zero energy buildings design using building performance simulation tools’, Building Simulation. Tsinghua Press, 6(1), pp. 3–21. doi: 10.1007/s12273-013-0105-z.

Attia, Shady, Hamdy, M., et al. (2013) ‘Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design’, Energy and Buildings, 60, pp. 110–124. doi: 10.1016/j.enbuild.2013.01.016.

Attia, S. et al. (2013) ‘Computational optimisation for zero energy buildings design: Interviews results with twenty eight international experts’, in Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pp. 3698–3705.

Attia, S. (2018) Net Zero Energy Buildings (NZEB): Concepts, frameworks and roadmap for project analysis and implementation, Net Zero Energy Buildings (NZEB): Concepts, Frameworks and Roadmap for Project Analysis and Implementation. Elsevier. doi: 10.1016/C2016-0-03166-2.

Ball, B. L. et al. (2020) ‘An open source analysis framework for large-scale building energy modeling’, Journal of Building Performance Simulation, 13(5), pp. 487–500. doi: 10.1080/19401493.2020.1778788.

Barber, K. A. and Krarti, M. (2022) ‘A review of optimization based tools for design and control of building energy systems’, Renewable and Sustainable Energy Reviews, 160, p. 112359. doi: https://doi.org/10.1016/j.rser.2022.112359.

Carlucci, S., Hamdy, M. and Moazami, A. (2018) ‘Challenges in the modeling and simulation of green buildings’, in Handbook of Energy Systems in Green Buildings. Springer Berlin Heidelberg, pp. 3–34. doi: 10.1007/978-3-662-49120-1_50.

Carpino, C. et al. (2022) ‘Improve decision-making process and reduce risks in the energy retrofit of existing buildings through uncertainty and sensitivity analysis’, Energy for Sustainable Development, 68, pp. 289–307. doi: https://doi.org/10.1016/j.esd.2022.04.007.

Carratt, A., Kokogiannakis, G. and Daly, D. (2020) ‘A critical review of methods for the performance evaluation of passive thermal retrofits in residential buildings’, Journal of Cleaner Production, 263, p. 121408. doi: https://doi.org/10.1016/j.jclepro.2020.121408.

Casini, M. (2022) ‘Chapter 6 - Advanced digital design tools and methods’, in Casini, M. B. T.-C. 4. . (ed.) Woodhead Publishing Series in Civil and Structural Engineering. Woodhead Publishing, pp. 263–334. doi: https://doi.org/10.1016/B978-0-12-821797-9.00009-X.

Cecconi, F. R. et al. (2017) ‘Probabilistic behavioral modeling in building performance simulation: A Monte Carlo approach’, Energy and Buildings. Elsevier Ltd, 148, pp. 128–141. doi: 10.1016/j.enbuild.2017.05.013.

Chegari, B. et al. (2021) ‘Multi-objective optimization of building energy performance and indoor thermal comfort by combining artificial neural networks and metaheuristic algorithms’, Energy and Buildings, 239, p. 110839. doi: https://doi.org/10.1016/j.enbuild.2021.110839.

Chen, B. et al. (2021) ‘Multiobjective optimization of building energy consumption based on BIM-DB and LSSVM-NSGA-II’, Journal of Cleaner Production, 294, p. 126153. doi: https://doi.org/10.1016/j.jclepro.2021.126153.

Chen, K. W., Choo, T. S. and Norford, L. (2019) ‘Enabling algorithm-assisted architectural design exploration for computational design novices’, Computer-Aided Design and Applications. CAD Solutions, LLC, 16(2), pp. 269–288. doi: 10.14733/cadaps.2019.269-288.

Chong, A., Gu, Y. and Jia, H. (2021) ‘Calibrating building energy simulation models: A review of the basics to guide future work’, Energy and Buildings, 253, p. 111533. doi: https://doi.org/10.1016/j.enbuild.2021.111533.

Cortés, A. et al. (2014) ‘Big Data Technology to Exploit Climate Information/Consumption Models and to Predict Future Behaviours’, in González Alonso, I. (ed.) International Technology Robotics Applications SE - 3. Springer International Publishing (Intelligent Systems, Control and Automation: Science and Engineering), pp. 25–36. doi: 10.1007/978-3-319-02332-8_3.

Costa-Carrapiço, I., Raslan, R. and González, J. N. J. N. (2020) ‘A systematic review of genetic algorithm-based multi-objective optimisation for building retrofitting strategies towards energy efficiency’, Energy and Buildings, 210, p. 109690. doi: https://doi.org/10.1016/j.enbuild.2019.109690.

D’Agostino, D. et al. (2021) ‘Proposal of a new automated workflow for the computational performance-driven design optimization of building energy need and construction cost’, Energy and Buildings, 239, p. 110857. doi: https://doi.org/10.1016/j.enbuild.2021.110857.

Debrah, C., Chan, A. P. C. and Darko, A. (2022) ‘Artificial intelligence in green building’, Automation in Construction, 137, p. 104192. doi: https://doi.org/10.1016/j.autcon.2022.104192.

Du, T. et al. (2020) ‘Gaps and requirements for automatic generation of space layouts with optimised energy performance’, Automation in Construction, 116, p. 103132. doi: https://doi.org/10.1016/j.autcon.2020.103132.

Eisenhower, B. et al. (2011) ‘Uncertainty and sensitivity decomposition of building energy models’, Journal of Building Performance Simulation. Taylor & Francis, 5(3), pp. 171–184. doi: 10.1080/19401493.2010.549964.

Elkadeem, M. R. et al. (2021) ‘Feasibility analysis and optimization of an energy-water-heat nexus supplied by an autonomous hybrid renewable power generation system: An empirical study on airport facilities’, Desalination, 504, p. 114952. doi: https://doi.org/10.1016/j.desal.2021.114952.

Elmorshedy, M. F. et al. (2022) ‘Feasibility study and performance analysis of microgrid with 100% hybrid renewables for a real gricultural irrigation application’, Sustainable Energy Technologies and Assessments, 53, p. 102746. doi: https://doi.org/10.1016/j.seta.2022.102746.

Elsheikh, A., Motawa, I. and Diab, E. (2021) ‘Multi-objective genetic algorithm optimization model for energy efficiency of residential building envelope under different climatic conditions in Egypt’, International Journal of Construction Management. Taylor & Francis, pp. 1–10. doi: 10.1080/15623599.2021.1966709.

Feng, F. et al. (2021) ‘A critical review of fenestration/window system design methods for high performance buildings’, Energy and Buildings, 248, p. 111184. doi: https://doi.org/10.1016/j.enbuild.2021.111184.

Foda, E., El-Hamalawi, A. and Le Dréau, J. (2020) ‘Computational analysis of energy and cost efficient retrofitting measures for the French house’, Building and Environment, 175, p. 106792. doi: https://doi.org/10.1016/j.buildenv.2020.106792.

Forde, J. et al. (2020) ‘Temporal optimization for affordable and resilient Passivhaus dwellings in the social housing sector’, Applied Energy, 261, p. 114383. doi: https://doi.org/10.1016/j.apenergy.2019.114383.

García Kerdan, I. and Morillón Gálvez, D. (2022) ‘ANNEXE: An open-source building energy design optimisation framework using artificial neural networks and genetic algorithms’, Journal of Cleaner Production, 371, p. 133500. doi: https://doi.org/10.1016/j.jclepro.2022.133500.

Garg, V. et al. (2010) ‘Energyplus simulation speedup using data parallelization concept’, in ASME 2010 4th International Conference on Energy Sustainability, ES 2010, pp. 1041–1047. doi: 10.1115/ES2010-90509.

Garg, V. et al. (2011) ‘Development and performance evaluation of a methodology, based on distributed computing, for speeding EnergyPlus simulation’, Journal of Building Performance Simulation, 4(3), pp. 257–270. doi: 10.1080/19401493.2010.531142.

Garg, V. et al. (2014) ‘Development and analysis of a tool for speed up of EnergyPlus through parallelization’, Journal of Building Performance Simulation, 7(3), pp. 179–191. doi: 10.1080/19401493.2013.808264.

Ghalambaz, M., Jalilzadeh Yengejeh, R. and Davami, A. H. (2021) ‘Building energy optimization using Grey Wolf Optimizer (GWO)’, Case Studies in Thermal Engineering, 27, p. 101250. doi: https://doi.org/10.1016/j.csite.2021.101250.

Gordillo, G. C. G. C. et al. (2020) ‘EplusLauncher: An API to Perform Complex EnergyPlus Simulations in MATLAB® and C#’, 12(2), p. 672. doi: 10.3390/su12020672.

Hamdy, M. and Sirén, K. (2015) ‘A multi-aid optimization scheme for large-scale investigation of cost-optimality and energy performance of buildings’, Journal of Building Performance Simulation. Taylor & Francis, 9(4), pp. 1–20. doi: 10.1080/19401493.2015.1069398.

Han, T. et al. (2018) ‘Simulation-Based Decision Support Tools in the Early Design Stages of a Green Building—A Review’, Sustainability, 10(10), p. 3696. doi: 10.3390/su10103696.

Ho, A. M. Y., Lai, J. H. K. and Chiu, B. W. Y. (2021) ‘Key performance indicators for holistic evaluation of building retrofits: Systematic literature review and focus group study’, Journal of Building Engineering, 43, p. 102926. doi: https://doi.org/10.1016/j.jobe.2021.102926.

Hygh, J. S. et al. (2012) ‘Multivariate regression as an energy assessment tool in early building design’, Building and Environment, 57, pp. 165–175. doi: 10.1016/j.buildenv.2012.04.021.

Jankovic, L. (2014) Energy and comfort modelling tools, Sustainable Retrofitting of Commercial Buildings: Cool Climates. doi: 10.4324/9781315765877.

Jia, H. and Chong, A. (2021) ‘eplusr: A framework for integrating building energy simulation and data-driven analytics’, Energy and Buildings, 237, p. 110757. doi: https://doi.org/10.1016/j.enbuild.2021.110757.

Jiang, S., Wang, M. and Ma, L. (2023) ‘Gaps and requirements for applying automatic architectural design to building renovation’, Automation in Construction, 147, p. 104742. doi: https://doi.org/10.1016/j.autcon.2023.104742.

Jin, Q. and Overend, M. (2014) ‘A prototype whole-life value optimization tool for façade design’, Journal of Building Performance Simulation, 7(3), pp. 217–232. doi: 10.1080/19401493.2013.812145.

Kang, S. et al. (2018) ‘Automated processes of estimating the heating and cooling load for building envelope design optimization’, Building Simulation. Tsinghua University Press, 11(2), pp. 219–233. Available at: https://link.springer.com/article/10.1007/s12273-017-0389-5 (Accessed: 24 January 2020).

Kheiri, F. (2018) ‘A review on optimization methods applied in energy-efficient building geometry and envelope design’, Renewable and Sustainable Energy Reviews. Elsevier Ltd, pp. 897–920. doi: 10.1016/j.rser.2018.04.080.

Lee, B. D. et al. (2013) ‘Towards better prediction of building performance: A workbench to analyze uncertainty in building simulation’, in Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pp. 1231–1238.

Lee, S. H., Hong, T., Piette, M. A., et al. (2015) ‘Accelerating the energy retrofit of commercial buildings using a database of energy efficiency performance’, Energy. Elsevier Ltd, 90, pp. 738–747. doi: 10.1016/j.energy.2015.07.107.

Lee, S. H., Hong, T., Sawaya, G., et al. (2015) ‘DEEP: A Database of Energy Efficiency Performance to Accelerate Energy Retrofitting of Commercial Buildings’, in ASHRAE Winter Conference 2015.

Li, H., De Wilde, P. and Rafiq, Y. (2014) ‘A methodology for building performance simulation using high power computing’, in EG-ICE 2011, European Group for Intelligent Computing in Engineering. Available at: https://www.researchgate.net/publication/259706512_A_methodology_for_building_performance_simulation_using_high_power_computing.

Li, W. et al. (2020) ‘A novel operation approach for the energy efficiency improvement of the HVAC system in office spaces through real-time big data analytics’, Renewable and Sustainable Energy Reviews, 127, p. 109885. doi: https://doi.org/10.1016/j.rser.2020.109885.

Liu, H. et al. (2021) ‘Impacts of green roofs on water, temperature, and air quality: A bibliometric review’, Building and Environment, 196, p. 107794. doi: https://doi.org/10.1016/j.buildenv.2021.107794.

Liu, P. and Li, Y. (2023) ‘Ecological technology of green building in the initial stage of design based on BIM technology’, Journal of Experimental Nanoscience. Taylor & Francis, 18(1), p. 2170355. doi: 10.1080/17458080.2023.2170355.

Maglad, A. M. et al. (2023) ‘Bim-based energy analysis and optimization using insight 360 (case study)’, Case Studies in Construction Materials, 18, p. e01755. doi: https://doi.org/10.1016/j.cscm.2022.e01755.

Mah, A. X. Y. et al. (2021) ‘Optimization of a standalone photovoltaic-based microgrid with electrical and hydrogen loads’, Energy, 235, p. 121218. doi: https://doi.org/10.1016/j.energy.2021.121218.

Manfren, M. (2017) ‘Multi-Scale Computing for a Sustainable Built Environment’, in Smart Cities: Foundations, Principles, and Applications. wiley, pp. 53–97. doi: 10.1002/9781119226444.ch3.

Mao, J. et al. (2018) ‘Optimization-aided calibration of an urban microclimate model under uncertainty’, Building and Environment. Elsevier Ltd, 143, pp. 390–403. doi: 10.1016/j.buildenv.2018.07.034.

Mostafavi, F., Tahsildoost, M. and Zomorodian, Z. (2021) ‘Energy efficiency and carbon emission in high-rise buildings: A review (2005-2020)’, Building and Environment, 206, p. 108329. doi: https://doi.org/10.1016/j.buildenv.2021.108329.

Naboni, E., Nielsen, J. and Maccarini, A. (2013) ‘AUTARKI: Coupling a 1:1 cross laminated timber building prototype with parametric energy simulation to investigate scenarios for energy self-sufficiency’, in Prototyping Architecture. London, UK, pp. 245–261. Available at: http://www.e3lab.org/upl/website/publication1111/Autarkipaper2.pdf.

Naji, S., Aye, L. and Noguchi, M. (2021) ‘Multi-objective optimisations of envelope components for a prefabricated house in six climate zones’, Applied Energy, 282, p. 116012. doi: https://doi.org/10.1016/j.apenergy.2020.116012.

Nguyen, A.-T. T., Reiter, S. and Rigo, P. (2014) ‘A review on simulation-based optimization methods applied to building performance analysis’, Applied Energy. Elsevier Ltd, 113, pp. 1043–1058. doi: 10.1016/j.apenergy.2013.08.061.

Østergård, T., Jensen, R. L. and Maagaard, S. E. (2016) ‘Building simulations supporting decision making in early design - A review’, Renewable and Sustainable Energy Reviews. Elsevier Ltd, pp. 187–201. doi: 10.1016/j.rser.2016.03.045.

Ozarisoy, B. and Altan, H. (2022) ‘Bridging the energy performance gap of social housing stock in south-eastern Mediterranean Europe: Climate change and mitigation’, Energy and Buildings, 258, p. 111687. doi: https://doi.org/10.1016/j.enbuild.2021.111687.

Palonen, M., Hamdy, M. and Hasan, A. (2013) ‘Mobo a new software for multi-objective building performance optimization’, in Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pp. 2567–2574. Available at: https://www.researchgate.net/publication/258028589_MOBO_a_new_software_for_multi-objective_building_performance_optimization.

Pang, Z. et al. (2020) ‘The role of sensitivity analysis in the building performance analysis: A critical review’, Energy and Buildings. Elsevier Ltd, 209, p. 109659. doi: https://doi.org/10.1016/j.enbuild.2019.109659.

Rabani, M. et al. (2020) ‘Minimizing delivered energy and life cycle cost using Graphical script: An office building retrofitting case’, Applied Energy, 268, p. 114929. doi: https://doi.org/10.1016/j.apenergy.2020.114929.

Rabani, M., Bayera Madessa, H. and Nord, N. (2021) ‘Achieving zero-energy building performance with thermal and visual comfort enhancement through optimization of fenestration, envelope, shading device, and energy supply system’, Sustainable Energy Technologies and Assessments, 44, p. 101020. doi: https://doi.org/10.1016/j.seta.2021.101020.

Rahmani Asl, M. et al. (2015) ‘BPOpt: A framework for BIM-based performance optimization’, Energy and Buildings. Elsevier Ltd, 108, pp. 401–412. doi: 10.1016/j.enbuild.2015.09.011.

Ram, K. et al. (2022) ‘Critical assessment on application of software for designing hybrid energy systems’, Materials Today: Proceedings, 49, pp. 425–432. doi: https://doi.org/10.1016/j.matpr.2021.02.452.

Razmi, A., Rahbar, M. and Bemanian, M. (2022) ‘PCA-ANN integrated NSGA-III framework for dormitory building design optimization: Energy efficiency, daylight, and thermal comfort’, Applied Energy, 305, p. 117828. doi: https://doi.org/10.1016/j.apenergy.2021.117828.

Richman, R., Zirnhelt, H. and Fix, S. (2014) ‘Large-scale building simulation using cloud computing for estimating lifecycle energy consumption’, Canadian Journal of Civil Engineering, 41(3), pp. 252–262. doi: 10.1139/cjce-2013-0235.

Rikkas, R. and Lahdelma, R. (2021) ‘Energy supply and storage optimization for mixed-type buildings’, Energy, 231, p. 120839. doi: https://doi.org/10.1016/j.energy.2021.120839.

Roudsari, M. S. and Pak, M. (2013) ‘Ladybug: A parametric environmental plugin for grasshopper to help designers create an environmentally-conscious design’, in Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pp. 3128–3135. Available at: https://www.researchgate.net/publication/287778694_Ladybug_A_parametric_environmental_plugin_for_grasshopper_to_help_designers_create_an_environmentally-conscious_design.

Roy, D., Hassan, R. and Das, B. K. (2022) ‘A hybrid renewable-based solution to electricity and freshwater problems in the off-grid Sundarbans region of India: Optimum sizing and socio-enviro-economic evaluation’, Journal of Cleaner Production, 372, p. 133761. doi: https://doi.org/10.1016/j.jclepro.2022.133761.

Ruiz, G. and Bandera, C. (2017) ‘Validation of Calibrated Energy Models: Common Errors’, Energies, 10(10), p. 1587. doi: 10.3390/en10101587.

Sha, H. et al. (2022) ‘Development of a key-variable-based parallel HVAC energy predictive model’, Building Simulation. Tsinghua University, 15(7), pp. 1193–1208. doi: 10.1007/S12273-021-0885-0/METRICS.

Shao, T. et al. (2022) ‘Multi-objective optimization design for rural houses in western zones of China’, Architectural Science Review. Taylor & Francis, 65(4), pp. 260–277. doi: 10.1080/00038628.2022.2040412.

Silva, A. S. and Ghisi, E. (2020) ‘Estimating the sensitivity of design variables in the thermal and energy performance of buildings through a systematic procedure’, Journal of Cleaner Production, 244, p. 118753. doi: https://doi.org/10.1016/j.jclepro.2019.118753.

Singh Rajput, T. and Thomas, A. (2022) ‘Optimizing passive design strategies for energy efficient buildings using hybrid artificial neural network (ANN) and multi-objective evolutionary algorithm through a case study approach’, International Journal of Construction Management. Taylor & Francis, pp. 1–13. doi: 10.1080/15623599.2022.2056409.

Song, J. et al. (2022) ‘Framework on low-carbon retrofit of rural residential buildings in arid areas of northwest China: A case study of Turpan residential buildings’, Building Simulation. Tsinghua University, 16(2), pp. 279–297. doi: 10.1007/S12273-022-0941-9/METRICS.

Stuart, G., Korolija, I. and Marjanovic-Halburd, L. (2012) ‘Navigating multi-dimensional results from large parametric building simulation studies’, in CIBSE ASHRAE Technical Symposium 2012. London, UK. Available at: http://www.cibse.org/content/cibsesymposium2012/Paper093.pdf.

Tavakolan, M. et al. (2022) ‘A parallel computing simulation-based multi-objective optimization framework for economic analysis of building energy retrofit: A case study in Iran’, Journal of Building Engineering, 45, p. 103485. doi: https://doi.org/10.1016/j.jobe.2021.103485.

Taveres-Cachat, E. and Goia, F. (2021) ‘Exploring the impact of problem formulation in numerical optimization: A case study of the design of PV integrated shading systems’, Building and Environment, 188, p. 107422. doi: https://doi.org/10.1016/j.buildenv.2020.107422.

Tian, Z. C. et al. (2015) ‘Building Energy Optimization Tools and Their Applicability in Architectural Conceptual Design Stage’, Energy Procedia, 78, pp. 2572–2577. doi: 10.1016/j.egypro.2015.11.288.

Toopshekan, A., Yousefi, H. and Astaraei, F. R. (2020) ‘Technical, economic, and performance analysis of a hybrid energy system using a novel dispatch strategy’, Energy, 213, p. 118850. doi: https://doi.org/10.1016/j.energy.2020.118850.

Vaziri Rad, M. A. et al. (2020) ‘A comprehensive study of techno-economic and environmental features of different solar tracking systems for residential photovoltaic installations’, Renewable and Sustainable Energy Reviews, 129, p. 109923. doi: https://doi.org/10.1016/j.rser.2020.109923.

Lo Verso, V. R. M., Pellegrino, A. and Pellerey, F. (2014) ‘A multivariate non-linear regression model to predict the energy demand for lighting in rooms with different architectural features and lighting control systems’, Energy and Buildings, 76, pp. 151–163. doi: 10.1016/j.enbuild.2014.02.063.

Viola, A. and Roudsari, M. S. (2013) ‘An innovative workflow for bridging the gap between design and environmental analysis’, in Proceedings of BS 2013: 13th Conference of the International Building Performance Simulation Association, pp. 1297–1304. Available at: https://www.researchgate.net/publication/279713961_An_innovative_workflow_for_bridging_the_gap_between_design_and_environmental_analysis.

Vukadinović, A. et al. (2021) ‘Multi-objective optimization of energy performance for a detached residential building with a sunspace using the NSGA-II genetic algorithm’, Solar Energy, 224, pp. 1426–1444. doi: https://doi.org/10.1016/j.solener.2021.06.082.

Wei, M. et al. (2021) ‘Approaches to cost-effective near-net zero energy new homes with time-of-use value of energy and battery storage’, Advances in Applied Energy, 2, p. 100018. doi: https://doi.org/10.1016/j.adapen.2021.100018.

Wortmann, T., Cichocka, J. and Waibel, C. (2022) ‘Simulation-based optimization in architecture and building engineering—Results from an international user survey in practice and research’, Energy and Buildings, 259, p. 111863. doi: https://doi.org/10.1016/j.enbuild.2022.111863.

Wu, B., Cai, W. and Chen, H. (2021) ‘A model-based multi-objective optimization of energy consumption and thermal comfort for active chilled beam systems’, Applied Energy, 287, p. 116531. doi: https://doi.org/10.1016/j.apenergy.2021.116531.

Xu, W. et al. (2016) ‘Improving evolutionary algorithm performance for integer type multi-objective building system design optimization’, Energy and Buildings. Elsevier Ltd, 127, pp. 714–729. doi: 10.1016/j.enbuild.2016.06.043.

Yang, D. et al. (2020) ‘Dynamic and interactive re-formulation of multi-objective optimization problems for conceptual architectural design exploration’, Automation in Construction, 118, p. 103251. doi: https://doi.org/10.1016/j.autcon.2020.103251.

Yu, F., Wennersten, R. and Leng, J. (2020) ‘A state-of-art review on concepts, criteria, methods and factors for reaching “thermal-daylighting balance”’, Building and Environment, 186, p. 107330. doi: https://doi.org/10.1016/j.buildenv.2020.107330.

Zemero, B. R. et al. (2019) ‘Methodology for Preliminary Design of Buildings Using Multi-Objective Optimization Based on Performance Simulation’, Journal of Solar Energy Engineering, Transactions of the ASME. American Society of Mechanical Engineers (ASME), 141(4). doi: 10.1115/1.4042244.

Zhu, C. et al. (2020) ‘Uncertainty calibration of building energy models by combining approximate Bayesian computation and machine learning algorithms’, Applied Energy, 268, p. 115025. doi: https://doi.org/10.1016/j.apenergy.2020.115025.