Quick energy prediction and comparison of options at the early design stage

de Wilde, 2002, Design analysis integration: supporting the selection of energy saving building components, Build. Environ., 37, 807, 10.1016/S0360-1323(02)00053-7 Østergård, 2017, Early Building Design: Informed decision-making by exploring multidimensional design space using sensitivity analysis, Energy Build., 142, 8, 10.1016/j.enbuild.2017.02.059 Østergård, 2016, Building simulations supporting decision making in early design – A review, Renew. Sustain. Energy Rev., 61, 187, 10.1016/j.rser.2016.03.045 Mahdavi, 1999, A comprehensive computational environment for performance based reasoning in building design and evaluation, Autom. Constr., 8, 427, 10.1016/S0926-5805(98)00089-2 Bleil de Souza, 2015, Thermal simulation software outputs: a framework to produce meaningful information for design decision-making, J. Build. Perform. Simul., 8, 57, 10.1080/19401493.2013.872191 F. Ritter, P. Geyer, A. Bormann, Simulation-based Decision-making in Early Design Stages, in: Proceeding 32nd CIB W78 Conf., 2015: pp. 657–666. Schlueter, 2009, Building information model based energy/exergy performance assessment in early design stages, Autom. Constr., 18, 153, 10.1016/j.autcon.2008.07.003 Méndez Echenagucia, 2015, The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis, Appl. Energy., 154, 577, 10.1016/j.apenergy.2015.04.090 Energy Systems Research Unit, A Tour of ESP-r, (2018). http://www.esru.strath.ac.uk/Courseware/ESP-r/tour/uncertainty.html (accessed July 4, 2020). Geyer, 2014, Automated metamodel generation for Design Space Exploration and decision-making – A novel method supporting performance-oriented building design and retrofitting, Appl. Energy., 119, 537, 10.1016/j.apenergy.2013.12.064 Negendahl, 2015, Building performance simulation in the early design stage: An introduction to integrated dynamic models, Autom. Constr., 54, 39, 10.1016/j.autcon.2015.03.002 Clarke, 2015, Integrated building performance simulation: Progress, prospects and requirements, Build. Environ., 91, 294, 10.1016/j.buildenv.2015.04.002 Underwood, 2004 J. Clarke, Energy simulation in building design, 2nd ed.; 2, Butterworth-Heinemann, 2001. K. Tupper, C. Fluhrer, Energy modeling at each design phase: strategies to minimise design energy use, in: Proc. SimBuild; 2010 SimBuild 2010. (2010). http://ibpsa-usa.org/index.php/ibpusa/article/view/271/260. US Department of Energy, EnergyPlus 8.9.0, (2018). https://energyplus.net/documentation. Solar Energy Laboratory University of Wisconsin-Madison, TRNSYS: A Transient Simulation Program, 2020. https://sel.me.wisc.edu/trnsys/features/features.html. Wetter, 2009, Modelica-based modelling and simulation to support research and development in building energy and control systems, J. Build. Perform. Simul., 2, 143, 10.1080/19401490902818259 de Wilde, 2014, The gap between predicted and measured energy performance of buildings: A framework for investigation, Autom. Constr., 41, 40, 10.1016/j.autcon.2014.02.009 Cuerda, 2020, Understanding the performance gap in energy retrofitting: Measured input data for adjusting building simulation models, Energy Build., 209, 10.1016/j.enbuild.2019.109688 R. Jones, A. Fuertes, P. Wilde, The gap between simulated and measured energy performance: A case study across six identical new-build flats in the UK, 2015. van Dronkelaar, 2016, A Review of the Regulatory Energy Performance Gap and Its Underlying Causes in Non-domestic Buildings, Front. Mech. Eng., 1, 10.3389/fmech.2015.00017 Khoury, 2017, Understanding and bridging the energy performance gap in building retrofit, Energy Proc., 122, 217, 10.1016/j.egypro.2017.07.348 Nall, 2011, Energy simulation in the building design process: mainframe computer simulation programs for energy analysis can provide important information throughout the building design process. (Reprint), ASHRAE J., 53, 36 N. Jones, C.J. McCrone, B.J. Walter, K.B. Pratt, D.P. Greenberg, Automated translation and thermal zoning of digital building models for energy analysis, 2013. Singaravel, 2018, Deep-learning neural-network architectures and methods: Using component-based models in building-design energy prediction, Adv. Eng. Inform., 38, 81, 10.1016/j.aei.2018.06.004 Strachan, 2008, History and development of validation with the ESP-r simulation program, Build. Environ., 43, 601, 10.1016/j.buildenv.2006.06.025 Picco, 2014, Towards energy performance evaluation in early stage building design: A simplification methodology for commercial building models, Energy Build., 76, 497, 10.1016/j.enbuild.2014.03.016 B. Urban, L. Glicksman, The MIT design advisor – a fast, simple tool for energy efficient building design, Proc. SimBuild; 2006 SimBuild 2006, 2006. http://ibpsa-usa.org/index.php/ibpusa/article/view/219. Van Gelder, 2014, Probabilistic design and analysis of building performances: Methodology and application example, Energy Build., 79, 202, 10.1016/j.enbuild.2014.04.042 Rezaee, 2015, Assessment of uncertainty and confidence in building design exploration, Artif. Intell. Eng. Des. Anal. Manuf., 29, 429, 10.1017/S0890060415000426 M.M. Singh, P. Geyer, Statistical decision assistance for determining energy-efficient options in building design under uncertainty, in: P. Geyer, K. Allacker, M. Schevenels, F. De Troyer, Pieter Pauwels (Eds.), 26th Int. Work. Intell. Comput. Eng., Leuven, 2019. http://ceur-ws.org/Vol-2394/paper08.pdf. M. Hollander, J. Sethuraman, Nonparametric Statistics: Rank-Based Methods, in: Int. Encycl. Soc. Behav. Sci., Elsevier, 2015: pp. 891–897. https://doi.org/10.1016/B978-0-08-097086-8.42157-4. The AIA Energy Modeling Working Group, An Architect’s Guide to Integrating Energy Modeling in the Design Process, 2012. http://content.aia.org/sites/default/files/2016-04/Energy-Modeling-Design-Process-Guide.pdf. B. Griffith, N. Long, P. Torcellini, R. Judkoff, D. Crawley, J. Ryan, Methodology for Modeling Building Energy Performance across the Commercial Sector, Golden, Colorado, 2008. https://www.nrel.gov/docs/fy08osti/41956.pdf. Shiel, 2018, Parametric analysis of design stage building energy performance simulation models, Energy Build., 172, 78, 10.1016/j.enbuild.2018.04.045 Chen, 2018, Impacts of building geometry modeling methods on the simulation results of urban building energy models, Appl. Energy., 215, 717, 10.1016/j.apenergy.2018.02.073 Dogan, 2016, Autozoner: an algorithm for automatic thermal zoning of buildings with unknown interior space definitions, J. Build. Perform. Simul., 9, 176, 10.1080/19401493.2015.1006527 T. Dogan, E. Saratsis, C. Reinhart, The optimisation potential of floor-plan typologies in early design energy modeling, in: Proc. BS2015 14th Conf. Int. Build. Perform. Simul. Assoc., Hyderabad, India, 2015. http://www.ibpsa.org/proceedings/BS2015/p2455.pdf. Smith, 2012, Beyond the Shoebox: Thermal Zoning Approaches for Complex Building Shapes, ASHRAE Trans., 118, 141 James J. Hirsch & ASSOCIATES, eQUEST Introductory Tutorial, Camarillo, CA, 2009. http://doe2.com/download/equest/eQ-v3-63_Introductory-Tutorial.pdf. Sefaira, Zoning Strategy - Perimeter / Core, (2019). https://support.sefaira.com/hc/en-us/articles/115000093632-Zoning-Strategy-Perimeter-Core (accessed December 16, 2019). Shin, 2019, Thermal zoning for building HVAC design and energy simulation: A literature review, Energy Build., 203, 10.1016/j.enbuild.2019.109429 Geyer, 2018, Component-Based Machine Learning for Energy Performance Prediction by MultiLOD Models in the Early Phases of Building Design, Adv. Comput. Strateg. Eng., 516, 10.1007/978-3-319-91635-4_27 Schlueter, 2018, Linking BIM and Design of Experiments to balance architectural and technical design factors for energy performance, Autom. Constr., 86, 33, 10.1016/j.autcon.2017.10.021 Wauman, 2013, Evaluation of the accuracy of the implementation of dynamic effects in the quasi steady-state calculation method for school buildings, Energy Build., 65, 173, 10.1016/j.enbuild.2013.05.046 Ballarini, 2018, On the limits of the quasi-steady-state method to predict the energy performance of low-energy buildings, Therm. Sci., 22, 1117, 10.2298/TSCI170724133B A. Moronis, C. Koulamas, A. Kalogeras, Validation of a monthly quasi-steady-state simulation model for the energy use in buildings, in: 2017 22nd IEEE Int. Conf. Emerg. Technol. Fact. Autom., IEEE, 2017: pp. 1–6. https://doi.org/10.1109/ETFA.2017.8247665. B. Wauman, Evaluation of the quasi-steady-state method for the assessment of energy use in school buildings, 2015. https://lirias.kuleuven.be/handle/123456789/507727. G. Pernigotto, A. Gasparella, Quasi-steady state and dynamic simulation approaches for the calculation of building energy needs: Part 1 thermal losses, in: Build. Simul. Appl., 2013: pp. 293–302. http://www.ibpsa.org/proceedings/BSA2013/31.pdf. Amasyali, 2018, A review of data-driven building energy consumption prediction studies, Renew. Sustain. Energy Rev., 81, 1192, 10.1016/j.rser.2017.04.095 Geyer, 2018, Component-based machine learning for performance prediction in building design, Appl. Energy., 228, 1439, 10.1016/j.apenergy.2018.07.011 Westermann, 2019, Surrogate modelling for sustainable building design – A review, Energy Build., 198, 170, 10.1016/j.enbuild.2019.05.057 Andriamamonjy, 2019, Automated grey box model implementation using BIM and Modelica, Energy Build., 188–189, 209, 10.1016/j.enbuild.2019.01.046 Ahn, 2014, BIM interface for full vs. semi-automated building energy simulation, Energy Build., 68, 671, 10.1016/j.enbuild.2013.08.063 Gao, 2019, Building information modelling based building energy modelling: A review, Appl. Energy., 238, 320, 10.1016/j.apenergy.2019.01.032 Kim, 2015, Developing a physical BIM library for building thermal energy simulation, Autom. Constr., 50, 16, 10.1016/j.autcon.2014.10.011 Karola, 2002, BSPro COM-Server—interoperability between software tools using industrial foundation classes, Energy Build., 34, 901, 10.1016/S0378-7788(02)00066-X Bazjanac, 2004, Building energy performance simulation as part of interoperable software environments, Build. Environ., 39, 879, 10.1016/j.buildenv.2004.01.012 Fazio, 2007, IFC-Based Framework for Evaluating Total Performance of Building Envelopes, J. Archit. Eng., 13, 44, 10.1061/(ASCE)1076-0431(2007)13:1(44) Negendahl, 2015, Building energy optimisation in the early design stages: A simplified method, Energy Build., 105, 88, 10.1016/j.enbuild.2015.06.087 Singaravel, 2019, Deep convolutional learning for general early design stage prediction models, Adv. Eng. Informatics., 42, 10.1016/j.aei.2019.100982 Struck, 2012, Uncertainty propagation and sensitivity analysis techniques in building performance simulation to support conceptual building and system design, Technische Universiteit Eindhoven Hemsath, 2015, Sensitivity analysis evaluating basic building geometry’s effect on energy use, Renew. Energy., 76, 526, 10.1016/j.renene.2014.11.044 ASHRAE, Standard 90.1-2013. Energy Standard for Buildings Except Low-Rise Residential Buildings, Atlanta, GA, 2013. www.ashrae.org. Hopfe, 2013, Multi-criteria decision making under uncertainty in building performance assessment, Build. Environ., 69, 81, 10.1016/j.buildenv.2013.07.019 Lee, 2016, Full-factorial design space exploration approach for multi-criteria decision making of the design of industrial halls, Energy Build., 117, 352, 10.1016/j.enbuild.2015.09.028 Singh, 2020, Validation of Early Design Stage EnergyPlus Model for Office, Building de Wit, 2001, Uncertainty Analysis of Building Design Evaluations, Build. Simul., 319–326 Corder, 2014 Julious, 2004, Sample sizes for clinical trials with Normal data, Stat. Med., 23, 1921, 10.1002/sim.1783 ASHRAE Standards Committee, Energy standard for buildings except low-rise residential buildings, Atlanta, GA : American Society of Heating, Refrigerating and Air-Conditioning Engineers, ASHRAE, [2010] ©2010, Atlanta, GA, 2016. https://doi.org/https://www.ashrae.org/technical-resources/bookstore/standard-90-1. M.M. Singh, S. Singaravel, P. Geyer, Improving Prediction Accuracy of Machine Learning Energy Prediction Models, in: B. Kumar, F.P. Rahimian, D. Greenwood, T. Hartmann (Eds.), Proc. 36th CIB W78 2019 Conf., Newcastle, UK, 2019: pp. 102–112. Singh, 2020, Verification of Early Design Stage Machine Learning Model using, EnergyPlus Simulation Data F. Chollet, Keras, (2015). https://keras.io (accessed May 6, 2019). Autodesk Inc., Revit | BIM Software, (2021). https://www.autodesk.com/products/revit (accessed July 19, 2020). Asadi, 2014, On the development of multi-linear regression analysis to assess energy consumption in the early stages of building design, Energy Build., 85, 246, 10.1016/j.enbuild.2014.07.096 E. Neufert, P. Neufert, Neufert Architects’ Data, Third, Blackwell Publishing Ltd, West Sussex, UK, 2010. de Wit, 2002, Analysis of uncertainty in building design evaluations and its implications, Energy Build., 34, 951, 10.1016/S0378-7788(02)00070-1 Hopfe, 2011, Uncertainty analysis in building performance simulation for design support, Energy Build., 43, 2798, 10.1016/j.enbuild.2011.06.034 Passivhaus Institut, Passive House requirements, (2015). https://passiv.de/en/02_informations/02_passive-house-requirements/02_passive-house-requirements.htm (accessed July 8, 2020). Federal Ministry of Transport Building and Urban Affairs (BMVBS), EnEV 2014: Annex 2 - Requirements for non-residential building, Germany, 2014. Johra, 2017, Numerical Analysis of the Impact of Thermal Inertia from the Furniture / Indoor Content and Phase Change Materials on the Building Energy Flexibility, Build. Simul., 2017, 35 de Wilde, 2010, Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution, Energy Build., 42, 1674, 10.1016/j.enbuild.2010.04.011