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Calibration and uncertainty analysis for computer models – A meta-model based approach for integrated building energy simulation

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Cited by:

  1. Chaudhary, Gaurav & New, Joshua & Sanyal, Jibonananda & Im, Piljae & O’Neill, Zheng & Garg, Vishal, 2016. "Evaluation of “Autotune” calibration against manual calibration of building energy models," Applied Energy, Elsevier, vol. 182(C), pages 115-134.
  2. Brandão de Vasconcelos, Ana & Pinheiro, Manuel Duarte & Manso, Armando & Cabaço, António, 2015. "A Portuguese approach to define reference buildings for cost-optimal methodologies," Applied Energy, Elsevier, vol. 140(C), pages 316-328.
  3. Massimiliano Manfren & Maurizio Sibilla & Lamberto Tronchin, 2021. "Energy Modelling and Analytics in the Built Environment—A Review of Their Role for Energy Transitions in the Construction Sector," Energies, MDPI, vol. 14(3), pages 1-29, January.
  4. Enrico Fabrizio & Valentina Monetti, 2015. "Methodologies and Advancements in the Calibration of Building Energy Models," Energies, MDPI, vol. 8(4), pages 1-27, March.
  5. Sun, Kaiyu & Hong, Tianzhen & Taylor-Lange, Sarah C. & Piette, Mary Ann, 2016. "A pattern-based automated approach to building energy model calibration," Applied Energy, Elsevier, vol. 165(C), pages 214-224.
  6. Keshtkar, Azim & Arzanpour, Siamak, 2017. "An adaptive fuzzy logic system for residential energy management in smart grid environments," Applied Energy, Elsevier, vol. 186(P1), pages 68-81.
  7. Cheoljoon Jeong & Ziang Xu & Albert S. Berahas & Eunshin Byon & Kristen Cetin, 2023. "Multiblock Parameter Calibration in Computer Models," INFORMS Joural on Data Science, INFORMS, vol. 2(2), pages 116-137, October.
  8. Tian, Wei & Heo, Yeonsook & de Wilde, Pieter & Li, Zhanyong & Yan, Da & Park, Cheol Soo & Feng, Xiaohang & Augenbroe, Godfried, 2018. "A review of uncertainty analysis in building energy assessment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 93(C), pages 285-301.
  9. Manfren, Massimiliano & Nastasi, Benedetto & Groppi, Daniele & Astiaso Garcia, Davide, 2020. "Open data and energy analytics - An analysis of essential information for energy system planning, design and operation," Energy, Elsevier, vol. 213(C).
  10. Carlos Fernández Bandera & Germán Ramos Ruiz, 2017. "Towards a New Generation of Building Envelope Calibration," Energies, MDPI, vol. 10(12), pages 1-19, December.
  11. Michael D. Murphy & Paul D. O’Sullivan & Guilherme Carrilho da Graça & Adam O’Donovan, 2021. "Development, Calibration and Validation of an Internal Air Temperature Model for a Naturally Ventilated Nearly Zero Energy Building: Comparison of Model Types and Calibration Methods," Energies, MDPI, vol. 14(4), pages 1-24, February.
  12. Edwards, Richard E. & New, Joshua & Parker, Lynne E. & Cui, Borui & Dong, Jin, 2017. "Constructing large scale surrogate models from big data and artificial intelligence," Applied Energy, Elsevier, vol. 202(C), pages 685-699.
  13. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
  14. Michel Noussan & Benedetto Nastasi, 2018. "Data Analysis of Heating Systems for Buildings—A Tool for Energy Planning, Policies and Systems Simulation," Energies, MDPI, vol. 11(1), pages 1-15, January.
  15. Sol Kim & Sungwon Jung & Seung-Man Baek, 2019. "A Model for Predicting Energy Usage Pattern Types with Energy Consumption Information According to the Behaviors of Single-Person Households in South Korea," Sustainability, MDPI, vol. 11(1), pages 1-24, January.
  16. Gholami, M. & Torreggiani, D. & Tassinari, P. & Barbaresi, A., 2021. "Narrowing uncertainties in forecasting urban building energy demand through an optimal archetyping method," Renewable and Sustainable Energy Reviews, Elsevier, vol. 148(C).
  17. João Delgado & Ana Mafalda Matos & Ana Sofia Guimarães, 2022. "Linking Indoor Thermal Comfort with Climate, Energy, Housing, and Living Conditions: Portuguese Case in European Context," Energies, MDPI, vol. 15(16), pages 1-22, August.
  18. Hu, Mengqi, 2015. "A data-driven feed-forward decision framework for building clusters operation under uncertainty," Applied Energy, Elsevier, vol. 141(C), pages 229-237.
  19. Manfren, Massimiliano & Nastasi, Benedetto & Tronchin, Lamberto & Groppi, Daniele & Garcia, Davide Astiaso, 2021. "Techno-economic analysis and energy modelling as a key enablers for smart energy services and technologies in buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  20. José Sánchez Ramos & MCarmen Guerrero Delgado & Servando Álvarez Domínguez & José Luis Molina Félix & Francisco José Sánchez de la Flor & José Antonio Tenorio Ríos, 2019. "Systematic Simplified Simulation Methodology for Deep Energy Retrofitting Towards Nze Targets Using Life Cycle Energy Assessment," Energies, MDPI, vol. 12(16), pages 1-27, August.
  21. Ramos Ruiz, Germán & Fernández Bandera, Carlos, 2017. "Analysis of uncertainty indices used for building envelope calibration," Applied Energy, Elsevier, vol. 185(P1), pages 82-94.
  22. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2020. "A framework for uncertainty quantification in building heat demand simulations using reduced-order grey-box energy models," Applied Energy, Elsevier, vol. 275(C).
  23. Tronchin, Lamberto & Manfren, Massimiliano & James, Patrick AB., 2018. "Linking design and operation performance analysis through model calibration: Parametric assessment on a Passive House building," Energy, Elsevier, vol. 165(PA), pages 26-40.
  24. Østergård, Torben & Jensen, Rasmus Lund & Maagaard, Steffen Enersen, 2018. "A comparison of six metamodeling techniques applied to building performance simulations," Applied Energy, Elsevier, vol. 211(C), pages 89-103.
  25. Marzouk, Mohamed & Seleem, Noreihan, 2018. "Assessment of existing buildings performance using system dynamics technique," Applied Energy, Elsevier, vol. 211(C), pages 1308-1323.
  26. Pang, Zhihong & O'Neill, Zheng, 2018. "Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels," Applied Energy, Elsevier, vol. 232(C), pages 424-442.
  27. Tronchin, Lamberto & Manfren, Massimiliano & Nastasi, Benedetto, 2018. "Energy efficiency, demand side management and energy storage technologies – A critical analysis of possible paths of integration in the built environment," Renewable and Sustainable Energy Reviews, Elsevier, vol. 95(C), pages 341-353.
  28. Brunetti, Giuseppe & Porti, Michele & Piro, Patrizia, 2018. "Multi-level numerical and statistical analysis of the hygrothermal behavior of a non-vegetated green roof in a mediterranean climate," Applied Energy, Elsevier, vol. 221(C), pages 204-219.
  29. Aste, Niccolò & Leonforte, Fabrizio & Manfren, Massimiliano & Mazzon, Manlio, 2015. "Thermal inertia and energy efficiency – Parametric simulation assessment on a calibrated case study," Applied Energy, Elsevier, vol. 145(C), pages 111-123.
  30. Kapp, Sean & Choi, Jun-Ki & Hong, Taehoon, 2023. "Predicting industrial building energy consumption with statistical and machine-learning models informed by physical system parameters," Renewable and Sustainable Energy Reviews, Elsevier, vol. 172(C).
  31. Massimiliano Manfren & Benedetto Nastasi, 2020. "Parametric Performance Analysis and Energy Model Calibration Workflow Integration—A Scalable Approach for Buildings," Energies, MDPI, vol. 13(3), pages 1-14, February.
  32. Calama-González, Carmen María & Symonds, Phil & Petrou, Giorgos & Suárez, Rafael & León-Rodríguez, Ángel Luis, 2021. "Bayesian calibration of building energy models for uncertainty analysis through test cells monitoring," Applied Energy, Elsevier, vol. 282(PA).
  33. Yang, Tao & Pan, Yiqun & Mao, Jiachen & Wang, Yonglong & Huang, Zhizhong, 2016. "An automated optimization method for calibrating building energy simulation models with measured data: Orientation and a case study," Applied Energy, Elsevier, vol. 179(C), pages 1220-1231.
  34. Muhammad Waseem Ahmad & Anthony Mouraud & Yacine Rezgui & Monjur Mourshed, 2018. "Deep Highway Networks and Tree-Based Ensemble for Predicting Short-Term Building Energy Consumption," Energies, MDPI, vol. 11(12), pages 1-21, December.
  35. Enríquez, R. & Jiménez, M.J. & Heras, M.R., 2017. "Towards non-intrusive thermal load Monitoring of buildings: BES calibration," Applied Energy, Elsevier, vol. 191(C), pages 44-54.
  36. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
  37. Ji, Ying & Xu, Peng, 2015. "A bottom-up and procedural calibration method for building energy simulation models based on hourly electricity submetering data," Energy, Elsevier, vol. 93(P2), pages 2337-2350.
  38. Hsu, David, 2015. "Identifying key variables and interactions in statistical models of building energy consumption using regularization," Energy, Elsevier, vol. 83(C), pages 144-155.
  39. Young Tae Chae & Young M. Lee & David Longinott, 2016. "Assessment of Retrofitting Measures for a Large Historic Research Facility Using a Building Energy Simulation Model," Energies, MDPI, vol. 9(6), pages 1-18, June.
  40. Ramos Ruiz, Germán & Fernández Bandera, Carlos & Gómez-Acebo Temes, Tomás & Sánchez-Ostiz Gutierrez, Ana, 2016. "Genetic algorithm for building envelope calibration," Applied Energy, Elsevier, vol. 168(C), pages 691-705.
  41. O' Donovan, Adam & O' Sullivan, Paul D. & Murphy, Michael D., 2019. "Predicting air temperatures in a naturally ventilated nearly zero energy building: Calibration, validation, analysis and approaches," Applied Energy, Elsevier, vol. 250(C), pages 991-1010.
  42. Yang, Liu & Yan, Haiyan & Lam, Joseph C., 2014. "Thermal comfort and building energy consumption implications – A review," Applied Energy, Elsevier, vol. 115(C), pages 164-173.
  43. Vicente Gutiérrez González & Lissette Álvarez Colmenares & Jesús Fernando López Fidalgo & Germán Ramos Ruiz & Carlos Fernández Bandera, 2019. "Uncertainy’s Indices Assessment for Calibrated Energy Models," Energies, MDPI, vol. 12(11), pages 1-18, May.
  44. Zhang, Yixiang & Wang, Zhaohua & Zhou, Guanghui, 2013. "Determinants and implications of employee electricity saving habit: An empirical study in China," Applied Energy, Elsevier, vol. 112(C), pages 1529-1535.
  45. Wate, P. & Iglesias, M. & Coors, V. & Robinson, D., 2020. "Framework for emulation and uncertainty quantification of a stochastic building performance simulator," Applied Energy, Elsevier, vol. 258(C).
  46. Lin, Boqiang & Zhang, Guoliang, 2013. "Estimates of electricity saving potential in Chinese nonferrous metals industry," Energy Policy, Elsevier, vol. 60(C), pages 558-568.
  47. Lim, Hyunwoo & Zhai, Zhiqiang (John), 2018. "Influences of energy data on Bayesian calibration of building energy model," Applied Energy, Elsevier, vol. 231(C), pages 686-698.
  48. Chen, Jianli & Gao, Xinghua & Hu, Yuqing & Zeng, Zhaoyun & Liu, Yanan, 2019. "A meta-model-based optimization approach for fast and reliable calibration of building energy models," Energy, Elsevier, vol. 188(C).
  49. Li, Nan & Yang, Zheng & Becerik-Gerber, Burcin & Tang, Chao & Chen, Nanlin, 2015. "Why is the reliability of building simulation limited as a tool for evaluating energy conservation measures?," Applied Energy, Elsevier, vol. 159(C), pages 196-205.
  50. Robertson, Joseph J. & Polly, Ben J. & Collis, Jon M., 2015. "Reduced-order modeling and simulated annealing optimization for efficient residential building utility bill calibration," Applied Energy, Elsevier, vol. 148(C), pages 169-177.
  51. Rackes, Adams & Melo, Ana Paula & Lamberts, Roberto, 2016. "Naturally comfortable and sustainable: Informed design guidance and performance labeling for passive commercial buildings in hot climates," Applied Energy, Elsevier, vol. 174(C), pages 256-274.
  52. Yuan, Jun & Nian, Victor & Su, Bin, 2019. "Evaluation of cost-effective building retrofit strategies through soft-linking a metamodel-based Bayesian method and a life cycle cost assessment method," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  53. Lee, P. & Lam, P.T.I. & Lee, W.L. & Chan, E.H.W., 2016. "Analysis of an air-cooled chiller replacement project using a probabilistic approach for energy performance contracts," Applied Energy, Elsevier, vol. 171(C), pages 415-428.
  54. Østergård, Torben & Jensen, Rasmus L. & Maagaard, Steffen E., 2016. "Building simulations supporting decision making in early design – A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 61(C), pages 187-201.
  55. Li, Zhengwei & Han, Yanmin & Xu, Peng, 2014. "Methods for benchmarking building energy consumption against its past or intended performance: An overview," Applied Energy, Elsevier, vol. 124(C), pages 325-334.
  56. Aste, Niccolò & Manfren, Massimiliano & Marenzi, Giorgia, 2017. "Building Automation and Control Systems and performance optimization: A framework for analysis," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 313-330.
  57. Yuan, Jun & Nian, Victor & Su, Bin & Meng, Qun, 2017. "A simultaneous calibration and parameter ranking method for building energy models," Applied Energy, Elsevier, vol. 206(C), pages 657-666.
  58. Capozzoli, Alfonso & Piscitelli, Marco Savino & Neri, Francesco & Grassi, Daniele & Serale, Gianluca, 2016. "A novel methodology for energy performance benchmarking of buildings by means of Linear Mixed Effect Model: The case of space and DHW heating of out-patient Healthcare Centres," Applied Energy, Elsevier, vol. 171(C), pages 592-607.
  59. Hou, D. & Hassan, I.G. & Wang, L., 2021. "Review on building energy model calibration by Bayesian inference," Renewable and Sustainable Energy Reviews, Elsevier, vol. 143(C).
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