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A comparison of six metamodeling techniques applied to building performance simulations

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  1. Pal, Anuj & Cornelius, Andrew & Sun, Zongxuan & Kim, Kenneth & Kweon, Chol-Bum Mike, 2024. "Data-driven real-time fuel cetane estimation and control design for multifuel UAVs," Applied Energy, Elsevier, vol. 367(C).
  2. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A novel improved model for building energy consumption prediction based on model integration," Applied Energy, Elsevier, vol. 262(C).
  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. Noye, Sarah & Mulero Martinez, Rubén & Carnieletto, Laura & De Carli, Michele & Castelruiz Aguirre, Amaia, 2022. "A review of advanced ground source heat pump control: Artificial intelligence for autonomous and adaptive control," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  5. 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).
  6. Shen, Meng & Lu, Yujie & Wei, Kua Harn & Cui, Qingbin, 2020. "Prediction of household electricity consumption and effectiveness of concerted intervention strategies based on occupant behaviour and personality traits," Renewable and Sustainable Energy Reviews, Elsevier, vol. 127(C).
  7. Massimiliano Manfren & Karla M. Gonzalez-Carreon & Patrick A. B. James, 2024. "Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps," Energies, MDPI, vol. 17(4), pages 1-22, February.
  8. Kaseb, Z. & Montazeri, H., 2022. "Data-driven optimization of building-integrated ducted openings for wind energy harvesting: Sensitivity analysis of metamodels," Energy, Elsevier, vol. 258(C).
  9. Mert Edali, 2022. "Pattern‐oriented analysis of system dynamics models via random forests," System Dynamics Review, System Dynamics Society, vol. 38(2), pages 135-166, April.
  10. 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.
  11. 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.
  12. Soheil Fathi & Ravi S. Srinivasan & Charles J. Kibert & Ruth L. Steiner & Emre Demirezen, 2020. "AI-Based Campus Energy Use Prediction for Assessing the Effects of Climate Change," Sustainability, MDPI, vol. 12(8), pages 1-22, April.
  13. Mostafa M. Saad & Ramanunni Parakkal Menon & Ursula Eicker, 2023. "Supporting Decision Making for Building Decarbonization: Developing Surrogate Models for Multi-Criteria Building Retrofitting Analysis," Energies, MDPI, vol. 16(16), pages 1-28, August.
  14. Fathi, Soheil & Srinivasan, Ravi & Fenner, Andriel & Fathi, Sahand, 2020. "Machine learning applications in urban building energy performance forecasting: A systematic review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
  15. Eneyew, Dagimawi D. & Capretz, Miriam A.M. & Bitsuamlak, Girma T., 2024. "Continuous model calibration framework for smart-building digital twin: A generative model-based approach," Applied Energy, Elsevier, vol. 375(C).
  16. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "Impact of adjustment strategies on building design process in different climates oriented by multiple performance," Applied Energy, Elsevier, vol. 266(C).
  17. 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).
  18. Guglielmina Mutani & Pamela Vocale & Kavan Javanroodi, 2023. "Toward Improved Urban Building Energy Modeling Using a Place-Based Approach," Energies, MDPI, vol. 16(9), pages 1-17, May.
  19. Sean Hay Kim & Jungmin Nam, 2020. "Can Both the Economic Value and Energy Performance of Small- and Mid-Sized Buildings Be Satisfied? Development of a Design Expert System in the Context of Korea," Sustainability, MDPI, vol. 12(12), pages 1-29, June.
  20. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
  21. Yue, Naihua & Caini, Mauro & Li, Lingling & Zhao, Yang & Li, Yu, 2023. "A comparison of six metamodeling techniques applied to multi building performance vectors prediction on gymnasiums under multiple climate conditions," Applied Energy, Elsevier, vol. 332(C).
  22. Prada, A. & Gasparella, A. & Baggio, P., 2018. "On the performance of meta-models in building design optimization," Applied Energy, Elsevier, vol. 225(C), pages 814-826.
  23. Gonçalo Roque Araújo & Ricardo Gomes & Maria Glória Gomes & Manuel Correia Guedes & Paulo Ferrão, 2023. "Surrogate Models for Efficient Multi-Objective Optimization of Building Performance," Energies, MDPI, vol. 16(10), pages 1-13, May.
  24. Walery Jezierski & Adam Święcicki & Anna Justyna Werner-Juszczuk, 2024. "Deterministic Mathematical Model of Energy Demand of Single-Family Building with Different Parameters and Orientation of Windows in Climatic Conditions of Poland," Energies, MDPI, vol. 17(10), pages 1-19, May.
  25. Zhan, Jin & He, Wenjing & Huang, Jianxiang, 2024. "Comfort, carbon emissions, and cost of building envelope and photovoltaic arrangement optimization through a two-stage model," Applied Energy, Elsevier, vol. 356(C).
  26. Mert Edali & Gönenç Yücel, 2020. "Analysis of an individual‐based influenza epidemic model using random forest metamodels and adaptive sequential sampling," Systems Research and Behavioral Science, Wiley Blackwell, vol. 37(6), pages 936-958, November.
  27. Díaz, Guzmán & Coto, José & Gómez-Aleixandre, Javier, 2019. "Prediction and explanation of the formation of the Spanish day-ahead electricity price through machine learning regression," Applied Energy, Elsevier, vol. 239(C), pages 610-625.
  28. Yunlong Qiu & Haiyang Wu & Yuntong Dai & Kai Li, 2024. "Behavior Prediction and Inverse Design for Self-Rotating Skipping Ropes Based on Random Forest and Neural Network," Mathematics, MDPI, vol. 12(7), pages 1-20, March.
  29. Singh, Manav Mahan & Singaravel, Sundaravelpandian & Geyer, Philipp, 2021. "Machine learning for early stage building energy prediction: Increment and enrichment," Applied Energy, Elsevier, vol. 304(C).
  30. Seung Yeoun Choi & Sean Hay Kim, 2022. "Selection of a Transparent Meta-Model Algorithm for Feasibility Analysis Stage of Energy Efficient Building Design: Clustering vs. Tree," Energies, MDPI, vol. 15(18), pages 1-25, September.
  31. John Austin McCandlish & Turgay Ayer & Jagpreet Chhatwal, 2023. "Cost-Effectiveness and Value-of-Information Analysis Using Machine Learning–Based Metamodeling: A Case of Hepatitis C Treatment," Medical Decision Making, , vol. 43(1), pages 68-77, January.
  32. Abokersh, Mohamed Hany & Vallès, Manel & Cabeza, Luisa F. & Boer, Dieter, 2020. "A framework for the optimal integration of solar assisted district heating in different urban sized communities: A robust machine learning approach incorporating global sensitivity analysis," Applied Energy, Elsevier, vol. 267(C).
  33. Yizhe Xu & Chengchu Yan & Hao Qian & Liang Sun & Gang Wang & Yanlong Jiang, 2021. "A Novel Optimization Method for Conventional Primary and Secondary School Classrooms in Southern China Considering Energy Demand, Thermal Comfort and Daylighting," Sustainability, MDPI, vol. 13(23), pages 1-19, November.
  34. Westermann, Paul & Welzel, Matthias & Evins, Ralph, 2020. "Using a deep temporal convolutional network as a building energy surrogate model that spans multiple climate zones," Applied Energy, Elsevier, vol. 278(C).
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