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A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design

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  • Seo, Dong-yeon
  • Koo, Choongwan
  • Hong, Taehoon

Abstract

It is important for an architect or a construction manager to easily and accurately estimate the heating and cooling demand of a residential building with a different envelope design in the early design phase. To achieve this objective, this study aimed to develop a nine-node-based Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design, which can be useful for an architect or a construction manager in the early design phase. This study was conducted in the following steps: (i) definition of the building envelope design elements; (ii) establishment of a standard database through energy simulation; and (iii) development of a Lagrangian finite element model. The prediction performance of the proposed nine-node-based model was improved in comparison with the four-node-based model. It was concluded that the prediction performance of the proposed nine-node-based model was superior to that of the four-node-based model. For the heating demand, RMSE(9-node, heating) (61.2), MAE(9-node, heating) (45.1), and MAPE(9-node, heating) (1.18%) of the proposed nine-node-based model was lower than that of the four-node-based model (73.9, 53.2, and 1.36%). For the cooling demand, RMSE(9-node, cooling) (71.5), MAE(9-node, cooling) (55.3), and MAPE(9-node, cooling) (8.24%) of the proposed nine-node-based model was lower than that of the four-node-based model (84.1, 61.1, and 8.71%). The series of processes used in this study could be applied to any other energy-saving technique or to a new/renewable energy system. It could also be extended to any other country or sector in the global environment.

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  • Seo, Dong-yeon & Koo, Choongwan & Hong, Taehoon, 2015. "A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design," Applied Energy, Elsevier, vol. 142(C), pages 66-79.
    • Handle: RePEc:eee:appene:v:142:y:2015:i:c:p:66-79
    DOI: 10.1016/j.apenergy.2014.12.051
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    5. Harish, V.S.K.V. & Kumar, Arun, 2016. "Reduced order modeling and parameter identification of a building energy system model through an optimization routine," Applied Energy, Elsevier, vol. 162(C), pages 1010-1023.
    6. Park, Hyo Seon & Koo, Choongwan & Hong, Taehoon & Oh, Jeongyoon & Jeong, Kwangbok, 2016. "A finite element model for estimating the techno-economic performance of the building-integrated photovoltaic blind," Applied Energy, Elsevier, vol. 179(C), pages 211-227.
    7. Koo, Choongwan & Hong, Taehoon & Kim, Jimin & Kim, Hyunjoong, 2015. "An integrated multi-objective optimization model for establishing the low-carbon scenario 2020 to achieve the national carbon emissions reduction target for residential buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 410-425.
    8. Oh, Jeongyoon & Koo, Choongwan & Hong, Taehoon & Cha, Seung Hyun, 2018. "An integrated model for estimating the techno-economic performance of the distributed solar generation system on building façades: Focused on energy demand and supply," Applied Energy, Elsevier, vol. 228(C), pages 1071-1090.
    9. Paul Anton Verwiebe & Stephan Seim & Simon Burges & Lennart Schulz & Joachim Müller-Kirchenbauer, 2021. "Modeling Energy Demand—A Systematic Literature Review," Energies, MDPI, vol. 14(23), pages 1-58, November.
    10. Al-Shammari, Eiman Tamah & Keivani, Afram & Shamshirband, Shahaboddin & Mostafaeipour, Ali & Yee, Por Lip & Petković, Dalibor & Ch, Sudheer, 2016. "Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm," Energy, Elsevier, vol. 95(C), pages 266-273.
    11. Evola, G. & Marletta, L., 2015. "The Solar Response Factor to calculate the cooling load induced by solar gains," Applied Energy, Elsevier, vol. 160(C), pages 431-441.
    12. Fang, Tingting & Lahdelma, Risto, 2016. "Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system," Applied Energy, Elsevier, vol. 179(C), pages 544-552.

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