Improving the prediction performance of the finite element model for estimating the technical performance of the distributed generation of solar power system in a building façade
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DOI: 10.1016/j.apenergy.2018.01.081
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Cited by:
- Gao, Yuan & Dong, Jianfei & Isabella, Olindo & Santbergen, Rudi & Tan, Hairen & Zeman, Miro & Zhang, Guoqi, 2018. "A photovoltaic window with sun-tracking shading elements towards maximum power generation and non-glare daylighting," Applied Energy, Elsevier, vol. 228(C), pages 1454-1472.
- 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).
- Albara M. Mustafa & Abbas Barabadi & Tore Markeset & Masoud Naseri, 2021. "An overall performance index for wind farms: a case study in Norway Arctic region," International Journal of System Assurance Engineering and Management, Springer;The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden, vol. 12(5), pages 938-950, October.
- 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.
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Keywords
Distributed generation of solar power system; Building-integrated photovoltaic blind; Finite element method; Prediction performance; Nonlinearity analysis;All these keywords.
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