A new regression model to predict BIPV cell temperature for various climates using a high-resolution CFD microclimate model

Zhang, Ruijun; Gan, Yangyu; Mirzaei, Parham A.

Abstract

Understanding of cell temperature of Building Integrated Photovoltaics (BIPV) is essential in the calculation of their conversion efficiency, durability and installation costs. Current PV cell temperature models mainly fail to provide accurate predictions in complex arrangement of BIPVs under various climatic conditions. To address this limitation, this paper proposes a new regression model for prediction of the BIPV cell temperature in various climates and design conditions, including the effects of relative PV position to the roof edge, solar radiation intensity, wind speed, and wind direction. To represent the large number of possible climatic and design scenarios, the advanced technique of Latin Hypercube Sampling was firstly utilized to reduce the number of investigated scenarios from 13,338 to 374. Then, a high-resolution validated full-scale 3-dimensional Computational Fluid Dynamics (CFD) microclimate model was developed for modelling of BIPV’s cell temperature, and then was applied to model all the reduced scenarios. A nonlinear multivariable regression model was afterward fit to this population of 374 sets of CFD simulations. Eventually, the developed regression model was evaluated with new sets of unused climatic and design data when a high agreement with a mean discrepancy of 3% between the predicted and simulated BIPV cell temperatures was observed.

Citation

Zhang, R., Gan, Y., & Mirzaei, P. A. (2020). A new regression model to predict BIPV cell temperature for various climates using a high-resolution CFD microclimate model. Advances in Building Energy Research, 14(4), 527-549. https://doi.org/10.1080/17512549.2019.1654917

• Journal Article Type: Article
• Acceptance Date: Aug 1, 2019
• Online Publication Date: Aug 25, 2019
• Publication Date: 2020
• Deposit Date: Aug 7, 2019
• Publicly Available Date: Aug 26, 2020
• Journal: Advances in Building Energy Research
• Print ISSN: 1751-2549
• Electronic ISSN: 1756-2201
• Publisher: Taylor & Francis Open
• Peer Reviewed: Peer Reviewed
• Volume: 14
• Issue: 4
• Pages: 527-549
• DOI: https://doi.org/10.1080/17512549.2019.1654917
• Keywords: Building, BIPV, Latin Hypercube Sampling, Regression, CFD
• Public URL: https://nottingham-repository.worktribe.com/output/2400458
• Publisher URL: https://www.tandfonline.com/doi/abs/10.1080/17512549.2019.1654917
• Additional Information: This is an Accepted Manuscript of an article published by Taylor & Francis in Advances in Building Energy Research on 25/08/2019, available online: http://www.tandfonline.com/10.1080/17512549.2019.1654917