Reconstruction of narrowband solar radiation for enhanced spectral selectivity in building-integrated solar energy simulations

Chenshun Chen, Qiuhua Duan, Yanxiao Feng, Julian Wang, Neda Ghaeili Ardabili, Nan Wang, Seyed Morteza Hosseini, Chao Shen

Research output: Contribution to journalArticlepeer-review

Abstract

Solar radiation is a critical factor in advanced envelope design and solar energy's building integration, necessitating a shift from broadband solar radiation analyses towards more precise narrowband or spectrum-focused approaches. Understanding the performance potential of spectral-selective materials or structures requires accurate solar spectral information at specified locations, a feature often overlooked by conventional modeling tools. This work presents an innovative solar decomposing model capable of differentiating key solar irradiation components—visible and infrared—from broadband solar irradiance, without the need for expensive spectrum measurements. Our approach employs the extreme boosting regression tree method and leverages existing or easily derivable data from typical weather files. An exploratory analysis of the importance and interaction of different features in predicting solar irradiation components is also conducted. The results show that the proposed algorithm has an R2 of 0.981 and 0.990, RMSE of 18.280 and 18.390, and MAE of 7.989 and 8.011, for predicting VIS and NIR amount in DNI, respectively (for the strongest model using all the predictors within the dataset). This research offers an added layer of practicality by including case studies demonstrating how the solar decomposition models serve in real-world applications, especially in the integration of wavelength-selective devices like window systems and transparent solar cells into advanced envelope designs. Such real-world testing has verified the presence of a disparity between the power output calculation of NIR-selective transparent photovoltaics upon the broadband solar radiation data and the suggested narrowband solar radiation data, potentially resulting in a maximum deviation of 15.7 %. The decomposing models developed empower researchers and designers to generate new weather files comprising narrowband solar irradiance data, thereby enhancing their capacity to examine the influence of spectral-selective materials on a building's solar performance using existing solar simulation programs. The proposed method will be further improved by including more data from different climate zones and weather characteristics in the training model and validating through field measurements.

Original languageEnglish (US)
Article number119554
JournalRenewable Energy
Volume219
DOIs
StatePublished - Dec 2023

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment

Keywords

  • Building integrated photovoltaics
  • Machine learning
  • Solar envelope design and simulation
  • Solar irradiation
  • Spectral selective
  • Transparent photovoltaics

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