Electricity Generation Assessment of Photovoltaic Window System Using Data-Driven Modeling
Abstract
Photovoltaic window systems have emerged as a promising building-integrated photovoltaic (BIPV) technology by enabling electricity generation while maintaining facade transparency. However, accurately estimating their electricity generation potential at early design stages remains challenging due to the complex influence of architectural geometry on solar exposure. In this study, a data-driven regression framework is developed to predict the total annual electricity generation of photovoltaic window systems using a parametric simulation dataset provided by the National Renewable Energy Laboratory (NREL). The proposed framework employs four architectural and geometric input parameters window-to-wall ratio, number of stories, floor height, and north–south to east–west aspect ratio which are typically available during the early stages of building design. Three regression models, namely Linear Regression, Random Forest, and Gradient Boosted Regression Trees, are systematically evaluated using a five-fold cross-validation strategy. Model performance is assessed using normalized error metrics, including root mean square error, mean absolute error, and the Pearson correlation coefficient. The results indicate that Gradient Boosted Regression Trees achieved the best predictive performance, with a normalized RMSE of 0.265, normalized MAE of 0.235, and Pearson correlation coefficient of 0.964. In the original physical scale, the corresponding RMSE and MAE were 545.67 kWh and 483.83 kWh, respectively. Feature importance analysis reveals that the number of stories is the most influential parameter governing photovoltaic window electricity generation, highlighting the dominant role of building vertical scale. Residual analysis further confirms the absence of systematic prediction bias across the range of electricity generation values. Overall, the proposed approach enables rapid and reliable estimation of photovoltaic window electricity generation using a limited set of early-stage architectural parameters. These findings demonstrate the potential of machine learning-based models as effective tools for data-driven performance assessment and decision support in PV-integrated building design.
Keywords
Photovoltaic windows, Building-integrated photovoltaics (BIPV), Machine learning, Regression, Architectural parameters
Supporting Institution
Ethical Statement
Thanks
References
- Ahmad, T., & Chen, H. (2020). A review on machine learning forecasting growth trends and their real-time applications in different energy systems. Sustainable Cities and Society, 54, 102010. https://doi.org/10.1016/j.scs.2019.102010
- Alpsalaz, F., Özüpak, Y., Aslan, E., & Uzel, H. (2026). Hybrid machine learning approach for enhanced fault detection and power estimation in photovoltaic systems. IET Renewable Power Generation, 20(1), Article e70153. https://doi.org/10.1049/rpg2.70153
- Amasyali, K., & El-Gohary, N. (2017). Deep learning for building energy consumption prediction. In Proceedings of the 6th CSCE-CRC International Construction Specialty Conference (pp. 466–474). Canadian Society for Civil Engineering.
- Asadi, E., Da Silva, M. G., Antunes, C. H., & Dias, L. (2012). Multi-objective optimization for building retrofit strategies: A model and an application. Energy and Buildings, 44, 81–87. https://doi.org/10.1016/j.enbuild.2011.10.016
- Chai, T., & Draxler, R. R. (2014). Root mean square error (RMSE) or mean absolute error (MAE)?–Arguments against avoiding RMSE in the literature. Geoscientific Model Development, 7(3), 1247–1250. https://doi.org/10.5194/gmd-7-1247-2014
- Friedman, J. H. (2001). Greedy function approximation: A gradient boosting machine. Annals of Statistics, 29(5), 1189–1232. https://doi.org/10.1214/aos/1013203451
- Ghamari, M., & Sundaram, S. (2024). Solar window innovations: Enhancing building performance through advanced technologies. Energies, 17(14), Article 3369. https://doi.org/10.3390/en17143369
- Pearson, K. (1895). VII. Note on regression and inheritance in the case of two parents. Proceedings of the Royal Society of London, 58(347–352), 240–242. https://doi.org/10.1098/rspl.1895.0041
- Peng, J., Lu, L., & Yang, H. (2013). Review on life cycle assessment of energy payback and greenhouse gas emission of solar photovoltaic systems. Renewable and Sustainable Energy Reviews, 19, 255–274. https://doi.org/10.1016/j.rser.2012.11.035
- Sadineni, S. B., Madala, S., & Boehm, R. F. (2011). Passive building energy savings: A review of building envelope components. Renewable and Sustainable Energy Reviews, 15(8), 3617–3631. https://doi.org/10.1016/j.rser.2011.07.014