Forecasting of Irradiance on Bifacial Solar Panel based on Climate Parameters and Tilt Angle

Abstract

The aim of this study was to explore the use of data-driven approaches for designing net zero energy buildings using bifacial solar panels and deep learning models. The study utilized historical data on solar irradiance to forecast future irradiation levels, which were then used to optimize the tilt angle of bifacial solar panels for maximum energy collection. Two deep learning models, namely RNN-LSTM and transformers, were evaluated using various evaluation metrics, including MAE, RMSE, SMAPE, and R2. The results showed that both models were effective in forecasting irradiations with varying forecasting horizons, with the transformers model outperforming the RNN-LSTM model in terms of MAE values. The study provides insights into the use of data-driven approaches for designing net zero energy buildings, highlighting the potential of deep learning models in optimizing the use of renewable energy sources for sustainable development. The results show that both models were effective in forecasting irradiance, with R2 values of 0.927 for RNN-LSTM and 0.894 for Transformers. Additionally, the Transformers model outperformed the RNN-LSTM model with a lower range of MAE values from 0.05 to 0.03 across the same horizons. These findings suggest that deep learning models can effectively forecast solar irradiance and aid in optimizing the performance of net-zero energy buildings.