Artificial neural networks for power output forecasting from bifacial solar PV system with enhanced building roof surface Albedo

Abstract

The main objective of this research study is to evaluate the performance of bifacial solar PV systems (bPV) installed on flat roof buildings with controlled surface albedo and to develop forecasting models to anticipate the power output from bifacial solar PV systems due to the enhancement of surface albedo. The originality of this study and the novelty include integrating innovative bifacial solar PV and cool roof technologies to increase solar PV panel power output and anticipate electricity generation in advance to balance supply and demand. For the methodology, modeling and simulation analysis was used to test the performance of bPV system installed on a building with a surface albedo of 0.2–0.8. An Artificial Neural Network was used as a machine learning approach for bifacial solar PV power and energy forecasting. The significant findings show that an increase in surface albedo from 0.2 to 0.5 and 0.2 to 0.8 will help to boost the annual bifacial solar PV power production by 7.75% and 14.96%, respectively. The predictive models are exceptionally accurate in anticipating the bifacial solar PV power. The monthly power from bPV rose from 10.87 to 21.54%, with a 0.3 (from 0.2 to 0.5) to 0.6 (from 0.2 to 0.8) albedo increase. The coefficient of correlation R values for all of the power data ranges from 0.99145 to 0.99382 for the roof surface albedo, ranging from 0.2 to 0.8. The ANN models illustrate how effectively the model mimics the modeling and simulation data. For the research implications, the developed forecast models will assist the industry in predicting ahead of the power production, building operations and maintenance, demand-side management, and advanced energy purchases.