Prediction of Solar Panel Maintenance using BiLSTM

This study focuses on predicting the need for maintenance in solar energy systems using various machine and deep learning models. With the increase in adoption of solar energy, maintenance prediction becomes crucial for ensuring solar panels efficiency and durability. This study explores the performance of LSTM, Bi LSTM, GRU, Decision Tree, Random Forest, Bagging, Gradient Boosting, Voting Regressor, and Stacking models. Data was collected from Vignan’s University solar panels within a time span of four years. Through comprehensive experimentation, it was found that LSTM architectures were suitable for time series prediction and in Bi LSTM model few layers were added that achieved r2 score of 0.99, indicating superior predictive capability compared to other models. The results of this research can significantly contribute to the optimization of maintenance schedules for solar energy in Vignan’s University, ultimately reducing the cost of frequent maintenance and maximizing energy production efficiency.