Smart Solar Energy Tracking and Forecasting System Using Data Analytics and LSTM Models

This paper presents a solar energy analysis system that utilizes data analytics for enhanced performance. The system integrates solar panel tracking technology, real-time data visualization through Power BI, and forecasting using Long Short-Term Memory (LSTM) techniques and regression analysis. The accuracy of the solar tracking system was evaluated using root mean square error (RMSE = 6.05) and mean squared error (MSE = 7.45). To further assess the forecasting model, the study examines the impact of error margins on practical applications. Additionally, scalability considerations for larger solar farms and potential forecasting limitations, such as environmental factors and long-term accuracy, are discussed.