A Reliable Estimation of Solar Energy Prediction Through the use of Hybrid CNN-LSTM Algorithm

The unpredictability of solar resources increases the difficulty of grid management as solar dispersion rates rise. Because renewable energy sources provide electricity in unpredictable and intermittent amounts, grid integration is one of the primary challenges. In order to maintain power reliability, economical delivery, and ideal entity dedication, it becomes essential to anticipate solar electricity. Every year, new methods and strategies are developed worldwide to improve forecast accuracy and minimize uncertainty in models. To address this, a unique hybrid deep learning technique based on convolutional neural networks (CNNs) and long short-term memory (LSTM) is suggested. Solar energy generation predictive performance is determined by use of statistical measurements, including root mean square error (RMSE), mean absolute error (MAE), normalized RMSE, relative RMSE, and coefficient of determination ($R^{2}$). When comparing the proposed model with current models, the model’s higher prediction accuracy is evident from lower statistical metrics. These indicators demonstrate the model’s increased capacity for precise prediction-making and serve as trustworthy gauges of its effectiveness. The continually lower results for each evaluation criterion suggest that forecasting accuracy and reliability, which are essential in many different applications and industries, may be improved.