International Journal of Research and Development in Engineering Sciences https://www.ijrdes.com Chigilipalli Bharat Kumar 8 5 (September - October) 2026 10 September-October enSolar Energy PredictionLSTMTime Series ForecastingDLRenewable Energy Management. Renewable energy sources particularly solar energy are essential to meeting the worlds growing energy needs Realtime prediction of DC power generation is necessary for optimizing energy management and utilization of solar energy We initially used regression models like Linear Regression XGBoost MLP Regressor and Random Forest to predict DC power directly While these models showed good accuracy they were not ideal for timeseries forecasting To eliminate this limitation we subsequently employed deep learning models LSTM 1D CNN and GRU to predict key features like AC frequency AC voltage DC link voltage energy today output current total energy output power DC pyranometer reading temperature and power factor based on date and time as input These forecasted features were further used to predict the target variable DC Power The LSTM model most accurately predicted the intermediate features and the end DC power output among the deep learning models Our LSTM network was trained on real data and achieved a Root Mean Squared Error RMSE of 0039 a Mean Absolute Error MAE of 0020 and an R score of 0909 indicating high prediction accuracy Copyright (c) 2026 International Journal of Research and Development in Engineering Sciences. All rights reserved. 2026 All Rights Reserved