Deep Learning Approach for Forecasting Renewable Energy Generation and Demand Patterns

Abstract

The increasing integration of renewable energy resources into modern power networks has created a strong demand for accurate forecasting systems capable of estimating future energy production and electricity consumption. Variations in solar irradiance, wind behavior, seasonal conditions, and user demand patterns often introduce instability in power distribution and energy scheduling. This study presents a hybrid deep learning architecture designed to analyze renewable energy generation and electricity demand using historical operational and meteorological datasets. The developed framework combines preprocessing techniques, temporal feature extraction, and sequence-learning networks to improve estimation reliability under changing environmental conditions. LSTM, GRU, and hybrid learning architectures were evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), Mean Absolute Percentage Error (MAPE), and coefficient of determination (R²). Experimental observations indicated that the integrated architecture achieved lower prediction errors and improved stability compared with conventional machine learning techniques. The findings also revealed that the system effectively captured short-term fluctuations and long-term temporal dependencies within the datasets. The developed approach can support intelligent grid management, optimized energy scheduling, and sustainable power-system planning in renewable-energy-based smart-grid environments.