Digital Signal Processing and Artificial Intelligence for Automatic Learning Xiaojuan Chen 4 4 2025 https://doi.org/10.6025/dspaial/2025/4/4/147-154 https://www.dline.info/dspai/fulltext/v4n4/dspaiv4n4_1.pdf The paper proposes a deep learning based approach to enhance the efficiency, stability, and control of hybrid renewable energy systems. It addresses challenges such as the intermittency and variability of sources like solar, wind, and geothermal energy, which hinder optimal energy utilization and grid stability. The study introduces an LSTM based multi channel convolutional autoencoder model that effectively extracts temporal and spectral features from system signals, improving prediction accuracy by 9.8% over traditional CNN models. The architecture combines convolutional neural networks (CNNs) for noise reduction and feature extraction with long short term memory (LSTM) networks for capturing temporal dependencies. Experimental results using real photovoltaic data from Shaoxing, China (2014–2018) demonstrate that the Conv LSTM model achieves superior performance across key metrics MAAPE, RMSE, and MAE especially for forecasting beyond 15 minutes. The research also highlights the importance of integrating environmental variables, such as temperature, which shows a correlation coefficient of 0.5539 with solar output. By leveraging advanced deep learning techniques, the system enables adaptive control strategies, demand forecasting, and optimized energy scheduling, ultimately improving economic viability and operational reliability. Despite its promise, the approach faces challenges in data collection, model interpretability, and real time deployment, pointing to directions for future research.