AI-Powered Renewable Energy Forecasting: A Hybrid Deep Learning and Physics-Based Model for Solar and Wind Energy Prediction in Smart Grid Applications
DOI:
https://doi.org/10.63282/3050-9246.IJETCSIT-V2I2P101Keywords:
- Hybrid Model, Renewable Energy, Deep Learning, CNN, LSTM, Physics-Based Model, Energy Forecasting, Smart Grid, Time-Series Prediction, Machine LearningAbstract
The integration of renewable energy sources (RES) into smart grids presents significant challenges due to the intermittent and unpredictable nature of solar and wind energy. Accurate forecasting of these energy sources is crucial for optimizing grid operations, ensuring reliability, and reducing costs. This paper proposes a hybrid deep learning and physics-based model for predicting solar and wind energy generation. The model combines the strengths of data-driven deep learning techniques with the physical principles governing renewable energy systems. Specifically, we integrate convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and physics-based models to create a robust forecasting framework. The proposed model is validated using real-world data from multiple solar and wind farms, demonstrating superior accuracy and reliability compared to existing methods. The results highlight the potential of hybrid models in enhancing the integration of renewable energy into smart grids
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