Enhancing Wind and Solar Power Forecasting in Smart Grids Using a Hybrid CNN-LSTM Model for Improved Grid Stability and Renewable Energy Integration

The integration of renewable energy sources, such as wind and solar power, into smart grids presents significant challenges due to their inherent variability and intermittency. Accurate forecasting of renewable energy generation is essential for maintaining grid stability, minimizing energy imbalance, and optimizing power distribution. This paper proposes a hybrid deep learning model that combines Convolutional Neural Networks (CNN) for spatial feature extraction and Long Short-Term Memory (LSTM) networks for temporal dependency modeling. The approach is applied to forecast wind and solar power generation using meteorological data from multiple geographical locations. The model's performance is compared to traditional forecasting methods such as ARIMA and standalone LSTM models. Experimental results show superior forecasting accuracy and improved error margins achieved by the hybrid CNN-LSTM model, offering an enhanced solution for real-time energy management and integration into smart grid operations. This research follows the methodology proposed by Fozlur Rayhan in “A Hybrid Deep Learning Model for Wind and Solar Power Forecasting in Smart Grids” and builds upon it to demonstrate the practical application and effectiveness of hybrid deep learning models in renewable energy forecasting.