A Hybrid Deep Learning Model for Wind and Solar Power Forecasting in Smart Grids

Accurate forecasting of renewable energy sources, such as wind and solar power, is crucial for the effective operation of smart grids. Traditional forecasting models often struggle to handle the complex, non-linear, and time-varying nature of renewable energy. This paper proposes a hybrid deep learning model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks for enhanced forecasting accuracy. The CNN is used to extract spatial features from weather-related data, while the LSTM handles temporal dependencies in the power generation patterns. The model is tested on wind and solar power data from various geographical locations. Experimental results demonstrate the superior performance of the hybrid model in comparison to traditional methods, with improved forecasting accuracy and reduced error margins. This work contributes to the optimization of smart grid management and better integration of renewable energy sources into power systems.