Leveraging CNN-LSTM for Enhanced Solar Irradiation Forecasting via Hybrid Deep Learning
Keywords:
Solar irradiation forecasting, Deep Learning, Hybrid Models, Convolutional Neural Networks (CNNs), Long Short-term Memory (LSTM), Renewable Energy, Energy Prediction, Meteorological Variables, Solar Energy IntegrationAbstract
The sophisticated method of ultraviolet (UV) radiation prediction presented in this study uses an innovative deep learning structure that combines LSTM (Long Short-Term Memory) networks with Convolutional neural network models (CNNs). The suggested approach, called SunNet, combines the capacity of LSTM networks to learn temporal sequences alongside the geographical extraction of features capability of CNNs with the goal of enhancing the precision and dependability of sun irradiance forecasts. With enhanced accuracy and resilience, SunNet is taught to anticipate solar irradiation by utilizing weather-related variables and previously collected information on the sun's irradiance as inputting characteristics. Performing better than both independent deep learning algorithms and conventional predicting methodologies, findings from experiments show how effective the suggested hybrid deep learning approach is. An effective way to maximize solar energy production and make it easier to integrate solar power into the energy grid is through the combination of CNNs and LSTMs in SunNet.
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