SOLAR ENERGY PRODUCTION ANALYSIS

Project Algorithm :

Recurrent Neural Networks (RNNs), Long Short-Term Memory (LSTM)

Project Overview :

The increasing global demand for renewable energy has made accurate prediction of solar energy generation crucial for efficient energy management and grid stability. This project focuses on developing a deep learning-based predictive model to forecast solar energy output based on historical and environmental data. By leveraging advancements in neural networks, the system aims to address the challenges posed by the intermittent and weather-dependent nature of solar energy. The proposed approach employs Recurrent Neural Networks (RNNs), particularly Long Short-Term Memory (LSTM) networks, to capture temporal dependencies in time-series data, such as solar irradiance, temperature, and weather conditions.

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