Solar Power Prediction Using LSTM & RNN

Solar power prediction using Long Short-Term Memory (LSTM) and Recurrent Neural Networks (RNN) in Python is a crucial application of deep learning for renewable energy optimization. This study focuses on leveraging time- series forecasting capabilities of LSTM and RNN to predict solar power generation based on historical data, including temperature, sunlight intensity, humidity, and other meteorological factors. By preprocessing data, normalizing inputs, and training models using TensorFlow and Keras, the study enhances prediction accuracy. The comparative analysis of LSTM and standard RNN highlights the superior performance of LSTM in capturing long-term dependencies and mitigating vanishing gradient issues. The results demonstrate that deep learning models can effectively forecast solar energy output, aiding energy grid management and sustainable resource planning.Solar power is one of the most promising renewable energy sources, playing a crucial role in sustainable energy solutions. However, its efficiency depends on various meteorological factors, such as sunlight intensity, temperature, humidity, and cloud cover, making accurate prediction a challenging task. This study explores the application of LSTM and RNN models for predicting solar power generation using Python-based machine learning frameworks such as TensorFlow and Keras. By leveraging historical meteorological data, the proposed models aim to improve forecasting accuracy, aiding energy management systems in optimizing solar energy utilization and grid stability. The research also includes a comparative analysis of RNN and LSTM to assess their effectiveness in predicting solar power generation.