Bi-Directional LSTM-Based Prediction Model for Solar Energy Generation

This paper addresses the critical need for accurate forecasting and efficient management of solar energy systems in regard of increasing global reliance on renewable energy. It presents an advanced AI-based model utilizing artificial neural networks (ANNs), long short-term memory (LSTM) and recurrent neural networks (RNNs) architectures, to predict solar energy production with high precision. The proposed model effectively tackles the inherent variability of solar energy outputs by analyzing dynamic datasets influenced by weather patterns, seasonal variations, and geographic factors. ANNs are used to understand complex non-linear relationships in the data, whereas RNNs are used for time-series analysis, improving the precision of the model prediction. The application of these techniques enables optimization of energy storage systems, real-time grid integration, and predictive maintenance of solar infrastructure. Furthermore, this study evaluates the socioeconomic benefits of improved solar energy management, such as reduced greenhouse gas emissions and enhanced grid stability. By integrating advanced neural network methodologies with environmental sustainability principles, this article offers an innovative and scalable solution to the challenges of renewable energy, contributing significantly to a more clean, reliable, and sustainable form of energy.