Preeti
Researchers investigated a new deep learning approach for forecasting solar power generation (SPG) across multiple sites, aiming to develop a scalable and accurate model that addresses the limitations of traditional site-specific models. The study focused on the importance of accurate SPG forecasting for grid stability and maximizing solar energy utilization efficiency. The proposed model utilized deep learning techniques, including convolutional neural networks (CNNs) and long short-term memory (LSTM) networks, to extract features from weather data and predict solar power output.
The research findings showed that a common model with a classifier module improved forecasting accuracy across new and diverse locations, surpassing regulatory requirements for renewable energy generation forecasting. Transfer learning (TL) further enhanced prediction accuracy, especially for sites with unique meteorological conditions. The model has significant implications for the renewable energy sector, aiding in better grid management and planning, and supporting the global shift towards sustainable energy sources.
Future work should focus on optimizing the configuration of common models, exploring hybrid models, and incorporating seasonal variations to enhance accuracy and reliability. Overall, the deep learning-based approach shows promise in improving solar power forecasting across multiple sites and supporting the integration of renewable energy into power grids.
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