Year: 2025
Type: article
Abstract: The efficient integration of solar energy into the power grid requires accurate regression of solar power generation and radiation levels. This work explores the development of "Solar Intelligence" - a system utilizing machine learning-based predictive models. These models will be trained on a multitude of data sources, including historical solar radiation measurements, weather forecasts, and environmental factors. By analyzing these complex relationships, Solar Intelligence aims to predict future solar power generation and radiation with high accuracy. This improved forecasting capability will empower grid operators to optimize energy production, integrate renewable sources seamlessly, and enhance overall grid stability. Furthermore, this "Solar Intelligence" system has the potential to revolutionize solar energy management for utilities and individual consumers, enabling informed decision-making and maximizing the utilization of this clean and sustainable energy source.
Author: Jerome Benny Hinndas D
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4409478966