  As solar power continues to grow and replace traditional energy sources, the
need for reliable forecasting models becomes increasingly important to ensure
the stability and efficiency of the grid. However, the management of these
models still needs to be improved, and new tools and technologies are required
to handle the deployment and control of solar facilities. This work introduces
a novel framework named Cloud-based Analysis and Integration for Data
Efficiency (CAIDE), designed for real-time monitoring, management, and
forecasting of solar irradiance sensor farms. CAIDE is designed to manage
multiple sensor farms simultaneously while improving predictive models in
real-time using well-grounded Modeling and Simulation (M&S) methodologies. The
framework leverages Model Based Systems Engineering (MBSE) and an Internet of
Things (IoT) infrastructure to support the deployment and analysis of solar
plants in dynamic environments. The system can adapt and re-train the model
when given incorrect results, ensuring that forecasts remain accurate and
up-to-date. Furthermore, CAIDE can be executed in sequential, parallel, and
distributed architectures, assuring scalability. The effectiveness of CAIDE is
demonstrated in a complex scenario composed of several solar irradiance sensor
farms connected to a centralized management system. Our results show that CAIDE
is scalable and effective in managing and forecasting solar power production
while improving the accuracy of predictive models in real time. The framework
has important implications for the deployment of solar plants and the future of
renewable energy sources.
