Subproject goals and how will FLUIDOS help you achieve them

The primary subproject goals are as follows:

1. Demonstrate the proper functioning of the proposed nowcasting service within the FLUIDOS infrastructure. The workflow, whether executed on the Edge or in the Cloud, must be capable of providing real-time predictions of photovoltaic generation.
2. Improve accuracy in the predictions. The goal is to attain accuracy in typical error estimation indicators (MAE, MBE, rMBE, RMSE, rRMSE), at least, slightly better to those found in scientific literature.

The FLUIDOS architecture provides several distinct advantages over current state-of-the-art solutions, particularly in terms of latency reduction, prediction accuracy, scalability, and cybersecurity, which are essential to achieve the subproject goals:

1. Faster Prediction Time
   FLUIDOS enables significant reductions in prediction time by leveraging edge-based processing for real-time data analysis. Traditional cloud-only systems suffer from latency caused by network delays and the need to transfer large volumes of data for processing. FLUIDOS, with its hybrid edge-cloud architecture, allows critical low-latency tasks (such as cloud cover and weather analysis) to be processed locally at the edge, while more computationally intensive processes (e.g., AI model training and WRF simulations) are offloaded to the cloud. This optimizes resource usage and dramatically improves prediction speed.
2. Improved Prediction Accuracy
   The integration of real-time data and AI models in FLUIDOS ensures more accurate energy production forecasting. Traditional batch-processing approaches lack the ability to continuously refine predictions as new data becomes available, leading to inaccuracies. FLUIDOS solves this by dynamically integrating real-time data from multiple sources (e.g., cameras, sensors, and SCADA) and processing it in real-time, allowing for continuous model updates and more accurate forecasts.
3. Scalability and Automation
   Current technologies often require manual configuration and are difficult to scale across multiple sites. FLUIDOS addresses this by automating the integration of data from external sources (such as weather APIs and SCADA systems) and dynamically managing resource allocation between edge and cloud environments. This allows the system to scale effortlessly across multiple photovoltaic plants without requiring extensive manual intervention.
4. Enhanced Cybersecurity
   FLUIDOS includes advanced cybersecurity features, ensuring that all data transferred between edge devices and cloud environments is encrypted and secure. This is especially important for critical infrastructure like photovoltaic plants, where sensitive SCADA data must be protected from potential cyberattacks.
5. Cost Efficiency
   By dynamically managing the distribution of computational tasks between edge and cloud resources, FLUIDOS reduces operational costs. Processing real-time data at the edge minimizes the amount of data that needs to be sent to the cloud, reducing bandwidth usage and cloud storage costs. Additionally, FLUIDOS automates many tasks that would otherwise require manual oversight, further reducing labor costs.