NEED for AI:
To improve productivity, reduce material waste, and support machines' diagnosis and to be used through all the stages of production.
AI REGIO SOLUTION:
Optimizing software systems by automatically adjusting their resource allocation based on real-time demand. It uses machine learning algorithms to predict resource needs and allocate them efficiently.
Improvement of the efficiency and reliability of solar energy production, reducing the cost and increasing the profitability of solar power generation
The technology's ability to track the sun's movements in real-time can capture more solar energy throughout the day, resulting in increased energy output
Detecting of potential issues before they become major problems, reducing downtime and maintenance costs
The system's integration with existing solar power infrastructure can be easily adopted by solar power plants without requiring significant changes to their existing setups
The SANDMAN project is addressing the challenges of applying machine learning techniques to Industry 4.0, including ML frameworks, meaningful data, and application use cases. SANDMAN aims to develop an innovative predictive maintenance application based on Deep Learning techniques for smart pipes for water distribution and critical infrastructure. The DL toolkit that will be developed in the SANDMAN context presents some innovations, such as being built on top of well-known AI and machine learning libraries, implemented in Python, and offering insights about the analyzed data.