Since the beginning of industrial revolution, there is an upsurge in global energy demands. Consumers are now expecting high quality and uninterrupted power supply 24/7. This puts an additional liability on the supply chain to increase energy generation sources.

Due to its low-carbon emission, renewable energy is often a popular source of electricity generation. However, its volatility imposes the risk of intermittent power supply and threatens utilities worldwide. Besides, grid stability is a major challenge for renewable energy network. Evidently, a strong energy supply chain that is stable and contains reliable assets is the key to mitigate such issues.

What drives energy storage?

Suitable energy storage has the potential for grid stabilization. It can mitigate the supply risks emergy from volatlie energy production. The domestic level usage also increases the need for energy storage for small to medium sized residential and commercial projects.

With improvements in lithium-ion batteries, there is a wider scope of deployment of energy-storage technologies and projects. Hence, new opportunities await the grid storage market. The need for maximize asset availabilities promotes the use of artificial intelligence (AI) and machine learning (ML). Besides it can ensure low-cost energy storage solutions for all stakeholders.  

How AI benefits this sector?

Presently, major roadblocks in energy storage are lack of innovation and planning, low storage capacity, battery downtime risks, low return of investments etc. There is a general lack of infrastructure to understand and utilize the potential of this market. This lags the development farther despite lucrative promises.

With monumental progress in the field of AI, it is time to use their data-driven approaches for exponential technical developments. AI-enabled systems can ensure real-time collection, analysis, and prediction of energy storage capacities. Furthermore, it can provide insights to optimize asset performance and grid stability by early detection of potential faults/failures.

What is SeetaLabs interested?

If storage systems were smarter, harnessing of renewable energy would be easier. As we realize the economic benefits of AI in condition monitoring, we take this as a perfect platform to display our skills for improving grid stability. Our capable team of experts can help you simplify the technological problems by drawing correct health frameworks for your assets and grids. Pairing your digital or analog monitoring platforms with our AI-enabled framework can accelerate your decision-making capabilities and reduce the planning time.  

If this interests you or you would like to have a first-hand experience, then click here to book a demo with us! We are more than eager to demonstrate RONIN as the first-ever intelligent health framework for oil-filled power transformers.