Artificial Intelligence

AI: A Game Changer for Renewable Energy Storage

Artificial Intelligence is transforming every industry, and renewable energy is no exception.  State-of the-art machine learning capabilities (e.g., deep learning) from the likes of Google, Microsoft and AWS, are readily available over the cloud to businesses of large and small.  By applying such powerful technologies to ever growing varieties and volumes of data, these organizations can easily analyze the past, optimize the present and predict the future. In doing so, they are using AI in transforming their own businesses by, for example, improving supply chain transparency and efficiency, reducing product defects and downtime, and increasing product safety and salespeople productivity, and improving customer experience.

Energy storage holds the key to overcoming the intermittency challenge of solar and wind and, therefore, to the ultimate future of renewables. Fire safety, along with high cost and limited cycle-life and c-rates are considered among top barriers to the broad adoption of battery technologies. While battery storage is a living system, it is commonly treated as a black box. As such, nobody knows what’s happening inside the box, which is what makes batteries unsafe.

With our unique, AI-embedded system design, we turn this black box into a clear box. In doing so, we can continuously analyze subtle changes occurring in battery cells, and infer about the real-time safety status of the battery. We can predict the battery fire risk long before such an event would actually occur. In that case, the system recommends replacing questionable battery modules to prevent potential fire hazards as part of routine maintenance.

AI BESS Systems: The Future of Intelligent Renewal Energy Is Here

  • Unparalleled Fire-Safe Energy Storage:  By combining LFP chemistry with data-driven intelligent edge controls, AGreatE delivers the industry’s safest batteries in the marketplace.
  • Competitive Total Cost of Ownership (TCO):  As an AI-first company, we apply AI to optimize every facet of our business, from supply chain and manufacturing to sales, services and support. We pass on these achieved savings to our customers by offering lower prices and better ROI.
  • Ease of Installation and Maintenance:  Our plug-n-play design, along with intelligent self-checks and diagnosis, and predictive maintenance, can significantly reduce initial setup time and downtime due to unexpected system failures.
  • Catalyzing Low-Carbon Planet through Microgrids:  AGreatE’s commitment to renewables goes beyond battery storage.  We also provide intelligent turnkey microgrid solutions to commercial & industrial, residential and utilities clients

AI & Battery Safety

In addition to battery safety, AGreatE also applies AI to improve other aspects of energy storage systems, including cost, cycle-life, system uptime and c-rates, through preventative maintenance.  In our turnkey renewal energy solutions, we leverage AI to drive down the total cost of ownership (TCO) and achieve better ROI through adaptive learning and continuous learning by tapping into additional data sources, such as customer energy usage behaviors and real-time electricity pricing

Deep Learning AI

Machine learning is the main reason for the renewed interest in artificial intelligence, but deep learning is where the most exciting innovations are happening today. Considered by some to be a subfield of machine learning, this new approach to AI is informed by neurological insights about how the human brain functions and the way that neurons connect with one another.

Deep learning systems are formed of artificial neural networks that exist on multiple layers (hence the word ‘deep’), with each layer given the task of making sense of a different pattern in images, sounds or texts. The first layer may detect rudimentary patterns, for example the outline of an object, whereas the next layer may identify a band of colours. And the process is repeated across all the layers and across all the data until the system can cluster the various patterns to create distinct categories of, say, objects or words

Predictive Maintenance

Once the batteries have populated enough data, these platforms help in predicting (way ahead of time) possible failures, or the need to change batteries.

Alerts can be set for each and every battery asset depending on the parameter thresholds set by the energy storage operator. Some of the more advanced platforms let you tag multiple assets and multiple tags for every battery asset. This enables the operator to view only those batteries that fall under a specific tag – geography, operating temperatures, battery types, etc.