Written By: Thanassis Zournatzis, AI Engineer.
Global society increasingly demands more participation for creating a carbon-free world. The concepts and hardware technologies for achieving Net Zero are in place. Cities are transforming into smart cities. So, what needs to be done for accelerating the green energy transition?
The massive adoption of Artificial Intelligence is key to implementing actions for modernizing and optimizing energy supply chains. Energy AI is here to provide the sum of Artificial Technology techniques and applications for making the Energy sector greener and smarter. And the challenge for Energy Data Scientists is to bring AI as close as possible to energy consumers.
However, the daunting task of bringing Energy AI to the doorstep of every household has the precondition of making energy-related hardware, at the edge, smarter. This can be achieved by embedding AI into inverters, Battery Energy Storage Systems (BESS), metering and submetering equipment for making heterogeneous energy hardware equipment collaborative and integrated. This way, Energy Data Scientists enable more participation in the context of decentralizing energy production towards creating microgrids and prosumer communities.
So, the opportunity window is wide open for improving available energy hardware with the power of AI. For example, scientists all over the world are looking at ways of optimizing BESS.
To all this, a key contributor is the IoT technology, that allows continuous and detailed monitoring of the system status, while the edge processing capabilities allow the deployment of any AI algorithm next to or even inside the BESS.
Regarding the AI-powered optimization, it must be not only multi-parametric and multi-objective, but also adaptive. In addition, the optimization should be personalized in terms of capturing user's energy consumption behavior. Accordingly, the decision to optimize the battery itself or the rest of the system by adjusting the BESS operating mode will be done dynamically by AI models that can process and interpret data at the edge in real time, without human intervention.
In conclusion, embedding Energy AI into edge devices is a precondition for accelerating the green energy transition. A new era for Energy AI is now starting. Stay tuned for more embedded Energy AI!
