Why is AI the key driver for smart grid?
The transformation from yesterday’s grid – defined as a one-way energy transmission between producer and consumer – to the grid of the future, where there is a need for a two-way communication of data and energy, requires a whole new set of innovative solutions.
Transforming the grid into a vast network of intelligent nodes will require a significant number of sensors, which inevitably will lead to a huge amount of data collection resulting in a data lake without comparison. The natural next step is to extract information and gain insight from this vast amount of data. That means using analysis based on advanced AI software to tackle the velocity, variety, and volume of data. There is a need for advanced algorithms that will be able to detect complex patterns facilitating for the possibility to anticipate upcoming situations in the grid.
Therefore, AI will be essential when creating the smart grid, but that will not be enough on its own. We need to add the organizational aspect to the equation. We need to talk about working processes and behaviours, and not only information delivery. Information originating from the data lake needs to be contextualized, its meaning transformed into meaningful knowledge, allowing for further implementation in different parts of the organisation and its internal processes. We need less dashboard design and more data-driven behaviour. The contextualization of data and the actions based on that knowledge, are critical steps in the necessary transformation.
The transformation of the electrical grid is a step-by-step process, starting with connecting and collecting grid data, then gaining insights through AI powered analysis, and ending by converting information to meaningful knowledge. And that is what dLab’s dInsight Analytics platform is set up to do for you.