With the imminent changes in the energy landscape, it is now the best time to combine machine learning and the power grid.
Machine learning may be used as an economic modeling tool to evaluate strategic development and decision-making related to the use of grid reinforcement solutions through cost-benefit analysis. In the future, we will not only respond to failures, but also use models that predict failures by analyzing technical and economic data to predict and avoid failures. Therefore, through machine learning, the power industry has taken a step forward in developing active systems rather than passive systems.
The scientific community is already studying the bright prospects of "smart" energy and machine learning in power networks. There have been many statements about the prediction of energy demand, the prediction of solar power generation, and even the accurate prediction of the energy that can be collected from food waste in the urban environment. Considering the in-depth understanding and extensive use of AI and machine learning in other fields, as we transition to a zero-net economy and society, the possibilities in the power grid are exciting.