Automation of the energy business processes using Machine Learning would play a big role in monitoring and preventing future hacking of the energy utility grids.
For instance, using ML algorithms to help analyze the satellite images and to infer the encroachment of the vegetation on the National Grid power supply lines to prevent any future power outages.
Another method is by using ML algorithms to analyze data from the sensors to monitor any form of critical infrastructure to predict future losses of data quality from degradation or any form of cyberattacks.
Use of ML and AI are critical for the energy industry in order to achieve aggressive decarbonization and also decentralization of energy supply and demand.
The use of ML in predictive maintenance of the energy industry assets, delivery of power to consumers, and automation of the business processes is necessary for future prevention of grid hacking.
Some of the companies and startups that uses ML and AI to provide solutions to this major problem include:
Using the available data to study and analyze the interaction will enable the energy company to understand the engagement of the customers. Through this, the energy company can now understand the needs of the customer and the assets interactions.
Additionally, ML can be useful in decarbonization of the energy utilities. The process will help in improvising new ways to operate and manage power grid supply more efficiently. This could lead to the net-zero world goal.
The next articles will analyze in details on how the above listed ML and AI companies operate and deliver the unique selling point to the energy utility companies.
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