Climate Change

22 May 2022

How AI Innovation Is Enabling the Energy Transition

22 May 2022  by   
Artificial intelligence (AI) is being introduced in utility use cases from renewables integration to resilience and more.

While AI is not new, modern IT technologies with advanced machine learning techniques are revolutionising the concept – not as some would have it to replace human intelligence but rather to bring analytical capabilities to potentially large, multiple disparate datasets for modelling and management on an approaching near real time basis.

Couple this with other modern IT developments such as the cloud, which is enabling ready access to AI technologies, and edge intelligence with the data management and latency benefits it brings, it is of no surprise that AI is now being widely employed in the energy sector – as it is in others throughout the economy – and in use cases across the utility business.

In some cases, it’s about identifying anomalies in data, which is used in for example cybersecurity applications and asset monitoring, while in others it’s about identifying past trends to predict future trends, such as in for example weather and renewables output forecasting.

In the future and in particular with the growth of 5G and then 6G and ongoing IT innovations, AI is expected increasingly to drive autonomy in which machines such as robots and driverless cars and potentially the future distributed power grid, are able to operate without human intervention.

Ultimately, it’s about realising the full value of all the data that is available to meet the business needs.

AI example use cases

One company that has developed multiple AI use cases is Silicon Valley headquartered NVIDIA and after having set its stall revolutionising the gaming industry has expanded into others including energy.

In one example around the implementation of renewable energy resources NVIDIA worked with the British startup Zenotech to model the energy output of large offshore wind farms, taking account of the complex wind flows that occur around wind turbines with the different wind speeds and directions that result in their wake as they impact on each other.

In another example, Siemens Energy is using NVIDIA’s inferencing technology for optimising power plants across the globe through monitoring for predictive servicing and eventually autonomous power plants.

In a third the company worked with US researchers to develop an architecture for ExxonMobil to select suitable carbon sequestration sites with modelling taking account of complex large scale subsurface flow models to enable excessive pressure build up to be avoided.

Another company with a heritage in AI is another Silicon Valley headquartered operation, AiDash, which specialises in applying AI techniques to satellite based imagery for multiple industries.

In the power sector offerings range from vegetation management to remote monitoring and inspection of hazards along powerlines, all of which can improve the operations and maintenance procedures for utilities.

Both NVIDIA and AiDash are exhibiting at Distributech International 2022 in Dallas, TX from May 23-25.


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