General Electric Co.’s subsidiary GE Power wants to help its electrical grid customers keep a better handle on their operations with a suite of new artificial intelligence-based analytics products that it says can make better use of their network data.

The new products leverage GE’s latest machine learning technology, which is a subset of AI that enables machines to learn and improve from experience without being explicitly programmed. They rely on data pulled from electrical grid operators’ networks, and are designed to boost operational efficiency, GE said.

The products include Storm Readiness, which is a tool that makes use of high-resolution weather forecasts, previous outage history and response data to inform users about the risk of impending bad weather. The idea is to prepare response teams ahead of time, so they can work to predict future outages and reduce the time it takes to get things back online.

“[Storm Readiness] will enable the utilities to become more surgical in prepositioning crews in advance of weather events, saving time, money, improving customer satisfaction and enhancing safety for employees,” said Brian Hurst, vice president and chief analytics officer of Exelon Utilities Corp., an early adopter of the tool.

Then there’s Network Connectivity, which is designed to correct and maintain network data integrity, GE said. Errors in network data, which usually happen when information is inputted manually, can cause big problems for grid operators, mainly by hindering their response to outages. The new product uses AI to detect and correct such errors in order to prevent such problems.

The third and final product in GE’s new suite is Effective Inertia, which is meant to deliver more visibility into transmission system operations. GE said this is particulalry important for suppliers of renewable energy, which often causes a “displacement of system inertia, a property of the grid that controls the rate of change of frequency when there is a power imbalance.”

According to GE, ineffective management of these transmission systems can result in blackouts, as well as big financial penalties for grid operators. Effective Inertia’s machine learning algorithms work by measuring and forecasting system inertia to help ensure more stability in the electrical grid.

“The energy industry today is leveraging a small fraction of their operational data,” said Steven Martin, acting chief executive officer for GE Digital and chief digital officer at GE Power. “Our Grid Analytics enable utilities to use more of that data and orchestrate their networks and the workers who operate them in ways previously unimagined.”

GE Power’s push into AI-based analytics might raise a few eyebrows given reports last year that its parent GE was reportedly looking to sell off the bulk of its digital assets after failing to establish itself as a leader in the so-called “industrial internet of things.”

The company reportedly gave up on its ambitions due to the competition it faced from bigger software firms such as IBM Corp. and Microsoft Corp. However, its failure to find a buyer for its digital assets so far and the release of today’s products suggests it may not have thrown in the towel just yet.

Analyst Holger Mueller of Constellation Research Inc. told SiliconANGLE it wasn’t a surprise that GE is continuing its software development efforts, since they’re an essential component of its industrial products.

“The only alternative for GE would be to partner, but evidently they have not found a suitable one yet,” Mueller said. “That said, the positioning of these new capabilities is rather conservative, as ‘predictive analytics’ was a buzzword more than a decade ago. But [the new tools] still deliver value.”

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