How Utilities Get Value with the Artificial Intelligence of Things

By Guest Blogger: Alyssa Farrell

Category:
Industry Trends

Artificial Intelligence (AI) in the Utilities industry – what’s the buzz all about?  The true value from IoT data is realized when it’s combined with advanced analytics and AI.  AIoT – the Artificial Intelligence of Things – is all about applying AI to data from smart devices and environments connected by the Internet of Things (IoT).

There are many definitions of AI, but I like this one: AI is the science of training systems to perform human tasks through learning and automation. With AI, machines can (1) learn from experience, (2) adjust to new inputs and (3) accomplish specific tasks without manual intervention.

From back office operations to drone-based line inspections, there are tremendous efficiencies to be gained in the utilities industry through automation with the assistance of intelligence algorithms on IoT data.  But first, it’s important to sort through the hype to get a clearer picture of the practical applications for utilities executives today.

To better understand the reality of AIoT in utilities and the business processes it’s impacting, I’ve been discussing scenarios with our customers and scanning analyst reports to uncover trends.  In the process, five distinct areas where AIoT can have a measurable impact on improved efficiencies and business performance have risen to the top:

Artificial Intelligence of Things .png

 

  1. Grid reliability. In the power grid itself, utilities can apply AI to the data from IoT devices in order to monitor and anticipate events that affect grid reliability. Utilities use troves of static data from smart line sensors and images collected from drones to build analytical models that can be deployed on data in motion or at rest. Artificial intelligence can anticipate grid disturbance and automatically issue a mitigating control to reduce or avoid an outage.
  2. Energy forecasting. Machine learning algorithms can predict energy demand and supply, then help optimize the dispatch and trading decisions within cost and emissions targets. Data used for energy forecasting is more dynamic than ever before, thanks to the IoT, from weather to real-time load. An adaptive machine learning algorithm may reduce forecast error and improve efficiency when the inputs are highly variable.
     
  3. Generation yield optimization. Energy production depends on the availability of generation assets. Utilities can use machine learning to optimize the startup / shut down process of rotating equipment, predict future maintenance needs based on performance degradations, and prevent unplanned downtime. McKinsey Global Insights writes, "machine learning can help optimize wind turbines’ yield based on their own past performance, real-time communication with other wind farms, the grid status, and changes in wind speed and direction."
     
  4. Microgrid management. The integration of Distributed Energy Resources (DERs), including microgrids, is a significant opportunity for the deployment of machine learning algorithms. AI can optimize IoT devices and orchestrate complex connections, but it must first be proven. If successful, AI/ML can improve the integration of renewable energy resources and reduce the losses that are experienced in energy grid today.
     
  5. Back office efficiencies. Utilities have significant back office processing, including new account setup and payment arrangements, fraud detection and claims processing. With natural language processing (NLP), a component of AI, many of these processes can be more efficient. For example, some utilities use virtual agents in call centers to listen to a caller’s purpose and automatically route them to the appropriate agent or online resolution based on service history. Utilities also rely on NLP to automatically classify property claims filed after major outage event. Machine learning algorithms improve models that predict non-technical loss and potential bad debt. Similar to opportunities to enhance general fraud detection through the application of AI, procurement integrity can be boosted with neural network techniques.

There’s no question that the next wave of innovation for utilities is where AI and IoT meet. As we look ahead to the digital utility of the future, it's apparent that strategic and operational decisions will be driven by better use of data and analytics in all forms, including AI.

About Your Guest Blogger:

Alyssa is responsible for marketing SAS solutions for the energy industries (utilities, oil and gas). She earned her MBA degree with a concentration in Management Information Systems from the University of Arizona. She also holds a Bachelor of Arts degree with honors from Duke University.  

Engage with ARC Advisory Group