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2020-09-21 12:10:00

ARTIFICIAL INTELLIGENCE FOR ENERGY UTILITIES

ARTIFICIAL INTELLIGENCE FOR ENERGY UTILITIES

By  Paul Korzeniowski  B2B Content producer Self-employed

ENERGYCENTRAL - Sep 16, 2020 - Artificial Intelligence Becomes Key in Utility Applications

Utilities find themselves collecting more information nowadays than ever before. Acting upon that information is challenging, but artificial intelligence is an emerging software area that could help with the process. Most utilities have limited experience working with the technology, so best practices are needed. 

Know Thy Data

Utilities do not want to simply collect information. They need to correlate it, see connections, and make changes to improve the business. 

So when working with information, it become important for them to understand the data and what it means to the business. That understanding often varies by department. For instance, the field service team would need customer information for location purposes, and the customer care team needs to monitor energy usage. When data is loaded into an AI application, such differences need to be accounted for. 

Start Simple

AI has vast potential but also prominent pitfalls. In collecting terabyte or petabytes of information, utilities have the potential of getting lost in the data, digging too deeply, and making the process of arriving at a conclusion challenging, sometimes impossible. In some cases, they enter too many variables or have vague potential outcomes. In addition, implementing, training, and evaluating a simple model takes much less time than a sophisticated one. Rather than boil the ocean, they should start with a simple tangible and reasonable metric, say reduced service time in the call center and build up from there.

Limit the Time Needed for Fine Tuning

The results from any AI model will never reach 100% because the tools are based on probabilities and leave room for something unexpected. Results in the 70% and higher range are often considered actionable. Sometimes, businesses try to raise the probability by tuning their AI model. Such quests chew up time, money, and resources. These projects, like all others, are usually time-constrained, so a wise approach to fine tuning seems to be needed.  Close enough to the original goal results often should be sufficient enough to label the project a success and move from examining your operation to improving it.

AI is an emerging technology that is changing how utilities build enterprise applications. The software has tremendous potential, but implementing it wisely requires that energy companies dig into their data at the start of the project, set reasonable objectives, and focus on delivering highly likely correlations rather than perfect ones.

 

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This thought leadership article was originally shared with Energy Central's Digital Utility Community Group. The communities are a place where professionals in the power industry can share, learn and connect in a collaborative environment. Join the Digital Utility Community today and learn from others who work in the industry.

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Tags: ARTIFICIAL, INTELLIGENCE, ENERGY, UTILITIES