Machine Learning Has Several Advantages Over Existing Utility Analytics Techniques

Falling costs, new technological advancements, and a fresh approach to analytics procurement make machine learning deployments easier than ever

A new report from Navigant Research examines use cases for machine learning in the utilities industry, detailing its advantages over other analytics techniques, and providing future requirements and recommendations.

Machine learning is rapidly moving into the mainstream and is high on the agenda of many utilities. While the technology has existed in parts of the utility value chain for years, various drivers are expected to increase its use throughout other areas of the business. Click to tweet: According to a new report from @NavigantRSRCH, machine learning has several advantages over existing utility analytics techniques when performing customer segmentation, pricing forecasts, anomaly detection, fraud detection, and predictive maintenance.

“The utilities industry is already using self-learning algorithms, particularly in the field of asset monitoring and predictive maintenance, and several reasons suggest the use of machine learning will expand to many more use cases and its adoption will accelerate,” says Stuart Ravens, principal research analyst with Navigant Research. “During the past decade, it has become easier for companies to deploy machine learning thanks to falling costs, new technological advancements, a softening of conservative attitudes, and a fresh approach to analytics procurement.”

Utilities are encouraged to investigate how and where machine learning can help their businesses now and in the future, but should be aware of existing limitations. According to the report, machine learning is best suited for a handful of specific analytical processes, including clustering, regression, and classification.

The report, Machine Learning for the Digital Utility, describes several use cases for machine learning and examines why machine learning has an advantage over existing analytics techniques. Future requirements for machine learning—specifically for distributed energy resources (DER) management and transactive energy—are also discussed, as are several recommendations for utilities developing their machine learning strategies. An Executive Summary of the report is available for free download on the Navigant Research website.

Contact: Lindsay Funicello-Paul


* The information contained in this press release concerning the report, Machine Learning for the Digital Utility, is a summary and reflects Navigant Research’s current expectations based on market data and trend analysis. Market predictions and expectations are inherently uncertain and actual results may differ materially from those contained in this press release or the report. Please refer to the full report for a complete understanding of the assumptions underlying the report’s conclusions and the methodologies used to create the report. Neither Navigant Research nor Navigant undertakes any obligation to update any of the information contained in this press release or the report.

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