Energy management: Machine learning in the utilities industry
Power management: Navigant Research report finds 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, report finds.
Energy management: 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. 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.
About Navigant Research
Navigant Research, the dedicated research arm of Navigant, provides market research and benchmarking services for rapidly changing and often highly regulated industries. In the energy sector, Navigant Research focuses on in-depth analysis and reporting about global clean technology markets. The team’s research methodology combines supply-side industry analysis, end-user primary research and demand assessment, and deep examination of technology trends to provide a comprehensive view of the Energy Technologies, Utility Transformations, Transportation Efficiencies, and Buildings Innovations sectors.
Navigant Consulting, Inc. is a specialized, global professional services firm that helps clients take control of their future. Navigant’s professionals apply deep industry knowledge, substantive technical expertise, and an enterprising approach to help clients build, manage and/or protect their business interests. With a focus on markets and clients facing transformational change and significant regulatory or legal pressures, the Firm primarily serves clients in the healthcare, energy and financial services industries. Across a range of advisory, consulting, outsourcing, and technology/analytics services, Navigant’s practitioners bring sharp insight that pinpoints opportunities and delivers powerful results.