Machine learning techniques look set to transform the way that utilities companies predict customer usage and production capacity in the years ahead. Utilities take note: when it comes to analyzing data, machine learning could be your best bet for achieving new insights, far outstripping other methods in terms of effectiveness, according to a new report published by analyst company Navigant Research, Machine Learning for the Digital Utility.
While machine learning has existed in parts of the ‘utility value chain’ for years, various drivers are expected to increase its use in other parts of the business, the report says. In particular, it has several advantages over other approaches when it comes to customer segmentation, pricing forecasts, anomaly decision, fraud detection and predictive maintenance. Basically, it’s about jobs that use the analytic processes of clustering, regression and classification.
“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 at Navigant.