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.
Learning how to integrate renewables
As we see it at Internet of Business, this could be of particular interest to utilities struggling to integrate renewable energy with more traditional sources of power supply. Wind and solar power is erratic – it’s hard to predict how much energy a utility can harness this way unless it knows exactly how long and how hard the wind will blow and/or the sun will shine.
Here, machine learning could provide some insight, enabling utilities to better predict renewable production and integrate it with other forms of supply. That’s the theory, at least, that software company Powel has tested out with a large utility in Norway, along with analytics specialist Swhere, on a project that applied machine learning for wind forecasting.
This involved answering three questions: When does wind occur, how powerful is it and in what direction does it blow? According to Swhere founder Dr Ernst van Duijn, the goal was to use machine learning algorithms to detect patterns in the wind that could lead to deeper insight into production capability. The results suggest it’s possible to reduce uncertainty in wind power production by more than 45% and, as a result, cut the costs of penalties that are applied when a utility is unable to meet its commitment to provide a certain amount of renewable energy.
Learning how to digitally disrupt
Machine learning may also be an enabler of entirely new utilities companies – ‘disrupters’ who are looking to topple incumbent providers. Take Drift, for example, a start-up power utility company based in Seattle that is using a combination of machine learning technologies, among others, to provide customers with cheaper wholesale energy prices, through being able to more accurately predict their consumption.
Fortunately, machine learning technologies have never been more accessible to utilities of all types and all sizes. According to Stuart Ravens at 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,” he adds.
That’s good news because, for utilities, machine learning may not just be what they need to thrive in the digital age. It may even be what they need to survive.
Coming soon: Our Internet of Energy event will be taking place in Berlin, Germany on 6 & 7 March 2018. Attendees will hear how companies in this sector are harnessing the power of IoT to transform distributed energy resources.