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Data analytics meets data visualization
- Posted on August 9, 2012
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Yesterday's article, "How Electric Utilities View Analytics," set the stage for today's discussion about where utilities are most focused in their analytics efforts, and the opportunities that are open to them in the future.
As we discussed the SAS analytics research reported on yesterday, I also had the opportunity to ask Alyssa Farrell, SAS worldwide alliance and product marketing, energy and sustainability, about the biggest areas of interest and opportunity for utilities now within the area of data analytics.
One topic that has surfaced in a number of recent conversations within the industry, and came up in my discussion with Farrell as well, is the new ability to mesh data analytics with data visualization, providing utility operators to quickly view correlations in usage data, or to assess the state of assets on the grid by geospatially visualizing asset inventory, asset health and key performance indicators.
The key is that it's quick, and allows operators and even those in the field to visually explore vast amounts of data and quickly uncover insights and relationships in the data that may require further investigation. And, because it can be set up to include data across silos, to better look at data relationships, it can also assist in those tough areas of information sharing and collaboration discussed in yesterday's article.
"As utilities put asset sensors on more and more devices -- from transformers to communication boxes to smart meters -- sensor data and non-sensor variables, such as weather and line sensor signals, become increasingly hard to manage, much less analyze," Farrell said.
Forecasting capabilities and predictive asset maintenance, in fact, are two areas in which SAS is seeing a lot of activity, according to Farrell. Within the area of forecasting, utilities are looking to new data to improve the volume and granularity they now have in terms of information about electricity usage, to better forecast need.
And being able to predict, pre-failure, when an asset needs to be replaced strengthens the utility's ability to shorten or eliminate downtime. SAS has worked to codify a data management strategy with regard to predictive asset maintenance, with a foundation of technologies it launched in 2008. "It's not just asset maintenance, it's really predicting the life of the asset, thereby reducing the O&M cost for the utility," Farrell explained.
Clearly, predicting when equipment will need maintenance and having the ability to plan for its repair or replacement can save utilities billions of dollars over the years in overtime and outage duration losses, not to mention the regulatory inquiries, she added.
Another area in which she says there is a lot of activity is revenue assurance. "This is an area that has economic impact for utilities," Farrell said. "They are using analytics to assess the right payment options for customers, and are doing increased work with analytics in credit risk assessment."
The global issue of fraud is the big issue utilities are hoping to track. And here's an area in which data analytics can be of certain use in identifying both known and unknown data relationships. "There are different ways of finding fraud," Farrell explained. "It's not just looking at the specific meter. Utilities can also look at the collection points. There are some fascinating ways in which fraud or collusion is being detected."
The focus on data analytics for the utility industry has definitely grown, especially in the past two years. "Big Data," "Business Intelligence," "High Performance Analytics" and terms have become everyday language not only in the rarefied IT offices of yore, but also in the C-suites of today's evolving electric utilities. But the analytics of the past are being shored up even further in their capabilities by the abilities provided by near-real-time data. No longer does an electric utility have to look at grid events, asset performance and energy usage in a purely historical fashion.
These issues and more will be discussed and debated, and best practices shared, at Utility Analytics Week, Sept. 18-20, in Arlington, TX.
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