Keeping the lights on and power grids stable in Belgium

Eandis turns to SAS® Visual Analytics for a self-service BI environment enabling advanced smart grid analytics

As more homeowners adopt renewable energy sources like solar panels and select energy providers that have “green” power options, utility companies must evaluate the readiness of the grid. While traditional energy sources like hydro, nuclear, gas and coal provide consistent energy, renewable energy sources vary based on weather conditions or time of day. And if these power sources are not managed properly, they can affect the stability and quality of electricity – and even damage power lines and other grid equipment.

To address these factors – and meet the European Union’s goals to increase energy efficiency by 2020 – Eandis implemented SAS Visual Analytics to better visualize the volumes of grid data available for analysis. The largest distribution operator in Belgium now has the tools to better manage a new smart grid that meets consumer demands while modernizing its infrastructure to accommodate renewable energy sources.

Evolving with a changing energy environment

For decades, Eandis had a classic distribution model for electricity and natural gas, which it provides to 229 cities and municipalities in Belgium. Its system used cables and gas pipelines to transport energy from the point of generation to the point of consumption. But that dynamic is changing, thanks to solar and wind power at the consumer side of the grid.

“Rather than a one-direction flow, energy goes both ways now,” explains Olivier Goethals, Enterprise Architect and Manager of the BI Competency Center (BICC) at Eandis. “And this is a big challenge for the distribution grid, because there’s a lot of pressure on it from a technical standpoint. The flow can deteriorate the powerlines and affect the stability of the electricity grid. For example, the lights might flicker in your house because there’s some variance on the voltage. And this increases if the grid isn’t managed properly.”

To address this decentralized production of energy, Eandis is upgrading its power grid to manage the two-way flow of energy. The company is also developing a smart grid that uses digital technology and sensors to continuously track energy usage, detect abnormalities and have better insight on consumers’ electricity use.

With SAS Visual Analytics, our analyst was able to complete that same job within minutes [versus three to six months], and in a matter of seconds he could visualize and manipulate the data, add columns and do calculations. It was truly amazing.
Olivier Goethals

Olivier Goethals
Enterprise Architect and Manager of the BI Competency Center

A legacy system unable to look ahead

“The energy business used to be an engineering business, all about managing pipes and wires,” Goethals says. “Now, it’s increasingly about managing data.”

In the past, Eandis used traditional methods of reporting. If a business unit needed a custom report, the IT department captured the requirements, developed the extractions, filled the data warehouse and built the reports.

The entire process was slow and frustrating, and the results weren’t easy to interpret. At the heart of the problem, the old system was set up for hindsight, to learn more about past events. But Eandis needed to ask questions about the future, like how many electric cars will there be in five years? Where will those cars be located? And more importantly, how will these cars affect the power grid?

A space for data experimentation and exploration

With SAS, Eandis now has a sandbox environment where users can test out models and the types of visualizations – like graphs and charts – that might be useful to them. When the analysts are satisfied with the candidate model, IT helps them move the predictive model into production.

“Not only is SAS Visual Analytics easy to use, but it allows us to explore big data and make decisions much faster than before,” Goethals says.

For example, every year Eandis measures the in-feed data from all the transmission stations supplying power to its grid, creating 10 million lines of measurements – enough to fill nine Excel spreadsheets. The analysis of that data took three to six months. “With SAS Visual Analytics, our analyst was able to complete that same job within minutes, and in a matter of seconds he could visualize and manipulate the data, add columns and do calculations,” Goethals says. “It was truly amazing.” The time gained can be used to analyze the data and make different alternative models, allowing faster and well-founded investment decisions, such as when to build new transmission stations.

Another benefit Goethals cites is that reports produced in SAS Visual Analytics are easy to interpret and support better decisions. “When reports are just a list of numbers, they’re unattractive or intimidating,” he says. “Now that our reports are visually compelling, user-friendly and refresh faster, we can make sense of the complex data and ultimately make sound business decisions.”

A greener future with increased consumer engagement

Eandis’ recent analysis shows that green energy production is increasing, but overall energy consumption is still going up. It’s looking to reverse that trend by educating consumers and further developing its smart grid project.

“We discovered that many people are unaware of just how much energy they consume, or they’re unsure if their consumption rates are considered high or low,” Goethals explains. “By introducing smart meters and creating a ‘smart consumers’ program we can promote energy awareness and encourage conservation efforts. Together, we can adapt to the changing energy landscape while being mindful of the environment.”



  • Better analyze energy consumption, develop a smart grid and encourage energy conservation among consumers.
  • Create a self-service BI environment where business users can explore huge volumes of data on their own.



  • Better manage a new smart grid to meet consumer demands.
  • Modernize its infrastructure to accommodate renewable energy sources.
  • Decrease time spent measuring the in-feed data from transmission stations supplying power: A project that once took three to six months can now be completed within minutes.
  • Plan for efficient use of government funds for the smart grid.
  • Pose “what if” questions of the data and explore numerous business scenarios.
  • Make sound business decisions much faster than before.
The results illustrated in this article are specific to the particular situations, business models, data input, and computing environments described herein. Each SAS customer’s experience is unique based on business and technical variables and all statements must be considered non-typical. Actual savings, results, and performance characteristics will vary depending on individual customer configurations and conditions. SAS does not guarantee or represent that every customer will achieve similar results. The only warranties for SAS products and services are those that are set forth in the express warranty statements in the written agreement for such products and services. Nothing herein should be construed as constituting an additional warranty. Customers have shared their successes with SAS as part of an agreed-upon contractual exchange or project success summarization following a successful implementation of SAS software. Brand and product names are trademarks of their respective companies.

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