A cold front moves down from Canada, and heaters start humming. A holiday comes along, and businesses shut down. Many different factors can influence natural gas use on any given day. To provide you, our customers, with reliable and cost-effective service, we depend on accurate forecasts of natural gas demand.
Those forecasts are made with the help of software developed by researchers and engineers at Marquette University. Director Ronald Brown launched Marquette’s GasDay program in 1993. We Energies was one of the first companies to test the lab’s models. Now approximately 35 natural gas companies rely on GasDay software nationwide, and our natural gas controllers use it to analyze demand and guide natural gas flow on a daily basis.
The software evaluates weather and market data to provide a “point forecast,” a single number representing how much natural gas customers will need. In 2016, natural gas controllers asked the GasDay researchers if they could provide even more information. It would be helpful to see the total range of possibilities in addition to the most likely forecast.
|Saber defends his Ph.D. dissertation at the GasDay Lab.|
This method is common in some fields, such as finance, but rarely has been used in the energy industry due to its complexity. Saber knew that if GasDay could show probabilistic results, it would present a more complete view of the risks that could lead to over- or under-supply, helping natural gas companies cut down on costs.
“It’s interesting – in everyday life, we think in probabilistic forecasting,” Saber said. “Maybe you’re thinking about how long it will take to get home from work. You don’t have any point forecasting in mind. If the traffic is heavy, you’ll leave earlier.”
But modeling probability isn’t so intuitive. When Saber chose this topic for his Ph.D. dissertation, he set out to develop not only new ways of generating probabilistic forecasts for the energy industry, but also new methods of evaluating and communicating those forecasts. He began implementing his research at the GasDay Laboratory in early 2017, and he successfully defended his dissertation in September. The next step? Incorporating it into the software we use.
|Example of a
probabilistic forecast predicting |
the amount of natural gas needed on an hourly basis.
Saber’s research soon may support our natural gas controllers as they make cost-effective choices and plan for the uncertainties ahead. It is just one example of the ways the GasDay Lab learns from and gives back to its customers.
“We love working with our local utilities,” said Tom Quinn, GasDay business director.
We’re glad to have GasDay predictions on our side this winter – helping us provide you with reliable and affordable natural gas service.