Electric co-op forecasts demand, transmission needs

The Northern Virginia Electric Cooperative (NOVEC) provides power to 144,000 customers. To keep electric costs down and to reliably serve customers, NOVEC needs to know how much power to buy, transmit and deliver for its consumers. SAS Analytics provide NOVEC with a broad array of econometric and time series forecasting techniques, along with point-and-click interfaces that can grow with the utility.

"SAS has the functionality to do what we need now and what we anticipate needing in the future.”

Jamie Hall
Senior Operations Research Analyst

Challenges for NOVEC

Doesn't produce its own power. The co-op needs to forecast power consumption accurately to make power purchasing decisions that will result in stable, competitively priced power for its consumers.

Must prudently provision for new or upgraded facilities such as substations and lines as its consumer base and consumer load grow.

Must optimally operate and maintain its electric infrastructure in order to provide superior service reliability at a competitive cost to its consumers.

Why SAS®?

Stability: "SAS has the functionality to do what we need now and what we anticipate needing in the future. It's the safe choice,'' says Jamie Hall, Senior Operations Research Analyst.

Ease of use: NOVEC analysts can point and click to build a model, making the product accessible to anyone, explains Ananya Kassahun, NOVEC Business Analyst. She had not used SAS before joining NOVEC.

SAS benefits

SAS automatically keeps track of the flow of the forecasting process and upwards of 50 time series used to build models.

Pulling in third-party and historical data from multiple sources is simple. The models use daily third-party weather forecasts and monthly economic information.

The model built in SAS provided a 21.7 percent improvement versus the competing model.

Future SAS uses

Determining the impact of load management programs.



NOVEC needs to forecast demand for power and the need to build or upgrade electric infrastructure.



The model built in SAS provided a 21.7% improvement over the competing model.

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.