- Customer Stories
- Northern Virginia Electric Cooperative (NOVEC)
Electric co-op relies on analytics to forecast demand, transmission needs
SAS helps NOVEC provide stable, competitively priced power for its consumers.
21.7% improvement
in forecasting over the competing model
Company achieved this using • SAS® Analytics Pro • SAS® Enterprise Guide® • SAS/ETS®
The Northern Virginia Electric Cooperative (NOVEC) provides power to 169,000 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 NOVEC
Challenges for NOVEC
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.
It must prudently provision for new or upgraded facilities such as substations and lines as its consumer base and consumer load grow.
NOVEC 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," says Ananya Kassahun, NOVEC Business Analyst. She had not used SAS before joining NOVEC.
NOVEC – Facts & Figures
169,000
residents and businesses receive electricity via NOVEC
7,200+
miles of electric lines
99.99%
average system reliability
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.
본 문서에 나오는 결과는 본 문서에 설명된 특정 상황, 비즈니스 모델, 데이터 입력 및 컴퓨팅 환경에 적합하게 되어 있습니다. 각 SAS 고객의 경험은 고유한 것으로, 비즈니스 및 기술적 변수에 따라 달라집니다. 따라서 모든 서술은 비전형적인 것이라는 점을 고려해야 합니다. 실제 절약, 결과 및 성능 특성은 개별 고객의 구성 및 조건에 따라 달라질 수 있습니다. SAS는 모든 고객이 비슷한 결과를 달성할 수 있다고 보증하거나 진술하지 않습니다. SAS 제품과 서비스에 대한 유일한 보증은 해당 제품 및 서비스에 대한 서면 계약의 보증서에 명시되어 있습니다. 본 문서의 어떠한 내용도 추가 보증을 구성하는 것으로 해석될 수 없습니다. 고객은 SAS 소프트웨어의 성공적인 구현에 따라 합의된 계약적 교환 또는 프로젝트 성공 요약의 일환으로 성공 사례를 SAS와 공유했습니다. 브랜드 및 제품 명칭은 각 기업의 상표입니다.
