SAS® Energy Forecasting
Improve load forecasting performance, reduce uncertainties and generate value
SAS Energy Forecasting provides trustworthy, repeatable and defensible load forecasts for planning horizons that range from very short-term to very long-term.
Built on SAS' experience in working with hundreds of utilities worldwide, the solution offers a broad degree of automation, scalability, statistical sophistication and transparency. It enables utilities to operate more efficiently and effectively at all levels of decision making.
Get forecasts that reflect business realities, to plan future events with confidence.
Through repeatable, scalable, traceable and defensible results, SAS Energy Forecasting improves forecasting performance across all locations, at any level of aggregation. Forecasts are transparent and documented for sharing with internal partners and third-party stakeholders.
Use all your data, old and new, to maximize investments in smart meters and advanced metering infrastructure.
SAS' expertise in data management and governance delivers the highest possible value out of all your data and enables you to fully harness data from large-scale smart meter implementation projects. SAS solutions also integrate with leading systems including data warehouses, ERP systems, GIS, CIS and more.
Do more – better – with existing planning and forecasting resources.
Designed for a broad range of users, the solution offers automatic, configurable and manual modes so you can produce forecasts and modify models interactively. Automated forecasting entails less manual input and makes large forecasting processes more manageable.
Handle increasingly large volumes of data efficiently.
With SAS High-Performance Computing options, you can make discoveries, solve complex problems and deploy accurate results and information across the enterprise faster than with traditional technologies.
Make better energy trading and contract purchase decisions.
With statistical and visual indication of the likely range of forecast outcomes, you can incorporate quantifiable variability and confidence limits in the forecast when making operational and financial decisions.