Energy Forecasting

Smart grid. Smart meters. Smarter forecasts.

How SAS Delivers Energy Forecasting Solutions

SAS® enables trustworthy, repeatable and defensible load forecasts for planning horizons ranging from very short-term to very long-term – deployable in the cloud or on-site.

High-performance load forecasting

  • Generate forecasts for all time horizons based on trusted data and advanced forecasting algorithms.
  • Maximize value from existing planning resources and improve forecast performance, operating with greater efficiency, unconstrained by data volume or forecasting objectives.
  • Create hierarchical forecasting for big data, including smart meter data.

What-if analysis & scenario planning

  • Compare alternate scenarios by running what-if analyses using prepackaged economic forecasts.
  • Simulate and test forecast rigor as a way to select long-term models that meet criteria for planning and operations.

Flexible, scalable advanced analytics

  • Take advantage of an extensible model repository.
  • Includes data mining, linear and logistic regression, decision trees, and neural networks.

Integrated data management

  • Ingest massive data sets quickly to perform automated forecasts.
  • Ensure that your data is trustworthy and governed with transparent and auditable lineage.

Single, visual administrative & reporting interface

  • Provide business managers with a visual interface for viewing forecasting results powered by – yet separate from – the forecast workbench.
  • Use drag-and-drop and autocharting capabilities that require no coding, and share reporting results via the web and mobile devices.

Why do utilities choose SAS® for energy forecasting?  

Harness new data streams to improve, minute-by-minute, how you respond to changing use conditions. Use advanced analytics to understand past trends, forecast future ones and see how your business functions. Then share your insights across all levels of the organization.      

Plan with confidence

  • Improve forecasting performance across all locations, at any level of aggregation with repeatable, scalable, traceable and defensible results.
  • Produce transparent, documented forecasts for sharing with internal partners and third-party stakeholders.

Make better 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 – whether energy trading or contract purchasing.
  • Use combined economic and weather range scenarios to evaluate more scenarios faster and with fewer resources.
  • With various scenarios in hand, create multiple medium- and long-term models based on anticipated outcomes and adjust the models with ease.
  • Make decisions that mitigate risks, surface new business opportunities and – ultimately – create competitive advantage with automatic reforecasting based on data updates.

Leverage all your data

  • Make better predictions about energy demand by building accurate predictive models based on more data from more sources, including smart meters and other IoT-connected devices.
  • Automatically track model accuracy and easily update models to reflect changes.
  • High-performance computing options efficiently handle increasingly large data volumes.
  • Make discoveries, solve complex problems and deploy accurate results and information across the enterprise faster than with traditional technologies.

Do more – better – with existing resources

  • Produce forecasts and modify models interactively with a solution that offers automatic, configurable and manual modes for a broad range of users.
  • Make large forecasting processes more manageable with automated forecasting that requires much less manual input.
  • Use existing planning resources and enable everyone to work more efficiently by using a single, comprehensive solution to forecast for all time horizons.
  • Eliminate the need to train forecasters on multiple software tools by using a common forecasting methodology and data integration processes across forecasting horizons.

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