IoT Analytics for Energy

Business Challenges

While the energy sector has been evolving in terms of generation and distribution, the IoT has the potential to be the most transformational if challenges related to reliability, integration, system complexity and security can be overcome. While reliable connectivity is an ongoing problem, many companies are struggling to integrate IoT technology with existing platforms, which tend to be overly complex, and may need to rethink their approach to data security in order to deploy IoT projects safely and securely.

How SAS Can Help

SAS enables organizations in the energy sector, including oil and gas companies and utilities, to capture and analyze increasing volumes and varieties of data streams flowing from numerous systems and connected devices, as well as shift analytics from traditional data centers toward devices at the edge. IoT solutions from SAS integrate streaming data with analytics and visualization so you can:

• Get the most value from your smart grid investments. Stop intentionally dropping valuable data because of bandwidth constraints. With SAS, you can use more new data sources without clogging operational systems by filtering and analyzing IoT data in motion, whether it’s from a data center, edge device or cloud.
• Optimize electric vehicle (EV) and distributed energy resource (DER) integration. Forecast specific needs of EVs and availability of DERs to meet demand, ensure grid stability and control costs.
• Extend your analytics infrastructure. Take algorithms to the data, reduce data movement and automate processes across your IoT infrastructure to reap incremental and long-term business gains.
• Develop new business opportunities. The SAS Platform enables innovation in both customer and grid applications, so you can get creative as you unlock new potential in distributed energy resources, advanced energy forecasting and smart city applications.

Why SAS?

SAS has the experience and expertise to deliver cutting edge IoT solutions for energy in the way that works best for your business:

• Advanced predictive modeling. Make better predictions of energy demand with more accurate forecasting models based on more data from more sources, including smart meters and weather stations. Automatically track model accuracy, and easily update models to reflect changes.
• Smart meter analytics. Optimize smart meter deployment and manage timely customer communications to get the most value from your investments in smart meters and advanced metering infrastructure.
• Comprehensive asset data. Integrate structured and unstructured data from all sources to get an enterprise view of asset performance and drive improved grid reliability.
• Advanced early-warning analytics. Identify potential issues early, even before they occur, so you can proactively take corrective action to improve outcomes.
Automated monitoring and predictive alerts. Reduce downtimes, avoid major defects and address potential performance issues before they escalate, and use built-in workflows and case management capabilities for faster problem resolution.