Predictive Analytics for Utility Load and DER Forecasting 2016: Markets, Technologies and Strategies

by Aakriti Gupta

Short-term load forecasting is the central predictive analytics application used to match supply with the demand for energy. The proliferation of DERs, efforts to improve utility asset efficiency and reliability, and increasing pressure to improve customer service is necessitating the evolution of these applications to provide more granular forecasts for nodes deeper in the distribution grid or at the customer site.

For utilities, granular short-term load and DER forecasting can form a foundation for new data-driven services for customers and internal stakeholders. As the grid evolves into a smarter, more distributed and more interconnected network, these site- and asset-level forecasting platforms will be key to:

  • Enable DER market integration
  • Improve grid utilization
  • Enable coordination between utility and customer-owned assets to provide specific locational benefits
  • Increase data access and services to customers

As initial predictive analytics solutions mature, short-term load forecasting use cases and business cases are becoming more defined.  Leading utilities are using these tools to reduce the risks associated with the operation of the distribution grid, improve revenue assurance and increase customer satisfaction.

Forecasting Vendor Ecosystem

This slide-based report gives an in-depth view of load forecasting for the DER market, including market drivers, technologies, applications, competitive positioning and analysis, and product differentiation.

Vendors Covered Include:

ABB | Accenture | Autogrid | AWS TruePower | Bit Stew Systems | C3 | Clean Power Research | Grid4C | IBM | Innowatt | Integral Analytics | Itron | Leidos | Locus Energy | OATI | Oracle | Pattern Recognition Technologies | SAP | SAS | Siemens | Viasala


Please contact Tate Ishimuro (ishimuro@greentechmedia.com) for more information.

Aakriti Gupta Analyst, Grid Edge

Aakriti Gupta is a Grid Analyst at GTM Research, focusing on soft grid, IOT and data analytics. Prior to GTM Research, Aakriti worked at Bank of America as a Quantitative Management Associate (a data analytics position operating across different lines of business). She graduated from Carnegie Mellon University with an MS and BS in Electrical and Computer Engineering with a double major in Engineering and Public Policy, focusing on power systems. She has been a strategy intern at GE with the digital energy group and a business analyst for a boutique consulting firm. She has also worked as a research analyst at CMU for a number of projects in the power systems domain.

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