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Inference From Smart Meter Data Using the Fourier Transform

Thursday, August 24 l 3 – 4 p.m. ET

SAS Utilites Users Group

This complimentary webinar is hosted by the SAS® Utilities Users Group (SUUG).

Want smarter customer data? Start here.

Want more from your data? This presentation demonstrates that applying fast Fourier transformation (FFT) on smart meter data can provide enhanced customer segmentation and discovery.

Fast Fourier transformation (FFT) is a mathematical method for transforming a function of time into a function of frequency. It's vastly used in analyzing sound but is also relevant for utilities. Advanced metering infrastructure (AMI) refers to the full measurement and collection system that includes meters at the customer site and communication networks between the customer and the utility.

With the inception of AMI, utilities experienced an explosion of data that provides vast analytical opportunities to improve reliability, customer satisfaction and safety. However, the data explosion comes with its own challenges. The first challenge is the volume.

Consider that just 20,000 customers with AMI data can reach over 300GB of data per year. Simply aggregating the data from minutes to hours or even days can skew results and not provide accurate segmentations. The second challenge is the bad data that is being collected. Outliers caused by missing or incorrect reads, outages or other factors must be addressed. FFT can eliminate this noise.

The proposed framework is expected to identify various customer segments that could be used for demand response programs. The framework also has the potential to investigate diversion, fraud or failing meters (revenue protection), which are big problems for many utilities.


Tom Anderson is a Principal Systems Engineer with the SAS US Energy Division. He has more than 20 years of analytical experience, including 16 years with SAS concentrating on advanced analytics and data management in both utilities and oil and gas. As a leader for developing technology solutions to smart grid initiatives, Anderson addresses the challenges that utilities are having around data management. The challenges result from new infrastructures that provide more data points than ever before. He evaluates existing infrastructures and best practices to provide a road map for the future of the true smart grid.

Prasenjit Shil, PhD, is a Senior Forecasting and Load Research Specialist at Ameren, a St. Louis-based utility company. Apart from leading Ameren Missouri’s short- and long-term forecast work, Shil is also responsible for sales variance analysis. His work involves load research and analytics for Ameren Missouri. Shil led numerous projects on customer segmentation for energy efficiency, call center analytics and process improvements analytics. He was also part of the analytics strategy team and data governance team at Ameren.

About the SAS® Utilities Users Group (SUUG)
This SAS users group provides a forum that enables the SAS utilities users community to collaborate on developing business and technology leadership through systems, processes and support. The group is a community that shares experiences, fosters growth and provides guidance with ongoing research and development for mutual success with SAS utilities business solutions. To learn more or join the group, visit the SUUG website.

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