Using Smart Meter Data to Forecast Grid Scale Electricity Demand (Deployed Track)
Abraham Stanway (Amperon Holdings, Inc); Ydo Wexler (Amperon)
Abstract
Highly accurate electricity demand forecasts represent a major opportunity to create grid stability in light of the concurrent deployment of distributed renewables and energy storage, as well as the increasing occurrence of extreme weather events caused by climate change. We present an overview of a deployed machine learning system that accomplishes this task by using smart meter data (AMI) within the region governed by the Electric Reliability Council of Texas (ERCOT).