Our paper, “Using Smart Meter Data to Forecast Grid Scale Electricity Demand”, was accepted for publication at the inaugural International Conference of Machine Learning (ICML) Climate Change workshop. Here is the 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).
View all presenters here.