Smart meters have drastically increased the temporal resolution of electrical load measurements available to both customers and utilities. Past research has shown promising results towards utilizing this information to break down measurements into the constituent loads within each customer’s facility without requiring the use of additional metering hardware. In this project we focus on a specific class of unsupervised algorithms based on deep learning techniques that can learn instantaneous power waveforms for individual devices as well as their activation patterns given sufficient data from a single meter.
- Estimating Climate Change Impacts on Water and Electric Power Infrastructure in the Southeast U.S.
- GridBallast: Autonomous Load Control For Grid Resilience
- Understanding Electricity Demand Patterns Coupled With On-Site Solar Generation
- Autonomous Solutions for Self-Regulating Sustainable Habitats
- HVAC control logic verification