Events Calendar

PhD Defense - Saima Aman

PhD Defense - Saima Aman

Title: Prediction Models for Dynamic Decision Making in Smart Grid

Committee: Viktor Prasanna (chair), Cauligi Raghavendra, Cyrus Shahabi

Abstract:
The widespread use of smart meters and sensors in the Smart Grid is generating large volumes of data, also designated as big data. Predictive modeling can be used to learn from this data about when peak demand periods occur to make dynamic decisions about when, by how much, and how to reduce consumption, by means of demand response (DR). While day-ahead predictions have long been used for DR, we propose dynamic demand response (D2R) that requires performing DR at a few hours- advance notice whenever necessitated by dynamic conditions such as intermittent generation from renewable energy sources. D2R is a prime example of dynamic decision making in smart grids that involves balancing supply and demand in real-time and adapting to dynamically changing conditions by automating and transforming the DR planning process.

We focus on the challenges of prediction modeling and evaluation to enable D2R. First, we address the partial data problem that arises when real-time data from sensors is only partially available at the utilities. Our proposed model learns the dependencies among time series collected from a set of sensors, and uses data from a small subset of -"influential" sensors to make accurate predictions for all sensors. The second problem we address is that of predicting reduced consumption during DR. We leverage big data on reduced consumption to learn a single ensemble model to predict reduced consumption for diverse customers over different time intervals, thus achieving high cost efficiency. Finally, we identify the limitations of existing measures for evaluating the performance of prediction models in smart grid and propose a suite of performance measures that address accuracy, reliability, and cost. We use the USC microgrid data in our experiments, and our proposed models are being used for D2R on the USC campus.

Biography:
Saima Aman is currently a Ph.D. candidate in the Computer Science Department at the University of Southern California. Her research interests are in Data Science and Artificial Intelligence. She has a M.S. in Computer Science from the University of Ottawa, Canada, and a B.Tech. in Computer Engineering from Aligarh Muslim University, India.

Location: Hughes Aircraft Electrical Engineering Center (EEB) - 248

Audiences: Everyone Is Invited

Contact: Kathy Kassar