The University of Texas at Dallas

Erik Jonsson School of Engineering and Computer Science

Content

Researchers Work to Improve Business Forecasting Practices

Researchers Work to Improve Business Forecasting Practices

A pair of researchers from the Department of Mechanical Engineering in the Erik Jonsson School of Engineering and Computer Science have received the 2017-2018 SAS-IIF Research Award from the International Institute of Forecasters (IIF).

IIF is a non-profit organization dedicated to developing and furthering the generation, distribution, and use of knowledge on forecasting. Every year, IIF provides two awards on forecasting in Business Applications and Methodology.

Dr. Jie Zhang
Assistant Professor Dr. Jie Zhang

Assistant Professor Dr. Jie Zhang and doctoral student Cong Feng are selected to receive the Business Applications award. The project “Hierarchy-Based Disaggregate Forecasting Using Deep Machine Learning in Power System Time Series,” aims to address the challenges of collecting decentralized information in power systems by developing a data-driven forecasting methodology based on deep machine learning.

“The power and energy system has been under significant transformation in recent years due to the growing prevalence of smart grid technology and the increasing decentralized components. Detailed information is required at the different levels to help the electricity users, utilities, and policy makers for better power system management,” Zhang said.

Cong Feng
PhD student Cong Feng

For PhD student Cong Feng, this is the second recognition for his research since last fall. In December 2017, Feng received a best student paper award at the IEEE/ACM International Conference on Big Data Computing, Applications and Technologies. That paper was titled “Characterizing Time Series Data Diversity for Wind Forecasting.”


Footer

Departments