Rate My Professor Tao Hong

Dr. Tao Hong is the Duke Energy Distinguished Professor, NCEMC Faculty Fellow of Energy Analytics, Graduate Director, and Research Director in the Department of Industrial and Systems Engineering at the University of North Carolina at Charlotte. He earned his B.Eng. in Automation from Tsinghua University in 2005, M.S. in Electrical Engineering in 2008, M.S. in Operations Research and Industrial Engineering in 2008, and Ph.D. with co-majors in Electrical Engineering and Operations Research in 2010 from North Carolina State University. As Director of the Big Data Energy Analytics Laboratory (BigDEAL), his research specializations include energy forecasting, power systems operations and planning, renewable integration, risk management, energy trading, retail forecasting, revenue optimization, and forecasting in healthcare, transportation, and sports.

Dr. Hong has demonstrated leadership and impact in the field through various roles, including Founding Chair of the IEEE Working Group on Energy Forecasting and the International Institute of Forecasters Section on Water, Energy, and Environment (SWEET). He serves as Chair of the International Symposium on Energy Analytics, General Chair of the Global Energy Forecasting Competition, and Director at Large of the International Institute of Forecasters. Dr. Hong is on the editorial boards of International Journal of Forecasting, Solar Energy, and Foresight. Key publications include “Probabilistic electric load forecasting: A tutorial review” (International Journal of Forecasting, 2016), “Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond” (International Journal of Forecasting, 2016), “Global Energy Forecasting Competition 2017: Hierarchical Probabilistic Load Forecasting” (International Journal of Forecasting, 2019), “Review of smart meter data analytics: applications, methodologies, and challenges” (IEEE Transactions on Smart Grid, 2019), “Weather station selection for electric load forecasting” (International Journal of Forecasting, 2015), “Global energy forecasting competition 2012” (International Journal of Forecasting, 2014), and “Long term probabilistic load forecasting and normalization with hourly information” (IEEE Transactions on Smart Grid, 2014). Awards include Charlotte Business Journal Energy Education Leader of the Year (2017), IEEE PES PSPI Technical Committee Prize Paper Award (2016), IEEE PES PSPI Technical Committee Working Group Recognition Award (2015), and IEEE PES Technical Council Distinguished Service Award (2014).