【报告摘要】The power companies are obligated to serve the end users at all time, including the peak hours. To maintain a reasonable service cost, they have the incentives to improve the efficiency of the power delivery process by managing the electricity demand profiles. This presentation focuses on forecasting the peak electricity demand. Through the comparisons among several methods with varying complexities, we will better understand an important feature of peak timing. The discussion will highlight some differences between theoretically sound solutions and the ones used in practice, as well as some future research directions that worth exploring.

【报告人简介】Dr. Tao Hong is Duke Energy Distinguished Professor, NCAEC Faculty Fellow of Energy Analytics, Graduate Director, and Research Director of Industrial and Systems Engineering Department, University of North Carolina at Charlotte. He is the Director of Big Data Energy Analytics Laboratory (BigDEAL), Chair of International Symposium on Energy Analytics, and General Chair of Global Energy Forecasting Competition. Dr. Hong currently serves on the editorial boards of International Journal of Forecasting, Solar Energy, and Foresight. Dr. Hong is the Founding Chair of IEEE Working Group on Energy Forecasting, and IIF Section on Water, Energy and Environment (SWEET). Dr. Hong received his B.Eng. in Automation from Tsinghua University in Beijing, and his PhD with co-majors in Operations Research and Electrical Engineering from North Carolina State University.