报告内容摘要：

Forecasts have been heavily used for operations and planning in virtually every major industry. Many traditional forecasting methodologies have been designed and implemented for a single dimension – taking single input to generate single-valued forecasts for a single subject. The recent progress made by the scientific forecasting community has gone beyond this single dimension. Many analytical tools are available for the forecasters to take advantage of high dimensional information. This talk will use the electric load forecasting problem to elaborate the findings, opportunities and challenges for demand forecasting in the high dimensional world.

 
报告人简介：

Tao Hong is Associate Professor of Systems Engineering and Engineering Management and NCEMC Faculty Fellow at the University of North Carolina at Charlotte. His primary research interest is on forecasting and its applications in various industries, such as energy, healthcare, supply chain, sports and gambling. He is the Founding Chair of IEEE Working Group on Energy Forecasting, Founding General Chair of Global Energy Forecasting Competition, Founding Chair of International Symposium on Energy Analytics, and a board member of the International Institute of Forecasters. He is an editorial board member of the IEEE Transactions on Smart Grid, IEEE Power and Energy Magazine and International Journal of Forecasting.
 
 
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