报告内容：
Load forecasting is a fundamental business problem established since the inception of the electric power industry. The business needs of load forecasting include power systems planning and operations, revenue projection, rate design, energy trading and so forth. Many different organizations other than utilities also need load forecasts, such as regulatory commissions, industrial and big commercial customers, banks, trading firms, and insurance companies. Over the past 100 plus years, both research efforts and industry practices in this area are primarily on point load forecasting. In the recent decade, due to the increased market competition, aging infrastructure and renewable integration requirements, probabilistic load forecasting is becoming more and more important to energy systems planning and operations. This presentation offers a tutorial review of probabilistic load forecasting, including notable techniques, methodologies, evaluation metrics, common misunderstandings and recommended research directions.

报告人简介：
Dr. Tao Hong is the Graduate Program Director of Systems Engineering and Engineering Management and Director of Energy Analytics Research Laboratory at University of North Carolina at Charlotte. He is the Founding Chair of IEEE Working Group on Energy Forecasting, General Chair of Global Energy Forecasting Competition, lead author of the online book Electric Load Forecasting: Fundamentals and Best Practices, and author of the blog Energy Forecasting. He is an editor of IEEE Transactions on Smart Grid and the past Guest Editor-in-Chief of its Special Section on Analytics for Energy Forecasting with Applications to Smart Grid. He is a guest editor of International Journal of Forecasting Special Issue on Probabilistic Energy Forecasting. 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.

联系人：康重庆 教授