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講座題目：

Artificial Intelligence for Load Forecasting: History, Illusions, and Opportunities

講座時間：

2021年12月10日 星期五

9:00 am - 10:30 am 北京時間

Zoom會議鏈接：

https://zoom.us/j/99547874448?pwd=U3NtbzF5aldJbzg2QkxUbmtjNVd0UT09

會議號: 995 4787 4448

密碼: 999756

演講人：

Tao Hong

Director of Big Data Energy Analytics Laboratory

Professor of Systems Engineering and Engineering Management

University of North Carolina at Charlotte

主持人：

宋潔 教授

北京大學(xué)工學(xué)院工業(yè)工程與管理系

長江特聘教授，副院長

Tao Hong

University of North Carolina at Charlotte

演講學(xué)者簡介

Peking University ES Seminars

Dr. Tao Hong is Professor, Graduate Director, and Research Director of Systems Engineering and Engineering Management Department, Director of BigDEAL (Big Data Energy AnalyticsLaboratory), and NCEMC Faculty Fellow of Energy Analytics. He is Director at Large of International Institute of Forecasters (IIF), General Chair of Global Energy Forecasting Competition (gefcom.org), the Founding and Past Chair of IEEEWorking Group on Energy Forecasting, and Founding and Past Chair of IIF Section on Water, Energy and Environment (SWEET). Dr. Hong serves as a Department Editor of IEEE Transactions on Engineering Management, Associate Editor of International Journal of Forecasting, and Associate Editor of Solar Energy. 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.

講座摘要

Peking University ES Seminars

Artificial Intelligence (AI) has found many applications in today’s world, such as computer vision for self-driving cars, speech recognition for personal assistants, and algorithm design for strategy gaming systems. While enjoying the convenience brought by AI to our daily life, people may be wondering when and how AI started and evolved. In fact, AI has gone through several waves since 1940s. One of the first commercial applications of AI was found right in the power industry, where artificial neural networks were practically used forshort-term load forecasting in 1990s. During the past three decades, the research community has published thousands of load forecasting papers that promote AI-based models. Nevertheless, most of them are still at the theoretical level,with few adopted in practice. This presentation will examine five illusions associated with developing AI-based load forecasting models, and present the clarifications to help improve the efficiency of AI research for load forecasting given the opportunities in the big data era.

歡迎各位同仁蒞臨！

Peking University ES Seminars

圖文編輯 | COE