Prof. Zhixin Wang

Shanghai Jiao Tong University, China

Zhixin Wang, Professor, Shanghai Jiao Tong University. He obtained bachelor 's, master 's and doctor 's degrees in Zhejiang University. He used to be the Deputy Director of Department of Electrical Engineering, Shanghai Jiao Tong University, the Director of Hydraulic Laboratory, the Deputy Director of Institute of Mechanical and Electrical Control, the Assistant to the President of School of Mechanical Engineering, and the Deputy Director of Science and Technology Department of Shanghai Jiao Tong University.   Editors and reviewers of ' Control Engineering ', ' Mechatronics ', ' Automation Instrumentation ', ' Chinese Journal of Electrical Engineering ', ' Power Grid Technology ', ' Journal of Power System and Automation ', ' Power Grid and Clean Energy ', ' Shanghai Water Conservancy and Hydropower Technology ', ' Shanghai New Energy ', etc. He has won many awards such as the certificate of young scientific and technological experts in China 's machinery industry, the certificate of cross-century talents of the former Ministry of Machinery Industry, the certificate of Shanghai New Long March Assault, the special prize for young teachers of the former Ministry of Machinery, the second prize for scientific and technological progress of the whole army, and the silver prize of China International Industrial Expo.

Speech Title: Electricity Computing Collaboration: Power Configuration, Load Modeling and Prediction, Power Dispatching for Data Center

Abstract: With the widespread deployment of artificial intelligence (AI) applications and the rapid expansion of digital infrastructure, data centers have experienced continuous growth in scale and computing demand, making energy consumption management a critical issue in the energy and power engineering domain. Distinct from conventional electricity consumers, data center loads exhibit unique operational characteristics, with total energy consumption dominated by information technology (IT) equipment and cooling systems. The strong non-linearity, multi-source coupling, and dynamic evolution of these components pose significant challenges tomakeaccurate energy consumption forecasting and power system dispatch. The paper systematically reviews the research progress in data center power supply configurations, load modelling, and energy consumption forecasting. Particular emphasis isfocused on a comparative analysis of statistical models, machine learning, and deep learning, which are applied to IT load and cooling energy prediction, applicable conditions, feature construction strategies, and modeling performance, etc. Based on extensive literature analysis, key feature selection principles and model adaptation patterns under multi-source monitoring data are summarized, highlight the role of workload characteristics, thermal environmental variables, and operational control parameterstoimproving prediction.Furthermore, data center participation mechanisms in power system operation, dispatch based on load and energy consumption forecasting are reviewed also. From both temporal and spatial perspectives, the influence of forecasting on demand response implementation, flexible regulation capability extraction, and coordinated operation between power system and computing resource is analyzed.