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Session 8: Data Driven based Renewable Cluster Power Generation Prediction and Electricity-Carbon Collaborative Scheduling

“基于数据驱动的新能源集群发电预测及电-碳协同调度”

Session 8

Data Driven based Renewable Cluster Power Generation Prediction and Electricity-Carbon Collaborative Scheduling
“基于数据驱动的新能源集群发电预测及电-碳协同调度”

With the increasing penetration of renewable energy sources and the pressing need for carbon emission reduction, the integration and efficient management of renewable clusters have become crucial. Data-driven approaches offer promising solutions for enhancing the predictability of renewable cluster power generation and facilitating the collaborative scheduling of electricity-carbon emissions. This research theme aims to explore the potential of advanced data analytics in improving forecasting accuracy for renewable energy sources such as wind and solar, while also addressing the challenge of aligning electricity generation with carbon emission targets. The topics covered in this research encompass the development of data-driven forecasting models for new energy cluster power generation, the integration of carbon emission considerations into power system scheduling, and the implementation of collaborative scheduling strategies that optimize both electricity supply and carbon footprint. By leveraging big data and machine learning techniques, this research seeks to contribute to the sustainable development of energy systems, ensuring reliable power supply while minimizing environmental impact.

Chair: Prof. Jinxing Che, Nanchang Institute of Technology, China

Jinxing Che is a professor at Nanchang Institute of Technology, with research concentration on prediction theory and renewable energy management. He received his Ph.D. from Xidian University in 2019 specializing in probability theory and mathematical statistics and his Master's Degree from Lanzhou University in 2010 specializing in applied mathematics. Since July 2010, Dr. Che has been engaged in teaching and research work at Nanchang Institute of Technology. During this period, he worked as an algorithm researcher and senior data analyst at Nanjing Huasu Technology Co., Ltd. and New Oriental Group, providing artificial intelligence solutions for intelligent monitoring, diagnosis, and prediction of communication, energy and power equipment, and operation management. Dr. Che co authored over 60 papers and authored 2 books. He has been listed as one of the top 2% scientists in the world by Stanford University.

Co-chair: Dr. Pei Du, Jiangnan University, China

Du Pei is a lecturer at the School of Business, Jiangnan University, with research interests in machine learning, intelligent optimization algorithms, and prediction theory and methods. He received his Ph.D. degree in statistics from Dongbei University of Finance and Economics (DUFE) in 2021, and his M.S. degree in statistics from DUFE in 2018. He worked as an assistant professor at the School of Management, Xi'an Jiaotong University (XJTU) from July 2021 to December 2022. Since January 2023, he has been teaching and researching at the School of Business, Jiangnan University. Up to now, he has co-authored more than 50 academic papers.

Co-chair: Assoc. Prof. Haiquan Qiu, Anhui Science and Technology University, China

Haiquan Qiu is an associate professor at the School of Information and Network Engineering, Anhui Science and Technology University. His main research areas include machine learning, high-dimensional data analysis, and charging pile error analysis. He obtained his PhD in Probability Theory and Mathematical Statistics from Xidian University in 2022. Since July 2007, Dr. Qiu has been engaged in teaching and research work at Anhui Science and Technology University. He has led multiple research projects at or above the provincial level, published over 20 papers, and collaborated with companies such as Zhongzhi Woda (Beijing) Electric Power Technology Co., Ltd. to study the error analysis of DC and AC charging piles, achieving certain results.

Co-chair: Dr. Qinghua Zhang, Foshan University, China

Dr. Qinghua Zhang is a master's supervisor and deputy director of the Department of Mechanical and Electronic Engineering at Foshan University. He graduated from South China University of Technology in June 2013 with a PhD in Mechanical Manufacturing and Automation. His main research areas are robotics, machine vision, image processing, and other related fields. At present, more than 20 papers have been published and 5 invention patents have been applied for/authorized. He served as a reviewer for journals and academic conferences such as Sensors, Journal of Agricultural Machinery Engineering, ROBIO, etc. The related research achievements have won the second prize of Guangdong Provincial Science and Technology Progress Award and the first prize of Guangdong Mechanical Engineering Society.

Call for Papers Timeline / 征稿时间

  • Submission of Full Paper: March 10th, 2025
    投稿截止日: 2025年3月10日 

  • Notification Deadline: March 30th, 2025
    通知书发送: 2025年3月30日 

  • Registration Deadline: April 10th, 2025
    注册截止日期: 2025年4月10日