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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 Jiangxi University of Water Resources and Electric Power, 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 Jiangxi University of Water Resources and Electric Power. 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: Prof. Yanying Li, Baoji University of Arts and Sciences, China

Yanying Li is a professor at Baoji University of Arts and Sciences, with research concentration on prediction theory and its application in energy and environment. She received her Ph.D. from Xidian University in 2015 specializing in applied mathematics. Since July 2007, Dr. Li has been engaged in teaching and research work at Baoji University of Arts and Sciences. During this period, she conducted extensive research on power load forecasting and dual carbon target forecasting, and was awarded the second prize for outstanding achievements in scientific research of higher education institutions in Shaanxi Province. Dr. Li co authored over 50 papers and authored 2 books. One of the papers was selected as a Highly Cited Papers and Hot Paper in Essential Science Indicators.

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 received 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. Liqin Sun, Henan University of Animal Husbandry and Economy, China

Sun Liqin, Lecturer at Henan University of Animal Husbandry and Economy, focuses her research on statistical forecasting theory and its applications in the fields of energy and environment. She obtained her PhD in Probability Theory and Mathematical Statistics from Xidian University in 2023, and her Master's degree in Mathematics from Chang'an University in 2016. Since February 2024, Dr. Sun has been engaged in teaching and research work at Henan University of Animal Husbandry and Economy. During this period, she has been committed to the theoretical research and applied practice in fields of big data processing, statistical forecasting and intelligent optimization. She has participated in a number of provincial and ministerial-level scientific research projects and published several academic papers in domestic and foreign journals.

Call for Papers Timeline / 征稿时间

  • Submission of Full Paper: February 1st, 2026
    投稿截止日: 2026年2月1日 

  • Notification Deadline: March 1st, 2026
    通知书发送: 2026年3月1日 

  • Registration Deadline: March 20th, 2026
    注册截止日期: 2026年3月20日