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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: Assoc. Prof. Yuhua Zhang, Jiangxi University of Water Resources and Electric Power, China

Yuhua Zhang is an associate professor at Jiangxi University of Water Resources and Electric Power, with research concentration on ecological environment and energy economic statistics. He received his Ph.D. from Xiamen University in 2016 specializing in computational mathematics and completed the postdoctoral research at the Applied Economics Postdoctoral Station of Shanghai University of Finance and Economics in 2021. Since July 2015, Dr. Zhang has been engaged in teaching and research work at Jiangxi University of Water Resources and Electric Power. Dr. Zhang co authored over 20 papers and authored 2 books.

Co-chair: Dr. Heping Wang, Jiangxi University of Water Resources and Electric Power, China

Wang Heping is a lecturer at Jiangxi University of Water Resources and Electric Power. His main research interests lie in prediction theory and energy economic statistics. He received his Ph.D. in science from Northwestern Polytechnical University in 2018. Since July 2018, he has been engaged in teaching and research at Jiangxi University of Water Resources and Electric Power. He has published over 20 papers and has led one national project and two provincial and ministerial projects.

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