上海交通大学-beats·365(中国)-官方网站国际暑期学校课程电力能源系统可持续发展的数据科学方法与应用

SJTU Global Summer School 2026Data Science and Applications for Power, Energy and Sustainability

气候变化与可持续发展，是当前全球共同面临的时代命题。在全球低碳转型加速推进的背景下，电力能源行业正迎来深刻的数字化变革，数据科学、人工智能与能源系统的深度融合，已成为行业发展的核心趋势。

Climate change and sustainable development have emerged as central priorities on the global agenda. In the context of an accelerating low-carbon transition, the power and energy sector is undergoing a profound digital transformation, with the deep integration of data science, artificial intelligence, and energy systems becoming a key driver of innovation and long-term industry evolution.

2026 年暑期上海交通大学重磅推出「电力能源系统可持续发展的数据科学方法与应用」全球暑期学校课程，现已正式开启全球招生，面向全球多学科背景的学子，搭建数据分析理论与电力能源工程应用的桥梁，带你解锁低碳未来的核心技术密码！

In the summer of 2026, Shanghai Jiao Tong University will launch its Global Summer School course, “Data Science and Applications for Power, Energy, and Sustainability.” and applications are now being accepted. Open to students worldwide from diverse academic backgrounds, the program is designed to bridge data analytics theory with real-world energy engineering practice, equipping participants with the methodological foundations and applied skills essential for advancing the low-carbon transition.

Overview

课程简介

01 课程时间 Duration

2026年7月13日-7月26日（2周）

2026.7.13 - 2026.7.26 (2 weeks)

02 课程地点 Campus

上海交通大学闵行校区

Minhang

03 课程收费 Tuition

Fee5000元人民币（约合710美元）

CNY 5000(USD 710)

本课程系统讲解数据科学、机器学习与人工智能技术在电力、能源与可持续发展领域的应用实践。课程面向对电力能源系统抱有研究兴趣、来自多学科背景的学生开设，核心目标是打通数据分析方法与真实工程应用场景的壁垒，实现二者的深度融合。

This course provides an introduction to the applications of data science, machine learning, and artificial intelligence in the fields of electric power, energy, and sustainability. Designed for students from diverse disciplinary backgrounds with an interest in power and energy systems, it builds a solid connection between data analytics methodologies and real-world engineering applications.

学生将系统掌握数据科学的基本工作流程，包括探索性数据分析、验证性数据分析与预测性数据分析三大模块。课程教学内容覆盖监督学习、无监督学习、深度学习等核心方法论，同时引入由大语言模型赋能的编码技术，助力学生快速完成数据科学项目的测试验证与原型开发。Students will master the fundamental workflow of data science, including exploratory, confirmatory, and predictive data analysis (EDA, CDA, and PDA). The curriculum covers core methodologies including supervised and unsupervised learning, deep learning, as well as "vibe coding" — an approach leveraged by large language models (LLMs) to support rapid testing and prototyping of data science projects.

学生将深入探究数据科学在相关领域的各类典型应用场景，包括气候变化影响量化、无人机电力线路巡检、基于卫星影像的光伏组件识别、电力负荷与可再生能源发电预测、能源系统异常检测等。此外，课程还特邀来自全球各地的一线科研人员与行业资深从业者担任特邀讲师，围绕领域前沿主题开展专题分享，将前沿内容纳入课程教学体系，全方位丰富学生的学习体验。

Students will also explore a wide range of representative applications of data science, including climate change impact quantification, drone-based power line inspection, solar panel detection via satellite imagery, load and renewable generation forecasting, and anomaly detection in energy systems. To further enrich the learning experience, the course invites active researchers and industry practitioners from across the globe as guest speakers, who will deliver lectures on cutting-edge topics as an integral part of the curriculum.

Highlights

课程特色

真实项目驱动：基于真实数据与工程场景开展小组项目，将想法从数据转化为可部署的解决方案，强化解决实际问题的能力。

Real-world Project Driven: Work on team projects built on real datasets and engineering scenarios, taking ideas from data to deployable solutions while strengthening real problem-solving skills.

深度交叉学科：融合数据科学、机器学习与电力能源系统知识，以典型应用场景为纽带，搭建多学科协同的学习与研究框架。

Deep Interdisciplinary Integration: Integrate data science, machine learning, and power & energy systems through representative use cases, building a coherent cross-disciplinary learning and research framework.

上海实地参访：依托上海在智能基础设施建设及其数据集成应用的领先优势，本课程将组织学员赴上海及周边相关企业及其数据驱动的可持续发展示范项目参访，亲身感受理论与产业实践的有机融合。

Shanghai Field Visits: Leveraging Shanghai’s leading advantage in smart infrastructure development and its data applications, this course will organize participants to visit relevant enterprises in and around Shanghai, as well as their data-driven sustainable development demonstration projects, enabling participants to experience the deep integration of theoretical knowledge and industrial practice.

上海充换电公共数据采集与监测市级平台

Shanghai Municipal Public Platform for EV Charging/Swapping Data Collection and Monitoring

特来电光储充一体化超充站

Teld Integrated PV-Storage-Charging Supercharging Station

蔚来换电站

NIO Battery Swapping Station

国家能源智能电网（上海）研发中心

State Energy Smart Grid R&D Center (Shanghai)

Instructors

课程师资

课程负责人

Course Coordinator

冯冬涵教授

Prof. Donghan Feng

Email: seed@sjtu.edu.cn

冯冬涵，上海交通大学教授、博士生导师，国家能源智能电网研发中心副主任。主持科研项目十六项，其中国家自然科学基金项目4项，国家科技重大专项课题1项；参与科研项目二十余项，包括国家科技支撑计划、国家重点研发计划、国家高技术研究发展计划(863计划)项目各一项。研究成果在国内外主流电力能源领域期刊发表学术论文100余篇，总他引超过2000次。出版学术专著两部，中英文各一部。冯冬涵教授是上海市优秀学术带头人（23XD1422000），入选国家留学基金委“未来科学家”计划、丹麦奥斯特基金、上海市东方英才拔尖计划（BJJY2024010）和上海交通大学晨星青年学者奖励计划（教授、A类项目）。研究成果获评上海市科技进步一等奖、上海市技术发明一等奖和国网公司科技进步奖一等奖各一项。近5年作为课程负责人新建并开设本科生全英文专业课和研究生双一流校企合作课程各1门。研究方向包括电力市场、车网互动、新一代人工智能在电力系统中的应用等。

Prof. Dr. Feng has been with the faculty of Shanghai Jiao Tong University since 2008, where he currently is a full professor. Prof. Dr. Feng also serves as the Deputy Director of the State Energy Smart Grid Research and Development Center, Shanghai, China. He was a Hans Christened Ørsted Postdoc with Technical University of Denmark from 2009 to 2010, and a Visiting Research Scholar with the University of California, Berkeley, USA, from 2015 to 2016. Prof. Feng published three monographs and more than 100 academic papers in world renowned journals which are cited over 2000 times. Professor Feng Donghan is awarded an Outstanding Academic Leader in Shanghai in 2023, and selected as Shanghai Eastern Talent Outstanding Program for year 2024. His research interests include spot pricing for electricity markets, uncertainty modelling of renewable energy, coordinated charging and discharging of electric vehicles, application of AI enhanced competitive and cooperative game theories in power system analysis.

Course Registration

课程报名

在线申请系统https://apply.sjtu.edu.cn需上传以下材料：

• 护照信息页扫描件（用于签证申请，护照有效期须至少剩余 6 个月）

• 证件照（与护照照片相符）

• 个人简历

• 申请陈述

• 语言能力证明

Please apply via the following website: https://apply.sjtu.edu.cn. The following items must be uploaded to the online application:

• A scan copy of the ID page of the student's passport. The passport must be valid for at least 6 months for the visa application.

• An ID photo (Similar to a passport photo)

• Curriculum vitae (CV)

• Motivation letter•Language proficiency certificate (If applicable)

Application Guide

申请指引

1. Visit the online application portal at http://apply.sjtu.edu.cn.

2. Create an account and log in to the system.

3. Select New Application, then choose Short-term Programs (Short term, Winter & Summer Programs), followed by Summer Programs.

4. Follow the on-screen instructions to complete the required personal and academic information.

5. In Step 2: Study Plan, select the program as follows:

•Teaching Language: English

•Study Duration: 2026 Global Summer School (Session A), From 2026-7-13 to 2026-7-26

•First Preference-School: School of Electrical Engineering

•First Preference-Major: Summer School - Data Science and Applications for Power, Energy and Sustainability

6. Continue filling in the remaining sections of the application as instructed by the system.

7. Upload all required supporting documents, complete the payment, and review all information carefully before submission.

8. Submit the application once all sections have been completed.