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ACPEE 2025已被列入IEEE会议列表!
荣誉主席
Saifur Rahman 教授
2023年IEEE主席兼首席执行官
弗吉尼亚理工大学高级研究院，美国
Saifur Rahman教授是美国弗吉尼亚理工大学高级研究所的创始主任，他是Joseph R.Loring的电气和计算机工程教授。Saifur Rahman教授还担任能源与全球环境中心主任。他是IEEE终身研究员和IEEE千年奖章获得者。他还是2023年IEEE主席兼首席执行官。Saifur Rahman教授曾在2018年和2019年担任IEEE电力与能源协会(PES)主席。他是《IEEE电气化杂志》和《IEEE可持续能源汇刊》的创始主编。
报告标题：A Technology Roadmap to Achieve Climate Resilience
摘要：This lecture explains what carbonization is, addresses its causes impacts. It then offers technological solutions to reduce CO2 emissions from the electric power sector which is responsible over 30% of global Carbon emissions. In order to address the reduction of carbon emissions from the electric power sector, a collaborative approach between the industrialized nation states emerging economies is necessary. This will involve a portfolio of solutions with low-carbon generation from wind, solar, hydro nuclear, storage, cross-border power transfer advanced technology focusing on energy efficiency. This talk also discusses the IEEE Climate Change program related activities.
周武元 教授
IEEE Fellow
上海交通大学，中国
周武元教授是北卡罗来纳州立大学高级诊断、自动化和控制实验室的创始人兼主任。周博士是 IEEE 院士，2014-2018 年《IEEE 工业信息学学报》联合主编，曾任 2010-2012 年《IEEE 工业电子学报》主编、《IEEE 工业电子学报》联合主编、前《IEEE 机电一体化学报》技术编辑、前《IEEE 工业电子学报》和《IEEE 工业信息学学报》副主编。他曾于 2006-2007 年担任 IEEE 工业电子学会出版副主席，并于 2000-2001 年担任 IEEE 工业电子学会会员副主席。他曾担任 IEEE-IECON05 的主席、IEEE-IECON10、IEEE-ISIE12、IECON18、ISIE19 的联合主席。他最近的研究重点是协作分布式控制和故障管理，应用于智能电网、插电式混合动力汽车、电池和机电一体化/机器人系统。
报告标题：TBA
 澳大利亚人工智能研究所 (AAII) 主任。她曾两次获得 IEEE 模糊系统杰出论文奖(2019 年和 2022 年)、NeurIPS 杰出论文奖(2022 年)、澳大利亚人工智能杰出研究贡献奖(2022 年)、澳大利亚新南威尔士州州长工程或信息与通信技术杰出奖(2023 年)和 2023 年澳大利亚国庆日澳大利亚勋章 (AO)。</p><p>报告标题：Autonomous Machine Learning for Decision Support in Complex Environments</p><p>摘要：The talk will present how machine learning can innovatively effectively learn from data to support data-driven decision-making in uncertain dynamic situations. A set of new autonomous transfer learning theories, methodologies algorithms will be presented that can transfer knowledge learnt in more source domains to a target domain by building latent space, mapping functions self-training to overcome tremendous uncertainties in data, learning processes decision outputs. Another set of autonomous concept drift theories, methodologies algorithms will be discussed about how to hle ever-changing dynamic data stream environments with unpredictable stream pattern drifts by effectively accurately detecting concept drift in an explanatory way, indicating when, where how concept drift occurs reacting accordingly. These new developments enable advanced machine learning therefore enhance data-driven prediction decision support systems in uncertain dynamic real-world environments.</p><p><img src=)
Hazlie Bin Mokhlis 教授
马来西亚大学，马来西亚
目前，Hazlie Mokhlishe是马来亚大学电气工程系的教授。因其杰出研究，在2020年至2024年期间他被斯坦福大学评为前2%的科学家。2021年，他被马来西亚学术科学授予马来西亚顶级研究科学家称号，2024年被任命为马来西亚学术科学研究员。Hazlie教授是英国特许工程师、马来西亚工程师委员会专业工程师、IET研究员和IEEE高级成员。他曾担任2020-2022年IEEE PES马来西亚会议的分会主席。他的研究兴趣是提高配电系统的性能和应对极端天气的弹性。
报告标题：Building Resilient Power Distribution System: Challenges & Research Opportunities
摘要：In recent years, extreme weather phenomena like windstorms, floods, earthquakes have emerged as significant global threats. These events not only pose risks to human life but also wreak havoc on critical infrastructures such as electricity, water, gas supply, healthcare, communication, transportation systems. Among these vital infrastructures, the electricity supply system, commonly referred to as the power system, sts out as the crucial. Its failure can cascade into disruptions across all other sectors, potentially leading to dire consequences. Recognizing the pivotal role of the electricity supply system, researchers have mobilized remarkable efforts toward enhancing its resilience. The overarching goal is to fortify the system against abrupt changes expedite recovery following catastrophic events, thus ensuring maximum continuity of electricity supply. Within the realm of power systems, the distribution network holds particular promise for resilience enhancement. This discussion will delve into the concept of power system resilience, exploring research avenues to bolster the resilience of distribution networks. An overview of prevailing climatic conditions that precipitate catastrophic events will be provided, elucidating the pressing need for a resilient power system. Furthermore, potential research areas examples of research to improve the resiliency of distribution system will be presented.
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