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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Title:
<o:p></o:p></span></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Data-Driven Cyber-Physical Energy Systems</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Abstract: </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Cyber-Physical Systems (CPS) play an increasingly important role in the development of various fields, including smart homes, smart buildings, infrastructure, smart
cities, and smart medicine. The smart grid is a crucial part of CPS. As Distributed Solar Energy Resources (DSER) are increasingly deployed and integrated, new challenges emerge. For instance, Net meter data alone cannot profile DESR completely. The energy
sharing among DSERs is suffering low efficiency and low fairness.DSER performance models are not very accurate and cannot detect damage or degradation conditions accurately. </span><o:p></o:p></p>
<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">In my presentation, I will introduce two open-source systems: SolarDetector and SolarTrader, developed to address these challenges. SolarDetector is designed to detect
and profile distributed solar photovoltaic arrays without incurring extra costs. Additionally, SolarTrader is an innovative, fairness-aware system specifically designed for peer-to-peer energy trading within virtual power plants (VPPs). Our research has shown
that these systems can significantly enhance the management of distributed solar arrays, benefiting utilities, government agencies, and third parties. Furthermore, they enable unsupervised, distributed, and equitable solar energy trading in residential VPPs.
Eventually, I will also share my research vision and future research plans.</span><o:p></o:p></p>
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Bio:
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<p class="MsoNormal"><span style="font-size:12.0pt;font-family:"Arial",sans-serif;color:black">Qi Li is a final year Ph.D. candidate at the Colorado School of Mines CS department, where she is advised by Dr. Dong Chen. Prior to joining Mines, Qi earned her
master’s degree from Northeast University, China. Her research interest is building data-driven computer systems, which combine Data Science and Deep Learning/Machine Learning techniques with a deep understanding of the domain-specific information from CPS
systems. She has presented papers in top-tier conferences, including IoTDI'23, CNS’22, IPSN’21, IPSN’20, BuildSys’20, and IGSC’20. Also, she received recognition for the Best Paper Award at BuildSys'20. In 2023, she was selected for CPS Rising Star. She was
selected to participate in the 2024 CRA-WP Grad Cohort for Women.</span><o:p></o:p></p>
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