A team from the University of Macau (UM) State Key Laboratory of Internet of Things for Smart City (SKL-IOTSC), also the students of the Faculty of Science and Technology (FST) today (28 January) won a second prize and the only Best Innovation Award at a contest of AI application in power dispatching in China, it was the only university team to win awards at this competition.
45 teams were qualified to compete in the preliminaries. They came from various well-known companies, universities, and organisations, including China Southern Power Grid Co, Guangdong Grid Co, China Energy Engineering Group Co, NARI Technology Development Co, Tsinghua University, Peking University, Zhejiang University, Shanghai Jiangtong University, Wuhan University, South China University of Technology, and Alibaba Group. The UM team advanced to the final with a third place in overall prediction accuracy, and was the only university team to advance to the final.
All members of the UM team are postgraduate students in the SKL-IOTSC. They include Yu Peipei (team leader), Zou Bin, Hui Hongxun, Feng Guangxu, and Yang Qifan. Because of requirements for epidemic prevention, Only Yu Peipei and Zou Bin went to Guangzhou to participate in the competition while the remaining three students provided support remotely. Their advisors are UM Rector Yonghua Song, who is also the director of the SKL-IOTSC and the Chair Professor of the Department of Electrical and Computer Engineering (ECE) of FST, Associate Professor of ECE Dai Ningyi and Assistant Professor of ECE Zhang Hongcai. Among judges for the final round were member of the Chinese Academy of Sciences, well-known professors in the field of power systems, and chief scientists at Huawei and Alibaba Group. In the end, the UM team won a second prize with the second best overall ranking. The team also won the only Best Innovation Award for its novel theoretical algorithms in the field of renewable energy prediction.
The event was co-organised by China Southern Power Grid’s dispatching and control center, hosted by the company’s two research institutes and media team as well as Alibaba Cloud, with assistance from various parties, including the Chinese Society for Electrical Engineering. The topic of the preliminaries is ‘Bus Load Forecasting of 220kV Substations in Zhanjiang Area of China Southern Power Grid’. Participating teams were required to predict the load curves of 17 substations and 33 generator transformers in one week. Load forecasting is one of the key technical issues in power system operation and management. It is also a hot research topic in the academic circle. This contest included a variety of forecasting scenarios, such as residential loads, commercial and industrial loads, high-speed railway loads, and distributed renewable energy generators, which added to the difficulty and complexity of this load forecasting competition.
Established in July 2018, the SKL-IOTSC is China’s first state key lab in the internet of things for smart city and was founded with the approval of the Ministry of Science and Technology. The lab aims to meet the needs of the country by developing into a world-class laboratory with local characteristics, with the support of experts in Macao. To solve the key technical issues in internet of things technology for smart city development, the lab develops common theories, algorithms, and systems that are of fundamental importance, and creates exemplary applications for smart city development. So far, the lab has established its research offices in five areas: intelligent sensing and network communication, urban big data and intelligent technology, smart energy, intelligent transportation, and public safety and disaster prevention. The lab is led by its director Prof Yonghua Song, who is a fellow of the Royal Academy of Engineering of the United Kingdom and a member of the Academia Europaea. The lab’s team consists of three scholars who are at the rank of chair professor or distinguished professor, as well as 24 scholars who are at the rank of full professor, associate professor, assistant professor, or lecturer.