金沙集团888881学术报告暨研究生学术年会
–Price Spike Classification and Regression Using A Hybrid Oversampling Method
报告题目：Price Spike Classification and Regression Using A Hybrid Oversampling Method
报告人：Chenxu Zhang
报告时间：2023年12月13日（周三）下午15：30
报告地点：线下地点：6A-301
主办单位：金沙集团888881
报告人介绍：Chenxu Zhang (M’22, S’12) received the B.S. degree from the Shandong University of Science and Technology, Qingdao, China, in 2013, the M.S. degree from the Illinois Institute of Technology, Chicago, IL, USA, in 2015, and the Ph.D. degree from the Mississippi State University, Starkville, MS, USA, in 2022, all in electrical engineering. Dr. Zhang is also a member of Eta Kappa Nu (IEEE-HKN) gamma omega chapter, which is an honor society of IEEE. Currently, he is a lecturer with the School of Electrical and Information Engineering, Xihua University, SiChuan, China. His research interests include electricity price forecast, power systems optimization and economics, contingency management, and parallel computing algorithms and applications.
报告内容简介：Price spikes are types of electricity prices that are much higher than normal electricity prices. However, due to low occurrence, price spikes are hard to be predicted by normal price forecast methods, which are designed to model patterns from the dataset with massive normal price cases. To facilitate price spike classification and regression, in this presentation, a hybrid oversampling method is proposed to increase the number of price spike cases. As electricity prices are time series data, to enable that the synthetic price spike cases have the similar structure as the real ones, the enhanced structure preserving oversampling technique is applied to conserve the temporal relationships. Also, the synthetic minority oversampling method for regression is utilized to ensure the authenticity of the oversampled price spike values. To evaluate the effectiveness of the proposed hybrid oversampling method, numerous case studies are analyzed, and the promising results demonstrate the capability of the proposed method to improve price spike classification and regression accuracy.
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