| Citation: | SU W，XIAO X L，SHI M M，et al. Periodic pattern-enhanced multi-view short-term load prediction[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（2）：477-486 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0399
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Short-term load prediction is essential to ensure the proper operation of the power system. The existing efforts have two limitations: lack of mining the dependencies between features and ignore the periodic pattern of load changes. To solve the above limitations, we propose periodic Pattern-enhanced MultI-view Short-term power load prediction nEtworks, dubbed EPISODE. The framework includes two core components: a multi-view feature learning component and a periodic pattern-enhanced load prediction component. The former aims to effectively extract static features and time series features to obtain enhanced feature representation; the latter is to perform general time series mining and periodic time series mining to obtain comprehensive historical feature representation. The combination of the two aforementioned qualities results in the realization of the short-term load forecast. Extensive experiments have been conducted on real-world datasets, and the experimental results demonstrate the superiority of our proposed method.
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