周期规律增强的多视角短期电力负荷预测

Periodic pattern-enhanced multi-view short-term load prediction

短期电力负荷预测对电力系统的可靠运行具有重要意义。现有方法存在如下问题:缺乏对特征之间依赖关系的挖掘;忽略了电力负荷变化的周期性规律。为此,提出一种周期规律增强的多视角短期电力负荷预测网络(EPISODE)方法。EPISODE方法主要包括2个核心组件:多视角特征学习组件和周期规律增强的电力负荷预测组件。前者旨在有效提取电力负荷数据中的静态特征与时序特征,以得到增强的特征表示;后者则是对电力负荷数据进行一般性时序挖掘和周期性时序挖掘,从而得到全面的电力负荷历史数据表征。基于后期融合的方式,实现短期电力负荷预测。在真实公开的电力负荷预测数据集上进行了大量实验。实验结果证明了所提方法相比现有基准方法的先进性。

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.