基于峰谷结构特征与sLSTM的短期负荷预测方法OA

Under increasingly volatile load conditions,existing short-term load forecasting methods exhibit limited capability in characterizing structural turning features in load sequences,such as peaks and valleys,resulting in degraded prediction accuracy at peak-valley transition moments.To address this issue,a short-term load forecasting method that incorporates peak-valley structural features and a scalar long short-term memory(sLSTM)network is proposed.First,local extrema are extracted from historical load series to construct structured features,including peak-valley state labels,occurrence probabilities,and temporal distances,enabling effective capture of key load transition patterns.Then,an sLSTM is introduced to design a multi-task shared encoding layer,so as to improve the joint modeling capability for load abrupt characteristics and long-term temporal dependencies.Meanwhile,a peak-flat-valley three-classification auxiliary task is constructed to form a multi-task collaborative optimization framework together with the primary load forecasting task.Finally,simulation verification is carried out using publicly available power load data from 2013 to 2014 in a region in China.The results show that the proposed method consistently improves the load forecasting accuracy across different day types while significantly enhancing the prediction effect during peak and valley periods.