基于STE-TCN的中短期电力负荷预测

Short- and Medium-term Power Load Forecasting Based on STE-TCN

目的 针对传统电力负荷预测模型对长序列预测精度低的问题,提出一种结合跳级卷积连接与时间编码网络的新型时序卷积神经网络(TCN)模型——STE-TCN模型。方法 首先对TCN模型加入跨周期的膨胀卷积通道(Skip-convolution)提取电力数据周期信息;再进行特征融合得到Skip-TCN网络,使网络抓取周期规律,增加信息利用长度;最后设计日期编码网络(Time encoding network)捕捉生活周期和季节性特征,与Skip-TCN进行特征融合得到STE-TCN模型,实现对电力负荷数据长序列预测。结果 实验表明:在与TCN模型和传统时序网络的对比下,Skip-TCN的预测精度均有提升,在预测长度更长的测试上提升尤为明显。结论 实验结果验证了通过对更长跨度时序关系的捕捉,STE-TCN网络改进方法有效提升了对长序列电力负荷的预测精度。

Objective In response to the problem of low prediction accuracy of traditional power load forecasting models for long sequences,a novel temporal convolutional neural network(TCN) model called STE-TCN,combining skip-level convolutional connections and time encoding networks,was proposed.Methods Firstly,skip-convolution channels across periods were added to the TCN model to extract cycle information from power data.The Skip-TCN network was obtained by fusing the features,so that the network captures the cycle pattern and increases the length of information utilization.Finally,the time encoding network was designed to capture the life cycle and seasonal features,and the STE-TCN model was obtained by fusing the features with the Skip-TCN.The long sequence prediction of power load data was realized.Results Experimental results show that compared with the TCN model and traditional sequential networks,Skip-TCN exhibited improved prediction accuracy,especially in longer prediction tests.Conclusion Experimental results validate that by capturing longer-spanned temporal relationships,the improved method of STE-TCN network effectively enhances the prediction accuracy of long sequence power load data.