基于数据驱动的方法已广泛应用于电力负荷预测领域,以提升预测精度。然而,当售电公司接入新用户时,由于缺乏用户历史用电数据,常规数据驱动方法的适用性会受到一定限制。为解决这一问题,文章提出了一种基于域对抗迁移网络(domain adversarial transfer network
DATN)的短期电力负荷预测方法。该模型利用Transformer模型作为特征提取器,以捕捉负荷数据中的动态特征和时间依赖性。随后,负荷预测器基于这些特征精准预测未来的负荷情况。判别器与特征提取器的对抗学习,确保模型能够学习到深层域不变特征,同时结合多核最大均值差异(multi-kernel maximum mean discrepancy
School of Economics and Management, North China Electric Power University, Beijing, 102206, China,Center for Energy and Environmental Policy Research, Beijing Institute of Technology, Beijing, 100181, China,China Electric Power Research Institute, Beijing, 100192, China,School of Economics and Management, North China Electric Power University, Beijing, 102206, China,Shanghai Electric Power Company, Shanghai, 200122, China.Short term electricity load forecasting using a hybrid model[J].Energy,2018.
Mauro Ribeiro,Katarina Grolinger,Hany F. ElYamany,Wilson A. Higashino,Miriam A.M. Capretz.Transfer learning with seasonal and trend adjustment for cross-building energy forecasting[J].Energy & Buildings,2018.