With increasing installation of behind-the-meter distributed energy resources (DERs) at the household level, the power load profile has been significantly masked. As a result, original load forecasting models are becoming not suitable for the continuously masked-load. Besides, present masked-load may not have sufficient samples to train an accurate forecasting model. This letter proposed a transfer learning-based method to solve this problem by capturing different but related relationships between the unmasked-load and masked-load datasets. The common feature vectors from the two datasets are extracted by adversarial training, then the feature vectors from masked-load could be compatible with a predictor which is based on feature vectors from unmasked-load, thus masked-load could be accurately predicted. Simulation results show that the proposed method has higher accuracy compared to the benchmark models.

中文翻译：

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一种利用分布式能源资源预测掩蔽负荷的迁移学习方法

随着家庭级分布式能源 (DER) 的安装越来越多，电力负荷分布已被显着掩盖。结果，原始负荷预测模型变得不适合连续屏蔽负荷。此外，目前的掩蔽负载可能没有足够的样本来训练准确的预测模型。这封信提出了一种基于迁移学习的方法，通过捕获未屏蔽负载和屏蔽负载数据集之间不同但相关的关系来解决此问题。通过对抗训练从两个数据集中提取共同的特征向量，然后掩蔽负载的特征向量可以与基于非掩蔽负载的特征向量的预测器兼容，从而可以准确地预测掩蔽负载。