This letter proposes a transferrable model-agnostic meta-learning (T-MAML) approach for short-term load forecasting for single households. The proposed approach enables multiple households to collaboratively train a generic artificial neural network (ANN) model. The generic ANN model is then further trained at each target household node for the STLF purpose. The proposed T-MAML based STLF approach is featured by: (1) significant reduction of computation and communication costs on the household side; and (2) superior STLF performance, especially when there is limited load data for training in a target household. Experiments based on a real Australian residential dataset are conducted to validate the effectiveness of the proposed approach.

中文翻译：

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具有有限训练数据的短期家庭负荷预测的可转移模型不可知元学习

这封信提出了一种可转移的与模型无关的元学习 (T-MAML) 方法，用于单户家庭的短期负荷预测。所提出的方法使多个家庭能够协作训练通用人工神经网络 (ANN) 模型。然后为了 STLF 目的，在每个目标家庭节点处进一步训练通用 ANN 模型。所提出的基于 T-MAML 的 STLF 方法的特点是：（1）显着降低了家庭侧的计算和通信成本；(2) 卓越的 STLF 性能，特别是当目标家庭的训练负荷数据有限时。进行了基于真实澳大利亚住宅数据集的实验，以验证所提出方法的有效性。