An adaptive deep-learning load forecasting framework by integrating transformer and domain knowledge

Advances in Applied Energy(2023)

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摘要
•Constructed a novel knowledge and data dual-driven approach (Adaptive-TgDLF) that makes full use of human knowledge and advanced deep learning techniques.•Employed adaptive learning to utilize load data at various locations and times, which improves the generalization ability of model.•Proposed a method to mine interpretability of the deep-learning model for load forecasting via attention matrix.•The proposed model is stronger (being 16% more accurate), more robust (the performance of the proposed model with 50% weather noise is the same as that of the previous efficient model without weather noise), easier to train (saving more than half of the training time), and requires less data.
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关键词
Load forecasting, Deep-learning, Domain knowledge, Transfer learning, Online learning, Interpretability
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