(1)
Constructed a novel knowledge and data dual-driven approach (Adaptive-TgDLF) that makes full use of human knowledge and advanced deep learning techniques
.
(2)
Employed adaptive learning to utilize load data at various locations and times, which improves the generalization ability of model
.
(3)
Proposed a method to mine interpretability of the deep-learning model for load forecasting via attention matrix
.
(4)
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
.