학술논문
Interpretable short-term load forecasting via multi-scale temporal decomposition
Document Type
Article
Author
Source
In Electric Power Systems Research October 2024 235
Subject
Language
ISSN
0378-7796
Abstract
Highlights •A novel deep learning based, post-hoc global interpretability method is proposed based on multi-scale temporal decomposition.•Comparison to baseline load forecasting models demonstrate both superior forecasting accuracy and physically consistent interpretations.•Flexible structure can be utilized for power forecasting with various temporal patterns by adjusting decomposition kernels and the kernel sizes.