Interpretable Short-Term Load Forecasting via Multi-Scale Temporal Decomposition

arXiv:2402.11664v1 Announce Type: new
Abstract: Rapid progress in machine learning and deep learning has enabled a wide range of applications in the electricity load forecasting of power systems, for instance, univariate and multivariate short-term load forecasting. Though the strong capabilities of learning the non-linearity of the load patterns and the high prediction accuracy have been achieved, the interpretability of typical deep learning models for electricity load forecasting is less studied. This paper proposes an interpretable deep learning method, which learns …

abstract applications arxiv capabilities cs.lg deep learning eess.sp electricity forecasting instance machine machine learning multivariate patterns power prediction progress scale systems temporal type via