Comparative Analysis of Zero-Shot Capability of Time-Series Foundation Models in Short-Term Load Prediction
Nan Lin (TU Delft - Intelligent Electrical Power Grids)
Dong Yun (Technical University of Denmark (DTU))
Weijie Xia (TU Delft - Intelligent Electrical Power Grids)
Peter Palensky (TU Delft - Electrical Sustainable Energy)
Pedro P. Vergara (TU Delft - Intelligent Electrical Power Grids)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
Short-term load prediction (STLP) is critical for modern power distribution system operations, particularly as demand and generation uncertainties grow with the integration of low-carbon technologies, such as electric vehicles and photovoltaics. In this study, we evaluate the zero-shot prediction capabilities of five Time-Series Foundation Models (TSFMs) - a new approach for STLP where models perform predictions without task-specific training - against two classical models, Gaussian Process (GP) and Support Vector Regression (SVR), which are trained on task-specific datasets. Our findings indicate that even without training, TSFMs like Chronos, TimesFM, and TimeGPT can surpass the performance of GP and SVR. This finding highlights the potential of TSFMs in STLP.