Empirical Assessment of Time-Series Foundation Models For Power System Forecasting Applications

TL;DR

This paper empirically benchmarks state-of-the-art time series foundation models for power system forecasting, evaluating their performance on solar, wind, and load data from ERCOT to guide practical application choices.

Contribution

It provides a comprehensive comparison of transformer-based and deep learning models for power forecasting, highlighting their strengths and limitations across various capabilities.

Findings

Transformer models show promise in multivariate and probabilistic forecasting.

Fine tuning efficiency varies significantly among models.

Models differ in horizon sensitivity and generalization to unseen sites.

Abstract

Accurate forecasting of electric load and renewable generation is essential for reliable and cost effective power system operations. Recent advances in transformer based and foundation machine learning models, driven by large scale pretraining, increased available data and computation, in addition to architectural innovations, have shown promise in time series forecasting across multiple domains. However, their application to power system forecasting tasks remains largely underexplored. This work presents a comprehensive, empirical benchmark of state of the art time series foundation models, transformer architectures, and deep learning baselines for solar, wind, and load forecasting using the high resolution ARPAE PERFORM dataset for the Electric Reliability Council of Texas (ERCOT) grid. Eight core capabilities are assessed, including zero shot performance, fine tuning efficiency, multivariate input and output handling, horizon sensitivity, generalization to unseen sites, probabilistic forecasting, and context window effects. Models evaluated include TimesFM, Chronos Bolt, MoiraiL, MOMENT, Tiny Time Mixer, Temporal Fusion Transformer, PatchTST, TimeXer, LSTM, and CNN. The manuscript aims to provide clear guidance on when foundation models can provide enhanced renewable and load forecasting capabilities and when other approaches remain the more practical choice for power system operations.