Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting tasks. However, real-world load forecasting often involves multiple target variables and requires the integration of exogenous variables, raising important questions about the utility of TSFMs in realistic settings. In this study, we position Chronos-2, a recently developed model by Amazon, as a representative multi-channel TSFM that supports univariate, multivariate, and covariate-informed forecasting, and conduct a systematic investigation of how such models can be used for real-world load forecasting. While prior work has evaluated Chronos-2 on a limited number of energy-related tasks in a zero-shot setting, its performance relative to established task-specific deep learning models and its behavior when adapted using task-specific historical data remains insufficiently understood. In this work, we evaluate Chronos-2 on two real-world utility datasets, ISO New England and ENTSO-E, and benchmark it against widely used task-specific deep learning models. Our results show that Chronos-2 benefits substantially from task-specific fine-tuning and achieves strong short-horizon forecasting performance, but its zero-shot accuracy lags behind task-specific models and its forecasting error grows more rapidly with increasing forecast steps. Overall, this study provides a detailed characterization of the strengths and limitations of TSFMs such as Chronos-2 in grid load forecasting and offers practical insights into how a pretrained TSFM can be effectively adapted for operational load forecasting applications.
Figures & tables
Dataset
Weather covariates
Train split
Test split
ISO‑New England (8 zones)
Dew point and dry bulb temperature
2011–2020 (3,653 days)
2021 (365 days)
ENTSO-E (6 countries)
Temperature; direct and diffuse horizontal radiation
TABLE III: Average MAPE of models trained for different forecast horizons over ISO New England ∗ .
Method
Covariates
Model type
H=30
H=60
H=90
MMMF
✓
TCN
4.52
4.51
4.43
LSTM
5.21
5.22
5.39
Transformer
4.65
4.7
4.82
Chronos-2
✓
Zero-shot
5.63
6.57
7.67
LoRA fine-tune
4.54
4.91
5.32
Full fine-tune
4.31
4.66
4.86
TABLE IV: Average MAPE of models trained for different forecast horizons over ENTSO-E ∗ .
Encoding
Zero-shot
Full fine-tune
No date features
10.15
5.58
Ordinal
10.33
5.3
Cyclical
10.61
5.19
∗ Table shows average MAPE across horizon H=60 .
TABLE V: Ablation study over ISO New England on the impact of calendar feature encoding ∗ .
Fig. 1: Weekly forecast performance. Columns correspond to model types, and rows correspond to datasets. Each model is trained with forecasting horizons H∈{30,60,90} . The y-axis shows average MAPE by week (x-axis), and error bars denote the standard deviation across five random seeds.
Fig. 2: Forecast performance of zero-shot Chronos-2 in cold-start scenarios, with and without ICL in (a) ISO New England (b) ENTSO-E. X-axis represents the context length of each target zone. Each ICL line plot represents a cross-learning scenario with the context length of non-target zone ∈{30,60,90} Note: Y-axes are scaled differently across subplots to best visualize the trends within each dataset.
Accurate short-term load forecasting (STLF) is essential for the reliable and efficient operation of modern power systems. While time series foundation models (TSFMs) have recently demonstrated remarkable performance across a wide range of forecasting tasks, their effectiveness for STLF under realistic operational conditions remains largely unexplored. In this paper, we present a comprehensive benchmark of four trained-from-scratch (TFS) models and four TSFMs across three real-world load forecasting datasets under operational scenarios that differ in the availability and quality of future covariate information. Our results show that Chronos-2 consistently achieves state-of-the-art performance in both zero-shot and fine-tuned settings when future covariates are available or accurately forecast. However, its performance degrades as covariate forecasts become increasingly noisy, whereas TimesNet exhibits greater robustness under severe covariate uncertainty. These findings demonstrate the effectiveness of covariate-informed TSFMs for STLF while highlighting the critical role of robust covariate modeling in real-world forecasting applications.
Tomas Kaljevic, Ivan Arzola, Yu Zhang
Department of Electrical and Computer Engineering University of California, Santa Cruz
Accurate load forecasting at multiple grid levels is essential for future smart grids, ranging from aggregated control area forecasts for balancing supply and demand to forecasts of individual end-consumer loads for demand-side management and energy management systems. We present a comprehensive benchmark for load forecasting across grid levels, comprising three datasets that represent a transmission system operator control area, low-voltage grid feeders, and individual end consumers. We evaluate ten methods for short-term load forecasting and find that Transformer-based approaches consistently outperform established methods, reducing forecast error by 6.6-10.7 %. To analyze the impact of architectural design, we introduce YAformer, a flexible Transformer architecture that integrates modifications from prior work and is optimized via hyperparameter optimization. However, the standard Transformer achieves superior performance, suggesting that these architectural modifications are not required for accurate load forecasting. We further evaluate the Transformer-based time-series foundation model Chronos-2, which demonstrates competitive zero-shot performance on two datasets but fails to accurately capture special events in the TSO data. Detailed analyses reveal model-specific strengths and weaknesses, and ablation studies highlight the importance of long input contexts, covariates and continuous retraining - aspects that are often overlooked in the time-series forecasting literature.
Matthias Hertel, Sebastian Pütz, Jonathan Kolar +3
Karlsruhe Institute of Technology, Germany · Helmholtz AI, Germany
Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation. However, current forecasting methods require significant manual effort, often lack uncertainty estimation and proper peak prediction, and they are often not adequately evaluated in terms of grid requirements. In the present study, we provide an extensive evaluation of short-term net load forecasts of 200 real-world low-voltage feeders with a focus on the rapidly evolving time series foundation models. Our study compares Chronos-Bolt, Chronos-2 and TabPFN-TS to six baseline models and demonstrates superior performance, in particular for Chronos-2. An ablation study, in which weather covariates are omitted, shows that time series foundation models adapt to increased uncertainty, despite the importance of weather information. A novel application-oriented metric links the model's forecasting capabilities in peak prediction to the trade-off in grid asset planning and operation between cost reduction and minimizing the risk of failure.
Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber +5
Karlsruhe Institute of Technology (KIT), Germany and Netze BW GmbH, Germany · Netze BW GmbH, Germany · Karlsruhe Institute of Technology (KIT), Germany