FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting

• URL: http://arxiv.org/abs/2604.22328v1
• Date: Fri, 24 Apr 2026 08:00:50 GMT
• Title: FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
• Abstract summary: Foundation models that aim to learn generalizable patterns via extensive pretraining have shown superior performance in multiple prediction tasks.<n>Despite their success and strong potential to address challenges in energy forecasting, their application in this domain remains largely unexplored.
• Score: 0.39373541926236766
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operation. Yet, it remains largely a dataset-specific task, requiring comprehensive training data, limiting scalability, and resulting in high model development and maintenance effort. Recently, foundation models that aim to learn generalizable patterns via extensive pretraining have shown superior performance in multiple prediction tasks. Despite their success and strong potential to address challenges in energy forecasting, their application in this domain remains largely unexplored. We address this gap by presenting the Foundation Models in Energy Time Series Forecasting (FETS) benchmark. We (1) provide a structured overview of energy forecasting use cases along three main dimensions: stakeholders, attributes, and data categories; (2) collect and analyze 54 datasets across 9 data categories, guided by typical stakeholder interests; (3) benchmark foundation models against classical machine learning approaches across different forecasting settings. Foundation models consistently outperform dataset-specific optimized machine learning approaches across all settings and data categories, despite the latter having seen the full historic target data during training. In particular, covariate-informed foundation models achieve the strongest performance. Further analysis reveals a strong correlation between predictive performance and spectral entropy, performance saturation beyond a certain context length, and improved performance at higher aggregation levels such as national load, district heating, and power grid data. Overall, our findings highlight the strong potential of foundation models as scalable and generalizable forecasting solutions for the energy domain, particularly in data-constrained and privacy-sensitive settings.

Related papers

• An Empirical Benchmark of Deep Time-Series Models for Smart Meter Energy Forecasting [1.539942973115038]
  We present an empirical benchmark of nine modern deep learning models for time-series forecasting.<n>We evaluate these models on two publicly available smart meter datasets.
  arXiv  Detail & Related papers  (2026-08-19T08:27:48Z)
• Time Series Foundation Models as Strong Baselines in Transportation Forecasting: A Large-Scale Benchmark Analysis [5.316908050163474]
  This paper evaluates whether general-purpose time-series foundation models can serve as forecasters for transportation tasks.<n>We benchmark the zero-shot performance of the state-of-the-art model, Chronos-2, across ten real-world datasets.<n>We find that, even without any task-specific fine-tuning, Chronos-2 delivers state-of-the-art or competitive accuracy across most datasets.
  arXiv  Detail & Related papers  (2026-02-27T18:10:54Z)
• On Electric Vehicle Energy Demand Forecasting and the Effect of Federated Learning [1.2599533416395765]
  Energy Demand Forecasting of Electric Vehicle Supply Equipments (EVSEs) is one of the most critical operations for ensuring efficient energy management and sustainability.<n>It enables utility providers to anticipate energy/power demand, optimize resource allocation, and implement proactive measures to improve grid reliability.<n>As concerns and restrictions about privacy and sustainability have grown, training data has become increasingly fragmented.
  arXiv  Detail & Related papers  (2026-02-24T11:21:45Z)
• It's TIME: Towards the Next Generation of Time Series Forecasting Benchmarks [87.7937890373758]
  Time series foundation models (TSFMs) are revolutionizing the forecasting landscape from specific dataset modeling to generalizable task evaluation.<n>We introduce TIME, a next-generation task-centric benchmark comprising 50 fresh datasets and 98 forecasting tasks.<n>We propose a novel pattern-level evaluation perspective that moves beyond traditional dataset-level evaluations based on static meta labels.
  arXiv  Detail & Related papers  (2026-02-12T16:31:01Z)
• Generalist vs Specialist Time Series Foundation Models: Investigating Potential Emergent Behaviors in Assessing Human Health Using PPG Signals [22.364607686570384]
  Foundation models are large-scale machine learning models that are pre-trained on massive amounts of data.<n>Recent works, such as MOMENT, train a generalist time series foundation model with data from multiple domains.<n>This paper aims to conduct a comprehensive benchmarking study to compare the performance of generalist and specialist models.
  arXiv  Detail & Related papers  (2025-10-16T03:13:04Z)
• Estimating Time Series Foundation Model Transferability via In-Context Learning [74.65355820906355]
  Time series foundation models (TSFMs) offer strong zero-shot forecasting via large-scale pre-training.<n>Fine-tuning remains critical for boosting performance in domains with limited public data.<n>We introduce TimeTic, a transferability estimation framework that recasts model selection as an in-context-learning problem.
  arXiv  Detail & Related papers  (2025-09-28T07:07:13Z)
• IISE PG&E Energy Analytics Challenge 2025: Hourly-Binned Regression Models Beat Transformers in Load Forecasting [0.0]
  This study evaluates forecasting models ranging from classical regression techniques to advanced deep learning architectures.<n>The dataset includes two years of historical electricity load data, alongside temperature and global horizontal irradiance (GHI) across five sites.<n>Our results reveal that deep learning models, including TimeGPT, fail to consistently outperform simpler statistical and machine learning approaches.
  arXiv  Detail & Related papers  (2025-05-16T15:55:34Z)
• Towards Data-Efficient Pretraining for Atomic Property Prediction [51.660835328611626]
  We show that pretraining on a task-relevant dataset can match or surpass large-scale pretraining.<n>We introduce the Chemical Similarity Index (CSI), a novel metric inspired by computer vision's Fr'echet Inception Distance.
  arXiv  Detail & Related papers  (2025-02-16T11:46:23Z)
• Tackling Data Heterogeneity in Federated Time Series Forecasting [61.021413959988216]
  Time series forecasting plays a critical role in various real-world applications, including energy consumption prediction, disease transmission monitoring, and weather forecasting. Most existing methods rely on a centralized training paradigm, where large amounts of data are collected from distributed devices to a central cloud server. We propose a novel framework, Fed-TREND, to address data heterogeneity by generating informative synthetic data as auxiliary knowledge carriers.
  arXiv  Detail & Related papers  (2024-11-24T04:56:45Z)
• F-FOMAML: GNN-Enhanced Meta-Learning for Peak Period Demand Forecasting with Proxy Data [65.6499834212641]
  We formulate the demand prediction as a meta-learning problem and develop the Feature-based First-Order Model-Agnostic Meta-Learning (F-FOMAML) algorithm. By considering domain similarities through task-specific metadata, our model improved generalization, where the excess risk decreases as the number of training tasks increases. Compared to existing state-of-the-art models, our method demonstrates a notable improvement in demand prediction accuracy, reducing the Mean Absolute Error by 26.24% on an internal vending machine dataset and by 1.04% on the publicly accessible JD.com dataset.
  arXiv  Detail & Related papers  (2024-06-23T21:28:50Z)
• Pushing the Limits of Pre-training for Time Series Forecasting in the CloudOps Domain [54.67888148566323]
  We introduce three large-scale time series forecasting datasets from the cloud operations domain. We show it is a strong zero-shot baseline and benefits from further scaling, both in model and dataset size. Accompanying these datasets and results is a suite of comprehensive benchmark results comparing classical and deep learning baselines to our pre-trained method.
  arXiv  Detail & Related papers  (2023-10-08T08:09:51Z)
• Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
  Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch. In this paper, we collected large-scale load datasets and released a new renewable energy dataset. We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
  arXiv  Detail & Related papers  (2023-07-14T06:50:02Z)
• Short-Term Load Forecasting using Bi-directional Sequential Models and Feature Engineering for Small Datasets [6.619735628398446]
  This paper presents a deep learning architecture for short-term load forecasting based on bidirectional sequential models. In the proposed architecture, the raw input and hand-crafted features are trained at separate levels and then their respective outputs are combined to make the final prediction. The efficacy of the proposed methodology is evaluated on datasets from five countries with completely different patterns.
  arXiv  Detail & Related papers  (2020-11-28T14:11:35Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.