Hugging Face Trending Papers

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

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.

arXiv Machine Learning

Jul 3

Probabilistic Low-Voltage Peak Load Forecasting with Time Series Foundation Models Evaluated on Application-Oriented Metrics

arXiv:2607. 01966v1 Announce Type: new Abstract: Low-voltage load forecasting is an important component in current and future energy systems with a high degree of electrification and decentralized generation.

By Benedikt Kaas, Manuel Treutlein, Hannes Benedikt Gerber, Oliver Neumann, Cheewan Phatthanakhuha, Oliver Resch, Ralf Mikut, Veit Hagenmeyer

benchmarks

arXiv Machine Learning

Jul 20

A Benchmark for Electrical Load Forecasting Across Grid Levels: Time-Series Transformers Outperform Established Methods

arXiv:2607. 15705v1 Announce Type: new Abstract: 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.

By Matthias Hertel, Sebastian P\"utz, Jonathan Kolar, Benjamin Sch\"afer, Ralf Mikut, Veit Hagenmeyer

llms benchmarks

arXiv Machine Learning

Jul 7

Evaluating Time Series Foundation Models for Electricity Price Forecasting: Contamination Risk, Distributional Shifts, and Covariate Dependence

arXiv:2607. 02623v1 Announce Type: new Abstract: Time series foundation models (TSFMs) have shown strong zero-shot forecasting performance, but their generalization in covariate-driven, non-stationary settings is underexplored.

By Zhenghua Pan, Ahmed Aziz Ezzat

benchmarks

arXiv AI

Jul 21

FETS Benchmark: Foundation Models Enable Scalable and Generalizable Energy Time Series Forecasting

arXiv:2604. 22328v2 Announce Type: replace-cross Abstract: Driven by the transition towards a climate-neutral energy system, accurate energy time series forecasting is critical for planning and operations.

By Marco Obermeier, Marco Pruckner, Florian Haselbeck, Andreas Zeiselmair

benchmarks

arXiv AI

Aug 6

Short-term load forecasting under EU-AI Act Requirements in Safety-Critical Environments: Results from a 41-day live challenge on the aggregated German transmission-grid load

arXiv:2608. 05018v1 Announce Type: new Abstract: Short-term load forecasting (STLF) play a vital role in the electric power industry.

By Thomas Bartz-Beielstein

benchmarks safety

arXiv Machine Learning

Jun 9

Investigating Calibration Challenges in Probabilistic Electricity Price Forecasting

arXiv:2606. 09517v1 Announce Type: new Abstract: As renewable energy integration increases market volatility, probabilistic electricity price forecasting has become essential for effective risk management.

By Jan Niklas Lettner, Hadeer El Ashhab, Benjamin Sch\"afer

arXiv Machine Learning

Jun 8

Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

arXiv:2606. 07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters.

By Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni, Riccardo Rosati

arXiv Machine Learning

Jul 24

SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting

arXiv:2607. 20587v1 Announce Type: cross Abstract: Modern power systems increasingly require probabilistic forecasts amid interacting uncertainties from renewable intermittency, flexible demand, market volatility, and weather-dependent generation.

By Hang Ye, Xinyan Jiang, Yuedong Shi, Yangxin Zhu, Jianming Wei, Tian Zheng, Xiaoying Zheng, Yongxin Zhu

safety

arXiv Machine Learning

Jun 15

Trend-Aware Multi-Task Learning for Short-Term Energy Forecasting

arXiv:2511. 09789v3 Announce Type: replace Abstract: Short-term energy forecasting plays an important role in real-time operational decision-making, such as electricity market bidding and power system dispatch, where both numerical accuracy and correct directional signals are essential.

By Fulong Yao, Wanqing Zhao, Chao Zheng, Xiaofei Han

benchmarks

arXiv Machine Learning

Jun 3

Enhanced Renewable Energy Forecasting using Context-Aware Conformal Prediction

arXiv:2510. 15780v2 Announce Type: replace-cross Abstract: Artificial intelligence (AI) is increasingly used to support renewable energy forecasting and grid operations.

By Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar, Pascal Van Hentenryck

arXiv Machine Learning

Jun 17

Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

arXiv:2606. 17692v1 Announce Type: new Abstract: Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns.

By Vansh Bansal

llms benchmarks safety

arXiv Machine Learning

Jun 18

Benchmarking Physics-Informed Time-Series Models for Operational Global Station Weather Forecasting

arXiv:2406. 14399v4 Announce Type: replace Abstract: The development of Time-Series Forecasting (TSF) models is often constrained by the lack of comprehensive datasets, especially in Global Station Weather Forecasting (GSWF), where existing datasets are small, temporally short, and spatially sparse.

By Tao Han, Zhibin Wen, Zhenghao Chen, Dazhao Du, Song Guo, Lei Bai

llms benchmarks safety

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