arXiv Machine Learning

Hierarchical Probabilistic Conformal Prediction for Distributed Energy Resources Adoption

arXiv:2411. 12193v4 Announce Type: replace-cross Abstract: The rapid growth of distributed energy resources (DERs) presents both opportunities and operational challenges for electric grid management.

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 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

Jul 1

Relational and Sequential Conformal Inference for Energy Time Series over Graphs via Foundation Models

arXiv:2606. 31804v1 Announce Type: new Abstract: Accurate energy demand forecasting is essential for the reliable operation and planning of modern sustainable energy systems.

By Keivan Faghih Niresi, Alice Cicirello, Olga Fink

llms benchmarks safety

arXiv Machine Learning

Jul 14

Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting

arXiv:2607. 11470v1 Announce Type: cross Abstract: Reliable uncertainty quantification is essential for integrating solar and wind generation into modern power systems, where operators must weigh risk rather than act on point forecasts alone.

By Shreedhar Gangwar (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Abhinav Bains (B. R. Ambedkar National Institute of Technology, Jalandhar, India), Banalaxmi Brahma (B. R. Ambedkar National Institute of Technology, Jalandhar, India)

arXiv AI

Jun 2

EnergyMamba: An Uncertainty-Aware Graph-Enhanced Selective State Space Model for Energy Consumption Prediction

arXiv:2606. 00506v1 Announce Type: new Abstract: Energy consumption prediction is essential for efficient grid management, demand-side optimization, and sustainable energy planning.

By Dahai Yu, Rongchao Xu, Lin Jiang, Guang Wang

benchmarks

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

Hugging Face Trending Papers

Jul 2

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.

benchmarks

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 AI

Jun 2

Certificates without Electrons? Theory and Evidence on Impacts from AI-Driven Power Demand

arXiv:2606. 00811v1 Announce Type: cross Abstract: Data centers now account for 4.

By Dana Golden, Aruna Balasubramanian, Niranjan Balasubramanian

llms

arXiv Machine Learning

Jul 28

Adaptive Multi-Scale Forecasting and Gate-Localized Conformal Prediction for Multivariate Nonstationary Time Series

arXiv:2607. 23165v1 Announce Type: cross Abstract: We propose ABF-T-GLCP, a model-agnostic framework for forecasting and uncertainty quantification in nonstationary multivariate time series.

By Ziling Ma, Junshu Jiang, \'Angel L\'opez-Oriona, Ying Sun, Hernando Ombao

benchmarks

arXiv AI

2d ago

Graph Machine Learning: An Opportunity for Power Systems

arXiv:2608. 16494v1 Announce Type: cross Abstract: Modern power systems face growing operational complexity driven by the integration of renewable energy sources, decentralization, and the need for real-time decision-making across a wide range of timescales.

By Martin Sadric, Sebastian P\"utz, Christian Nauck, Veit Hagenmeyer, Frank Hellmann, Dirk Witthaut, Benjamin Sch\"afer

benchmarks safety

arXiv Machine Learning

Jul 31

Bridging AI and Energy Forecasting: An Autonomous Workflow with Customized Toolkit

arXiv:2307. 07191v3 Announce Type: replace Abstract: Energy forecasting is crucial for the power grid, but fundamentally different from general time series analysis: it highly relies on covariates like meteorological factors, and its goals must align with actual power grid operations, such as risk assessment and system reliability.

By Zhixian Wang, Leandro Von Krannichfeldt, Qingsong Wen, Chaoli Zhang, Liang Sun, Shirui Pan, Yi Wang

llms agents benchmarks

Related stories