arXiv Machine Learning

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.

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

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 AI

Aug 18

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

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arXiv Machine Learning

Aug 10

ArchEGraph: A Large-Scale Graph Dataset for Geometry-Topology-Physics Aligned Building Energy Modeling

arXiv:2608. 06772v1 Announce Type: new Abstract: Accurate estimation of building energy use is essential for achieving carbon neutral and sustainable buildings.

By Yihui Li, Yihui Chen, Kaidi Zha, Xiaoyue Yan, Zhexuan Yu, Shiqi Dai, Jun Xiao, Jun Yin, Ramon Elias Weber, Borong Lin

benchmarks

arXiv Machine Learning

Jun 10

COGENT: Continuous Graph Emulators with Neural Ordinary Differential Equations for Long-Term Physical Forecasting

arXiv:2606. 11162v1 Announce Type: new Abstract: In this work, we present COGENT, a continuous graph emulator with Neural Ordinary Differential Equations for long-term physical forecasting on irregular geospatial meshes.

By Zesheng Liu, Maryam Rahnemoonfar

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

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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 Machine Learning

Aug 4

Multi-Source Dynamic Graph Learning for Compound-Flood Forecasting in Managed Coastal Systems

arXiv:2608. 01775v1 Announce Type: new Abstract: Compound flooding in managed coastal systems is influenced by hydrological conditions and water-management activity observed across multiple monitoring stations.

By Liangjun You, Min Wu, Orlando Woods, Dongsheng Luo

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arXiv Machine Learning

Jun 11

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.

By Wenbin Zhou, Shixiang Zhu

arXiv Machine Learning

Sep 15

Beyond Point Forecasts: A Survey on Probabilistic Forecasting for Time Series and Spatiotemporal Data

arXiv:2609.13345v1 Announce Type: cross Abstract: Probabilistic forecasting is central to decision-making under uncertainty, yet its methodological landscape has become increasingly fragmented across...

By Donia Besher, Rajdeep Pathak, Madhurima Panja, Tanujit Chakraborty

arXiv Machine Learning

Jun 18

INDEQS: Informed Neural controlled Differential EQuationS

arXiv:2606. 19138v1 Announce Type: new Abstract: Neural Controlled Differential Equations (NCDE) provide a powerful continuous-time framework for forecasting time series, but standard graph-based extensions typically learn spatial structure purely from data, even in settings where a directed graph structure is known a priori.

By Michael Detzel, Gabriel Nobis, Kristiyan Blagov, Juri Schubert, Jackie Ma, Wojciech Samek

benchmarks

arXiv Machine Learning

Sep 22

GraphToolbox: A Configurable Python Framework for Graph Neural Network Forecasting

arXiv:2609.24609v1 Announce Type: new Abstract: Electricity forecasting often involves spatially related signals observed over regions, substations, and feeders, and Graph Neural Networks (GNNs) prov...

By Eloi Campagne (CB), Yvenn Amara-Ouali (LMO, CELESTE), Yannig Goude (EDF R\&D), Argyris Kalogeratos (CB, ENS Paris Saclay)

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