Energy Forecasting

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Period ending 2026-09-21

4 new papers

A weekly snapshot of new work published in Energy Forecasting.

Period ending 2026-09-14

1 new paper

A weekly snapshot of new work published in Energy Forecasting.

43 papers

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PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images
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Multi-Appliance Non-Intrusive Load Monitoring via Label-Preserving Aggregate Recomposition and Prediction Consistency
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Sep 15, 2026Liana Toderean, Tudor Cioara, Vasilis Michalakopoulos +3Energy Forecasting Joint-Embedding Predictive Architectures
Ensemble Complexity in Photovoltaic Forecasting
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Horizon-specific Expert Fusion for Photovoltaic Power Forecasting
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Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Sep 11, 2026Ken Chen, Maneesha Perera, Wei Wang +3Energy Forecasting Solar
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
Aug 6, 2026Yann Fabel, Bijan Nouri, Milon Miah +4Energy Forecasting Generative
Personalized Federated Sparse Adaptation of Time-Series Foundation Models
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An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting
Aug 3, 2026Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah +2Energy Forecasting Atmospheric Dynamics
Skillful forecasting of offshore winds from satellite scatterometer constellations
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A Controlled Visual-Backbone Benchmark for Multimodal Short-Term Solar Irradiance Forecasting
Jul 26, 2026Oshadha Samarakoon, Dushan Herath, Ishara Ranmandala +4Solar Energy Forecasting
SPECTRA: State-Space Exogenous Context and Temporal-Frequency Resolution Architecture for Probabilistic Energy Forecasting
Jul 22, 2026Hang Ye, Xinyan Jiang, Yuedong Shi +5Energy Forecasting State Space Models
A Deep Learning Framework for Predicting Solar EUV Irradiance During Significant Flares
Jul 21, 2026Sathvik Soman, Jason T. L. Wang, Haimin Wang +1Geomagnetic Storms Solar
Probabilistic Physics-Aware Machine Learning Predictions of Electric Truck Energy Consumption with Field Data
Jul 21, 2026Hannes Nilsson, Rafael Basso, Balázs Kulcsár +1Electricity Price Forecasting Electric Vehicles
LLMs and Agentic AI Systems for Smart Grids: A Tutorial on Architectures and Applications
Jul 20, 2026Daniela Rojas, Abdulwahab Albassam, Aidan G. Leung +11Smart Grids Energy Forecasting
Fourier Geometric Wind Power Forecasting with Numerical Weather Prediction
Jul 19, 2026Shiyuan Piao, Fan Zehui, Yang Liu +4Energy Forecasting Numerical Weather Prediction
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Jul 15, 2026Daniel Grillmeyer, Marius Hadry, Michael Stenger +3Energy Forecasting Offshore Wind Turbine
Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
Jul 14, 2026Dandan Chen, Yan Zhao, Xuepeng ChenEnergy Forecasting Numerical Weather Prediction
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
Jul 13, 2026Shreedhar Gangwar, Abhinav Bains, Banalaxmi BrahmaEnergy Forecasting Solar
PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
Jul 9, 2026Hang Fan, Weican Liu, Ying Lu +3Energy Forecasting Distribution Shifts
UniWind: Toward Unified Day-Ahead Wind Power Forecasting via Physics-Informed State Routing
Jul 2, 2026Ronghui Xu, Tongxin Wu, Guozhen Zhang +4Energy Forecasting Physics-Informed Machine Learning
Towards Continuous Power Forecasting: Practical Continual Learning for Real-World Energy Systems in Nonstationary Time Series
Jun 23, 2026Yujiang He, Frederic Uhrweiller, Bernhard SickElectricity Price Forecasting Efficient Continual Learning
QueryMarket: Cost-Aware Online Active Learning in Data Markets
Jun 16, 2026Xiwen Huang, Pierre PinsonLearning-Augmented Algorithms Active Learning
A Statistical and Machine Learning Framework for Operational Threshold Detection and Deployable Dispatch Controller Development in Hydrogen Multi-Energy Systems
Jun 12, 2026Shadi Heenatigala, Hasanika SamarasingheHydrogen Multi-Energy Systems Energy Forecasting
Tyan-WP: A Wind Power Foundation Model for Ultra-Short-Term Probabilistic Forecasting
Jun 7, 2026Jiahui Huang, Ao Luo, Lei Liu +6Energy Forecasting Offshore Wind Turbine
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Jun 5, 2026Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi +2Energy Forecasting Time Series Foundation Models
CF-JEPA: Mask-free forward prediction with asymmetric encoder utilization for time-series representation learning
Jun 5, 2026Jaehoon Lee, Sunghyun SimJoint-Embedding Predictive Architectures Sphere-Jepa
Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
Jun 4, 2026Jingxin Zhang Xiaoqin WangEnergy Forecasting Weather Models
A Systematic Evaluation of Current Architectures in Wind Power Forecasting
Jun 1, 2026Vinicius Bortolini, Gilson Adamczuk Oliveira, Erick Oliveira Rodrigues +1Energy Forecasting Offshore Wind Turbine
Uncertainty-Aware Transfer Learning for Cross-Building Energy Forecasting: Toward Robust and Scalable District-Level Energy Management
May 28, 2026Shadmehr Zaregarizi, Khashayar YavariElectricity Price Forecasting Energy Forecasting
Decision-focused learning for optimal PV-Battery scheduling
May 27, 2026Joris Depoortere, Hussain Kazmi, Johan DriesenPhotovoltaic Energy Forecasting
Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting
May 27, 2026Kourosh Kiani, S. M. MuyeenEnergy Forecasting Multivariate Time Series Forecasting
LLM Agent Based Renewable Energy Forecasting Using Edge and IoT Data A Review of Solar Wind Weather and Grid Aware Decision Support
May 24, 2026Pavan Manjunath, Thomas PrueferEnergy Forecasting Geomagnetic Storms
A Posterior-Predictive Variance Decomposition for Epistemic and Aleatoric Uncertainty in Wind Power Forecasting
May 21, 2026Yinsong Chen, Samson S. Yu, Kashem M. MuttaqiEnergy Forecasting Predictive Uncertainty
Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting
May 18, 2026Sumit Laha, Ankit Sharma, Hassan ForooshEnergy Forecasting Photovoltaic
ECTO: Exogenous-Conditioned Temporal Operator for Ultra-Short-Term Wind Power Forecasting
May 12, 2026Cao Yuan, Junjun WangEnergy Forecasting Weather Models
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferEnergy Forecasting Solar
AI and Open-data Driven Scalable Solar Power Profiling
May 4, 2026Shiliang Zhang, Sabita Maharjan, Damla TurgutPhotovoltaic Solar
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
Apr 27, 2026Ankan Basu, Jyotiraditya Roy, Aditya Datta +2Energy Forecasting Solar
FETS Benchmark: Foundation Models Outperform Dataset-specific Machine Learning in Energy Time Series Forecasting
Apr 24, 2026Marco Obermeier, Marco Pruckner, Florian Haselbeck +1Electricity Price Forecasting Time Series Foundation Models
Forecasting Solar Energy Using a Single Image
Apr 23, 2026Jeremy Klotz, Shree K. NayarSolar Energy Forecasting
Barrier-enforced multi-objective optimization for direct point and sharp interval forecasting
Apr 20, 2026Worachit Amnuaypongsa, Yotsapat Suparanonrat, Pana Wanitchollakit +1Energy Forecasting Predictive Uncertainty
Probabilistic Wind Power Forecasting with Tree-Based Machine Learning and Weather Ensembles
Feb 13, 2026Max Bruninx, Diederik van Binsbergen, Timothy Verstraeten +2Energy Forecasting Offshore Wind Turbine

Sep 22, 2026 · astro-ph.SRJ/K move · Enter open · S save

PROSWIN: Probabilistic Solar Wind Speed Forecasting Using Deep Distributional Regression From Solar Images

Daniel Collin, Yuri Shprits, Luca Chiarabini, Stefan J. Hofmeister+2

Accurately predicting fast solar wind conditions is challenging, as uncertainties are large and unquantified by traditional single-value prediction models. In particular, the risks of high-speed solar wind streams (HSSs), which can cause damage to technological infrastructure, cannot be reliably assessed without probabilistic forecasts. We present PROSWIN, a probabilistic machine learning model that forecasts the hourly solar wind speed (SWS) at Earth with a four-day lead time. The approach combines solar images and magnetograms using a deep neural network coupled to a distributional regression algorithm. Because standard error metrics underweight the relevance of HSS peaks, we further introduce the prediction score, a model-selection metric that jointly rewards timeline and HSS peak accuracy. On 14 years of data, our forecast achieves very well-calibrated uncertainties (<1% average deviation). Using the continuous ranked probability score (CRPS), a metric that assesses distributional accuracy, we obtain a timeline CRPS of 41.0 km/s, an HSS peak CRPS of 45.3 km/s, and a prediction score of 42.3 km/s. We find that the 171 Å channel is an important complement to the typically used 193 Å and 211 Å channels and that the prediction score for model selection improves the applicability of the model. Compared to selected models from the literature, ours is the only one that is accurate for both timeline and HSS peak values, rather than trading one off against the other. These results support the advantages of probabilistic over single-value solar wind models. The introduced methods are also transferable to other forecasting problems.