Solar Power Forecasting

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5 papers in the last four weeks, with none the four weeks before. 0.0% of all new papers.

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State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting
Sep 29, 2026Xu Yuqing, Zhou Liguo, Sun Ze +2Time Series Forecasting Solar Power Forecasting
Huaibei Normal University, China
Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity
Sep 22, 2026Rakib Abdullah, K. M. Tahlil Mahfuz FarukSolar Power Forecasting Transfer Learning
Department of AI and Data Science Green University of Bangladesh
Ensemble Complexity in Photovoltaic Forecasting
Sep 14, 2026Sun Ze, Zhou Liguo, Xu Yuqing +2Ensemble Learning Solar Power Forecasting
aSchool of Computer Science and Technology, Huaibei Normal University, China
Horizon-specific Expert Fusion for Photovoltaic Power Forecasting
Sep 14, 2026Xu Yuqing, Zhou Liguo, Sun Ze +2Ensemble Learning Solar Power Forecasting
School of Computer Science and Technology, Huaibei Normal University, China
Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models
Sep 11, 2026Ken Chen, Maneesha Perera, Wei Wang +3Multivariate Time Series Forecasting Multimodal Large Language Models
Department of Mechanical Engineering, The University of Melbourne, Melbourne, Australia · Department of Mechanical Engineering, Sri Lanka Institute of Information Technology, Sri Lanka
FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting
Aug 6, 2026Yann Fabel, Bijan Nouri, Milon Miah +4Time Series Forecasting Solar Power Forecasting
1DLR, Institute of Solar Research · 2RWTH Aachen University · 3CIEMAT, Energy Department, Renewable Energy Division
An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting
Aug 3, 2026Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah +2Ensemble Learning Time Series Forecasting
University of Southampton
A Controlled Visual-Backbone Benchmark for Multimodal Short-Term Solar Irradiance Forecasting
Jul 26, 2026Oshadha Samarakoon, Dushan Herath, Ishara Ranmandala +4Vision Transformer Time Series Forecasting
Dept. of EEE, University of Peradeniya · Peradeniya, Sri Lanka · MARC, University of Peradeniya
Improving Wind and Solar Power Prediction with Efficient Wrapper-based Feature Selection: An Empirical Study
Jul 15, 2026Daniel Grillmeyer, Marius Hadry, Michael Stenger +3Solar Power Forecasting Feature Selection
University of Würzburg, Würzburg, Germany · Baden-Wuerttemberg Cooperative State University Mosbach, Mosbach, Germany
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 ChenTime Series Forecasting Solar Power Forecasting
State Key Laboratory of Severe Weather Meteorological Science and Technology, Chinese Academy of Meteorological Sciences, No. 46 Zhongguancun South Street, Beijing, 100081, China · Key Laboratory of High Impact Weather (special), China Meteorological Administration, Changsha, 410073, China
Climate-Invariant Conformal Prediction Intervals for Multi-Horizon Solar and Wind Forecasting
Jul 13, 2026Shreedhar Gangwar, Abhinav Bains, Banalaxmi BrahmaGroup-Conditional Conformal Prediction Time Series Forecasting
B. R. Ambedkar National Institute of Technology, Jalandhar, India · Department of Computer Science and Engineering, Dr. B.R. Ambedkar National Institute of Technology Jalandhar, India
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 +3Time Series Forecasting Non-Stationary Time Series Forecasting
School of Economics and Management, North China Electric Power University, 102206, Beijing, China · School of Electrical and Electronic Engineering, Nanyang Technological University, 639798, 50 Nanyang Avenue, Singapore · Department of Electrical Engineering, Tsinghua University, 100084, Beijing, China
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
Jun 5, 2026Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi +2Zero-Shot Time Series Forecasting Time Series Forecasting
Sistemi 2000 srl, Civitanova Marche, Italy · Department of Engineering ”Enzo Ferrari”, University of Modena and Reggio Emilia, Modena, Italy · Department of Political Sciences, Communication and International Relations, University of Macerata, Macerata, Italy
Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting
Jun 4, 2026Jingxin Zhang Xiaoqin WangTime Series Forecasting Multi-Scale Feature Fusion
School of Automation, Southeast University, Nanjing, 210096, China
Decision-focused learning for optimal PV-Battery scheduling
May 27, 2026Joris Depoortere, Hussain Kazmi, Johan DriesenSequential Decision Making Time Series Forecasting
ESAT-Electa KU Leuven, Kasteelpark Arenberg 10, Leuven, 3001, Belgium
Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting
May 27, 2026Kourosh Kiani, S. M. MuyeenMultivariate Time Series Forecasting Time Series Forecasting
1Electrical and Computer Engineering, Semnan University, Semnan, Iran · Electrical Engineering Department, Qatar University, Doha, Qatar
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 PrueferTime Series Forecasting LLM-Based Time Series Forecasting
Independent Researcher
Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting
May 18, 2026Sumit Laha, Ankit Sharma, Hassan ForooshMultivariate Time Series Forecasting Time Series Forecasting
Department of Computer Science University of Central Florida Orlando, Florida, United States.
A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting
May 9, 2026Pavan Manjunath, Thomas PruferTime Series Forecasting Quantum-Inspired ML
Independent Research Scholar, Germany
SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting
Apr 27, 2026Ankan Basu, Jyotiraditya Roy, Aditya Datta +2Multivariate Time Series Forecasting Time Series Forecasting
Positional Astronomy Centre, India Meteorological Department, Kolkata, India · Department of Computer Science and Engineering, Jadavpur University, Kolkata, India · Department of Computer Science and Engineering (Artificial Intelligence and Machine Learning), Heritage Institute of Technology, Kolkata, India +2
Forecasting Solar Energy Using a Single Image
Apr 23, 2026Jeremy Klotz, Shree K. NayarSolar Power Forecasting
Computer Science Department, Columbia University, 500 West 120th St., New York, 10027, NY, USA

Sep 29, 2026, cs.LGJ/K move · Enter open · S save

State Transport Routing for Short-horizon Adaptation in Multi-horizon Photovoltaic Forecasting

Xu Yuqing, Zhou Liguo, Sun Ze, Yu Lei+1

Huaibei Normal University, China

Time Series Forecasting Solar Power Forecasting

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Recent power measurements provide valuable information for photovoltaic(PV) power forecasting, but directly extrapolating short-term trends can introduce substantial errors over longer forecast horizons. To address this challenge, we propose state transport routing (STR), a lightweight adapter that refines the predictions of a frozen forecasting model. STR combines the original forecast with two complementary trajectories derived from the latest measured power level and its recent trend. A horizon-conditioned router adjusts their contributions over the first 120 min, while leaving subsequent predictions unchanged. Experiments on four public PV datasets show that STR consistently outperforms a parameter-matched residual adapter. On PVDAQ, the same approach improves five neural forecasting backbones, reducing all-horizon normalized mean absolute error by 0.0201-0.2364 percentage points, with paired 95% confidence intervals excluding zero. No reliable improvement is observed for LightGBM. These findings demonstrate the potential of structured state adaptation to improve short-term forecasting across different neural architectures without retraining the underlying models or altering their longer-horizon predictions.

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