Solar Power Forecasting

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Solar power forecasting estimates future photovoltaic output or solar irradiance, helping grid operators and energy systems plan around weather-driven variability and fast ramps. Methods range from persistence and statistical or boosted-tree models to temporal neural networks, transformers, graph models, and physics-informed approaches; many combine power histories with numerical weather forecasts, satellite data, or sky images. Probabilistic methods, including quantile and conformal prediction, add calibrated uncertainty, while transfer learning and synthetic histories address sites with scarce data. A central challenge is generalizing across sites and weather regimes: average accuracy can hide failures during extremes, and results depend on forecast inputs, evaluation design, and the trade-off between accuracy, reliability, and computational cost.

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45 papers7 papers in the last four weeks, level with the four weeks before. 0.0% of all new papers.

Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks
Apr 15, 2026Mohammed Ezzaldin Babiker Abdullah, Rufaidah Abdallah Ibrahim MohammedSolar Irradiance Forecasting Solar Power Forecasting
Omdurman Islamic University, Omdurman, Sudan.
Thermodynamic Liquid Manifold Networks: Physics-Bounded Deep Learning for Solar Forecasting in Autonomous Off-Grid Microgrids
Apr 13, 2026Mohammed Ezzaldin Babiker AbdullahSolar Power Forecasting Physics-Informed ML
Department of Electrical and Electronic Engineering, Faculty of Engineering Sciences Omdurman Islamic University, Omdurman, Sudan
On-Meter Graph Machine Learning: A Case Study of PV Power Forecasting for Grid Edge Intelligence
Apr 10, 2026Jian Huang, Zixiang Ming, Yongli Zhu +1Edge ML Solar Power Forecasting
Sun Yat-sen University, Guangzhou, China
Solar-VLM: Multimodal Vision-Language Models for Augmented Solar Power Forecasting
Apr 5, 2026Hang Fan, Haoran Pei, Runze Liang +3Solar Power Forecasting Spatiotemporal Forecasting
School of Economic and Management, North China Electric Power University, 102206, Beijing, China · School of Control and Computer Engineering, North China Electric Power University, 102206, Beijing, China · Department of Electrical Engineering, Tsinghua University, 100084, Beijing, China +1
Characterization and forecasting of national-scale solar power ramp events
Mar 27, 2026Luca Lanzilao, Angela MeyerSolar Power Forecasting Power Systems
School of Engineering and Computer Science, Bern University of Applied Sciences, Quellgasse 21, Biel, 2501, Bern, Switzerland · Department of Geosciences and Remote Sensing, TU Delft, Stevinweg 1, Delft, 2628 CN, South-Holland, The Netherlands
Cloud-Edge Collaborative Large Models for Robust Photovoltaic Power Forecasting
Mar 21, 2026Nan Qiao, Shuning Wang, Sijing Duan +5Solar Power Forecasting Distribution Shift Robustness
School of Computer Science and Engineering, Central South University, Changsha, Hunan 410083, China · Department of Computer Science and Technology, BNRist, Tsinghua University, Beijing 100084, China · Beijing Smartchip Microelectronics Technology Co., Ltd, Beijing 102200, China
Deep Learning Multi-Horizon Irradiance Nowcasting: A Comparative Evaluation of Three Methods for Leveraging Sky Images
Mar 17, 2026Erling W. Eriksen, Magnus M. Nygård, Niklas Erdmann +1Solar Irradiance Forecasting Solar Power Forecasting
Institute for Energy Technology · University of Oslo Department of Technology Systems
Predictive Uncertainty in Short-Term PV Forecasting under Missing Data: A Multiple Imputation Approach
Mar 16, 2026Parastoo Pashmchi, Jérôme Benoit, Motonobu KanagawaSolar Power Forecasting Uncertainty Propagation
SAP Labs France, Mougins, France · EURECOM, Biot, France
Integrating Weather Foundation Model and Satellite to Enable Fine-Grained Solar Irradiance Forecasting
Mar 16, 2026Ziqing Ma, Kai Ying, Xinyue Gu +7Solar Irradiance Forecasting Solar Power Forecasting
DAMO Academy, Alibaba Group, Hangzhou, Zhejiang, China · College of Computer Science, Zhejiang University of Technology, Hangzhou, Zhejiang, China · DAMO Academy, Alibaba Group, Bellevue, USA
Two-Stage Photovoltaic Forecasting: Separating Weather Prediction from Plant-Characteristics
Mar 4, 2026Philipp Danner, Hermann de MeerSolar Power Forecasting Solar Irradiance Forecasting
University of Passau, Innstraße 41, Passau, Germany
FreeGNN: Continual Source-Free Graph Neural Network Adaptation for Renewable Energy Forecasting
Mar 2, 2026Abderaouf Bahi, Amel Ourici, Ibtissem Gasmi +3Source-Free Domain Adaptation Continual Test-Time Adaptation
Computer Science and Applied Mathematics Laboratory (LIMA), Faculty of Science and Technology, Chadli Bendjedid University, P.O. Box 73, El Tarf 36000, Algeria · Mathematical Modeling and Numerical Simulation Laboratory (LAM2SIN), Faculty of Technology, Badji Mokhtar University, P.O. Box 12, Annaba 23000, Algeria · College of Information Technology, United Arab Emirates University, Al Ain, United Arab Emirates
M3S-Net: Multimodal Feature Fusion Network Based on Multi-scale Data for Ultra-short-term PV Power Forecasting
Feb 23, 2026Penghui Niu, Taotao Cai, Suqi Zhang +4Solar Power Forecasting Multimodal Fusion
School of Artificial Intelligence, Hebei University of Technology, Tianjin 300401, China · University of Southern Queensland, Toowoomba 487-535, Australia · School of Information Engineering, Tianjin University of Commerce, Tianjin 300134, China +3
R $^2$ Energy: A Large-Scale Benchmark for Robust Renewable Energy Forecasting under Diverse and Extreme Conditions
Feb 17, 2026Zhi Sheng, Yuan Yuan, Guozhen Zhang +1Forecasting Benchmarks Time Series Forecasting
Center for Urban Science and Computation, Tsinghua University · Courant Institute of Mathematical Sciences, New York University
Meteorological data and Sky Images meets Neural Models for Photovoltaic Power Forecasting
Feb 17, 2026Ines Montoya-Espinagosa, Antonio AgudoSolar Power Forecasting Multivariate Time Series Forecasting
Universitat Pompeu Fabra, Barcelona, Spain. · Institut de Robòtica i Informàtica Industrial, CSIC-UPC, Barcelona, Spain.
CoCopula: Conformal Calibration and Copula Aggregation for Reliable Renewable Energy Forecasting
Jan 31, 2026Alireza Moradi, Mathieu Tanneau, Reza Zandehshahvar +1Probabilistic Forecasting Solar Power Forecasting
NSF Artificial Intelligence Institute for Advances in Optimization H. Milton Stewart School of Industrial and Systems Engineering Georgia Institute of Technology, Atlanta, GA, USA

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Outperforming Self-Attention Mechanisms in Solar Irradiance Forecasting via Physics-Guided Neural Networks

Mohammed Ezzaldin Babiker Abdullah, Rufaidah Abdallah Ibrahim Mohammed

Omdurman Islamic University, Omdurman, Sudan.

Solar Irradiance Forecasting Solar Power Forecasting Time Series Forecasting Physics-Informed ML Convolutional RNNs

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Accurate Global Horizontal Irradiance (GHI) forecasting is critical for grid stability, particularly in arid regions characterized by rapid aerosol fluctuations. While recent trends favor computationally expensive Transformer-based architectures, this paper challenges the prevailing "complexity-first" paradigm. We propose a lightweight, Physics-Informed Hybrid CNN-BiLSTM framework that prioritizes domain knowledge over architectural depth. The model integrates a Convolutional Neural Network (CNN) for spatial feature extraction with a Bi-Directional LSTM for capturing temporal dependencies. Unlike standard data-driven approaches, our model is explicitly guided by a vector of 15 engineered features including Clear-Sky indices and Solar Zenith Angle - rather than relying solely on raw historical data. Hyperparameters are rigorously tuned using Bayesian Optimization to ensure global optimality. Experimental validation using NASA POWER data in Sudan demonstrates that our physics-guided approach achieves a Root Mean Square Error (RMSE) of 19.53 W/m^2, significantly outperforming complex attention-based baselines (RMSE 30.64 W/m^2). These results confirm a "Complexity Paradox": in high-noise meteorological tasks, explicit physical constraints offer a more efficient and accurate alternative to self-attention mechanisms. The findings advocate for a shift towards hybrid, physics-aware AI for real-time renewable energy management.