Solar Radiation and Photovoltaics

Latest papers

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

  Xu Yuqing, Zhou Liguo, Sun Ze, Yu Lei, Jiang Mingming · 30 September 2026

  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 …

• When Does Advection-Aware Graph Nowcasting Help? A Controlled Study of Distributed Solar Ramp Forecasting with a Self-Supervised Cloud-Motion Estimator

  Phillip Jiang · 28 September 2026

  Short-term forecasting of cloud-induced power ramps across a network of distributed photovoltaic (PV) or irradiance sensors is a recognised pain point for grid operators. A natural idea is to make the graph neural network (GNN) advection-aware: connect each site to the sites upwind of it, with edge …

• Transfer Learning with Conformalized Quantile Regression for Solar PV Forecasting Under Load-Shedding-Driven Data Scarcity

  Rakib Abdullah, K. M. Tahlil Mahfuz Faruk · 24 September 2026

  Solar photovoltaic (PV) forecasting in regions affected by load shedding is challenging because reliable historical observations are scarce. This study proposes a transfer learning framework combined with Conformalized Quantile Regression (CQR) to improve PV power forecasting and provide reliable un…

• SolarFlowRefiner: Refinement-Aware Flow Matching for Surface Solar Radiation Downscaling

  Udbhav Srivastava, Antonita Racheal, Yiheng Chen, Runlong Yu, Xinyue Ye · 22 September 2026

  High-resolution surface solar radiation (SSR) is important for solar forecasting and grid operation. However, physically consistent reanalysis products are too coarse to resolve localized cloud-driven variability. In this paper, we study a multisource downscaling task that reconstructs high-resoluti…

• Nationally Consistent, Locally Incomplete: A Bayesian Remote-Sensing Audit of Rooftop Photovoltaic Registries

  Gabriel Kasmi, Yves-Marie Saint-Drenan, Laurent Dubus, Philippe Blanc · 16 September 2026

  Tracking the energy transition requires reliable statistics on renewable deployment. Rooftop photovoltaics (PV) are especially hard to track, owing to their decentralised nature, and the resulting inaccuracies in official statistics are known but not quantified. Remote sensing offers an independent …

• Ensemble Complexity in Photovoltaic Forecasting

  Sun Ze, Zhou Liguo, Xu Yuqing, Yu Lei, Jiang Mingming · 15 September 2026

  An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation. We assess these effects through matched comparisons and ablations of a fixed heterogeneous predictor bank. Hourly experiments use GEFCom2014 and three additional public datasets, wi…

• Horizon-specific Expert Fusion for Photovoltaic Power Forecasting

  Xu Yuqing, Zhou Liguo, Sun Ze, Yu Lei, Jiang Mingming · 15 September 2026

  Short-term photovoltaic power forecasting requires models to represent regular solar cycles and weather-driven fluctuations whose importance changes with the forecast horizon. This study develops a hierarchical ensemble that combines temporal neural models, historical analogs, state climatology, and…

• Solar Intelligence

  Jyotsna Singh · 15 September 2026

  Solar energy decision support is fragmented across dashboards that provide data without explanation, research papers are slow to parse, and general-purpose language models are not solar domain specific and answer without evidence. This paper introduces Solar Intelligence, a hybrid retrieval-augmente…

• Bidirectional Multimodal Fusion of Sky Images and Time-Series for Solar Forecasting with Large Language Models

  Ken Chen, Maneesha Perera, Wei Wang, Sachith Seneviratne, Hansani Weeratunge, Saman Halgamuge · 11 September 2026

  Short-term photovoltaic (PV) power and global horizontal irradiance (GHI) forecasts are essential for effective dispatch, reserve scheduling, and grid operations. At these forecasting horizons, errors are predominantly driven by cloud induced ramps: relying solely on historical numerical data may st…

• Energy Yield and Lifetime Climate Classification via Machine Learning for Optimizing Photovoltaic Module Design and Materials

  Youri Blom, Sofia Dutto, Alexandru Costache, Rowan Richie, Ruben Pelsser, Wesley Berger, Jing Sun, Rudi Santbergen, Olindo Isabella, Malte Ruben Vogt · 27 August 2026

  To resiliently and sustainably meet our future energy demand, photovoltaic (PV) modules must be deployed across a broad and diverse range of geographical regions with varying operating conditions. As these conditions strongly affect both performance and optimal system design, a dedicated PV-specific…

• FarSky: Task-Aware Latent-Space Coupling for Generative Intra-Hour Solar Forecasting

  Yann Fabel, Bijan Nouri, Milon Miah, Niklas Blum, Luis F. Zarzalejo, Julia Kowalski, Robert Pitz-Paal · 13 August 2026

  Accurate solar irradiance forecasting is essential for the reliable integration of photovoltaic power into modern electricity grids. All-sky imagers (ASI) provide high-resolution observations of clouds, making them well suited for intra-hour forecasting. Recent deep learning approaches have substant…

• Evaluating Semantic and Spatial Guidance for Foundation Model Segmentation of Small-Scale PV in Remote Sensing Imagery

  Roni Blushtein-Livnon, Tal Svoray, Osher Rafaeli, Michael Dorman, Itay Fischhendler, Havazelet Yahel, Emir Galilee · 12 August 2026

  Spatio-temporal PV data are essential for understanding adoption processes in off-grid regions, yet such data remain largely unavailable. Automated segmentation of remote sensing (RS) imagery offers a promising solution; yet, residential PV systems remain challenging targets because of their small s…

• An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting

  Fariba Dehghan, Sebastian Stein, Vahid Yazdanpanah, Stephanie Gauthier, Masood Nazari · 4 August 2026

  Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records. This makes standard day-ahead forecasting difficult: persistence and physical baselines can be sensitive to calibration and timestamp alignment, while single m…

• OpenPVMapper: A Multi-source, Nationwide Database of Rooftop Photovoltaic Systems in France

  Gabiel Kasmi · 29 July 2026

  Rooftop photovoltaic (PV) systems account for the vast majority of PV grid connections, yet no open, comprehensive, installation-level dataset of these systems exists: public registries aggregate data only above a capacity threshold, and remote sensing-based detection efforts, while extensive, are t…

• A Modern ConvNet for Solar Filament Detection

  J. R. Hu, Q. Hao, Z. Zheng, P. F. Chen, C. Li, Y. Meng · 28 July 2026

  Automated solar filament detection using deep learning faces several challenges. Semantic segmentation of solar filaments is a complicated multiscale feature extraction task with long-tail distribution. Furthermore, a large-scale, highly complete, and finely detailed dataset has become mandatory for…

• A Controlled Visual-Backbone Benchmark for Multimodal Short-Term Solar Irradiance Forecasting

  Oshadha Samarakoon, Dushan Herath, Ishara Ranmandala, Dilshara Herath, Roshan Godaliyadda, Parakrama Ekanayake, Vijitha Herath · 28 July 2026

  Sky-image irradiance studies often compare forecasting systems in which the image encoder, temporal model, fusion block, target definition, and training recipe all change together. We use a narrower protocol: the multimodal forecasting pipeline is fixed, and only the visual backbone is varied. The s…

• Distributed solar generation forecasting using attention-based deep neural networks for cloud movement prediction

  Maneesha Perera, Julian De Hoog, Kasun Bandara, Hansani Weeratunge, Saman Halgamuge · 21 July 2026

  Accurate forecasts of distributed solar generation are necessary to maintain grid stability amid the increased uptake of distributed solar photovoltaic (PV) systems. However, the high variability of solar generation over short time intervals (seconds to minutes) caused by cloud movement makes this f…

• Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis

  Dandan Chen, Yan Zhao, Xuepeng Chen · 15 July 2026

  Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state depen…

• Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting

  Lorenzo Longarini, Alessandro Rongoni, Simone Silenzi, Emanuele Frontoni, Riccardo Rosati · 8 June 2026

  At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters. This cold-start setting is addressed with a zero-shot pipeline that generates a synthetic production history from pla…

• Step-adaptive multimodal fusion network with multi-scale cloud feature learning for ultra-short-term solar irradiance forecasting

  Jingxin Zhang Xiaoqin Wang · 5 June 2026

  Ultra-short-term solar irradiance prediction is critical for photovoltaic system dispatch and power grid stability. Existing approaches suffer from three key shortcomings: single time-series models cannot capture the spatial dynamics of clouds under complex conditions, standard convolutions inadequa…

• MATNet: Multi-Level Fusion Transformer-Based Model for Day-Ahead PV Generation Forecasting

  Matteo Tortora, Francesco Conte, Gianluca Natrella, Paolo Soda · 29 May 2026

  Accurate forecasting of renewable generation is crucial to facilitate the integration of Renewable Energy Sources into the power system. Focusing on photovoltaic (PV) units, forecasting methods can be divided into two main categories: physics-based and data-based strategies, with Artificial Intellig…

• Inpainting-Style Conditional Diffusion for Multivariable Time Series Forecasting

  Kourosh Kiani, S. M. Muyeen · 28 May 2026

  In this paper, we propose a novel conditional diffusion-based framework for multivariable time-series solar power forecasting. The proposed method reformulates temporal PV data as structured two-dimensional representations (images) using a sliding-window patch construction, enabling the application …

• Learning Long-Term Temporal Dependencies in Photovoltaic Power Output Prediction Through Multi-Horizon Forecasting

  Sumit Laha, Ankit Sharma, Hassan Foroosh · 20 May 2026

  The rapid global expansion of solar photovoltaic (PV) capacity-reaching a record 597 GW in 2024-highlights the urgent need for robust forecasting models to mitigate the grid instability caused by the intermittent nature of solar irradiance. While deep learning-based direct forecasting using ground-b…

• A Quantum Inspired Variational Kernel and Explainable AI Framework for Cross Region Solar and Wind Energy Forecasting

  Pavan Manjunath, Thomas Prufer · 12 May 2026

  Reliable short horizon forecasting of solar and wind generation is a structural prerequisite of any modern power system yet most published forecasters are tuned and evaluated on a single climatic regime and most algorithmic novelty has been concentrated either on classical recurrent networks or on m…

• AI and Open-data Driven Scalable Solar Power Profiling

  Shiliang Zhang, Sabita Maharjan, Damla Turgut · 6 May 2026

  Solar photovoltaic (PV) deployment is expanding rapidly, yet detailed, up-to-date information on the spatial distribution and capacity of rooftop PV remains limited. This paper presents an open, scalable framework for detecting solar panels from open data and generating city-level solar power profil…