SolarBench: A global solar energy nowcasting benchmark

• URL: http://arxiv.org/abs/2609.06187v1
• Date: Sat, 05 Sep 2026 17:09:13 GMT
• Title: SolarBench: A global solar energy nowcasting benchmark
• Abstract summary: We introduce SolarBench, an open global benchmark for image-based solar nowcasting.<n>SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade.<n>We benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations.
• Score: 10.905771157487608
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As the share of solar power grows, nowcasting weather-driven solar variability becomes critical for reliable energy system operation. State-of-the-art approaches increasingly apply deep learning to sky camera and geostationary satellite observations, but fragmented datasets and inconsistent evaluation make it difficult to determine whether reported improvements generalize across climates, cloud regimes, and photovoltaic (PV) systems. Here we introduce SolarBench, an open global benchmark for image-based solar nowcasting. SolarBench harmonizes more than six million sky and satellite images from 11 diverse sites spanning a decade, together with irradiance or PV output and auxiliary atmospheric data. An accompanying toolbox supports reproducible data access, processing, model development, and evaluation. Using SolarBench, we benchmark representative models and reveal a gap between average forecasting accuracy and the ability to capture rapid solar fluctuations. We further quantify predictability across cloud regimes and demonstrate data-efficient adaptation to new PV systems. SolarBench provides an extensible foundation for fair comparison and methodological innovation in solar nowcasting.

Related papers

• AI and Open-data Driven Scalable Solar Power Profiling [48.72868465736126]
  This paper presents an open, scalable framework for detecting solar panels from open data and generating city-level solar power profiles.<n>We leverage foundation vision AI models to detect solar panel from open-source satellite imagery.<n>The framework reduces dependency on proprietary imagery, manual labeling, and closed-source models, and offers a transparent and scalable approach for solar planning and analysis.
  arXiv  Detail & Related papers  (2026-05-04T15:37:52Z)
• SolarTformer: A Transformer Based Deep Learning Approach for Short Term Solar Power Forecasting [0.0]
  "SolarTformer" is designed to predict solar power output from meteorological data.<n>Our experiments demonstrate that SolarTformer significantly outperforms previous models on the same data set.<n>These findings highlight the potential of attention-based architectures in enhancing the accuracy of solar forecasting.
  arXiv  Detail & Related papers  (2026-04-27T10:56:16Z)
• Ultra-short-term solar power forecasting by deep learning and data reconstruction [60.200987006598524]
  We propose a deep-learning based ultra-short-term solar power prediction with data reconstruction.<n>We employ deep-learning models to capture long- and short-term dependencies towards the target prediction period.
  arXiv  Detail & Related papers  (2025-09-21T14:22:35Z)
• Data-driven solar forecasting enables near-optimal economic decisions [38.70705528456187]
  We present SunCastNet, a lightweight data-driven forecasting system.<n>It provides 0.05$circ$, 10-minute resolution predictions of surface solar radiation downwards.<n>It reduces operational regret by 76--93% compared to robust decision making.<n>It enables up to five of ten high-emitting industrial sectors per region to cross the commercial viability threshold of 12% Internal Rate of Return.
  arXiv  Detail & Related papers  (2025-09-08T17:38:05Z)
• SolarFormer: Multi-scale Transformer for Solar PV Profiling [7.686020113962378]
  SolarFormer is designed to segment solar panels from aerial imagery, offering insights into their location and size. Our model leverages low-level features and incorporates an instance query mechanism to enhance the localization of solar PV installations. Our experiments consistently demonstrate that our model either matches or surpasses state-of-the-art models.
  arXiv  Detail & Related papers  (2023-10-30T22:22:01Z)
• Improving day-ahead Solar Irradiance Time Series Forecasting by Leveraging Spatio-Temporal Context [46.72071291175356]
  Solar power harbors immense potential in mitigating climate change by substantially reducing CO$_2$ emissions. However, the inherent variability of solar irradiance poses a significant challenge for seamlessly integrating solar power into the electrical grid. In this paper, we put forth a deep learning architecture designed to harnesstemporal context using satellite data.
  arXiv  Detail & Related papers  (2023-06-01T19:54:39Z)
• A Comparative Study on Generative Models for High Resolution Solar Observation Imaging [59.372588316558826]
  This work investigates capabilities of current state-of-the-art generative models to accurately capture the data distribution behind observed solar activity states. Using distributed training on supercomputers, we are able to train generative models for up to 1024x1024 resolution that produce high quality samples indistinguishable to human experts.
  arXiv  Detail & Related papers  (2023-04-14T14:40:32Z)
• Data-driven soiling detection in PV modules [58.6906336996604]
  We study the problem of estimating the soiling ratio in photo-voltaic (PV) modules. A key advantage of our algorithms is that they estimate soiling, without needing to train on labelled data. Our experimental evaluation shows that we significantly outperform current state-of-the-art methods for estimating soiling ratio.
  arXiv  Detail & Related papers  (2023-01-30T14:35:47Z)
• SKIPP'D: a SKy Images and Photovoltaic Power Generation Dataset for Short-term Solar Forecasting [0.0]
  There are few publicly available standardized benchmark datasets for image-based solar forecasting. We introduce SKIPP'D -- a SKy Images and Photovoltaic Power Generation dataset. The dataset contains quality-controlled down-sampled sky images and PV power generation data ready-to-use for short-term solar forecasting using deep learning.
  arXiv  Detail & Related papers  (2022-07-02T21:52:50Z)
• Short term solar energy prediction by machine learning algorithms [0.47791962198275073]
  We report daily prediction of solar energy by exploiting the strength of machine learning techniques. Forecast models of base line regressors including linear, ridge, lasso, decision tree, random forest and artificial neural networks have been implemented. It has been observed that improved accuracy is achieved through random forest and ridge regressor for both grid sizes.
  arXiv  Detail & Related papers  (2020-10-25T17:56:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.