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Found 9 papers, 4 papers with code
SkyGPT: Probabilistic Short-term Solar Forecasting Using Synthetic Sky Videos from Physics-constrained VideoGPT
Furthermore, we feed the generated future sky images from the video prediction models for 15-minute-ahead probabilistic solar forecasting for a 30-kW roof-top PV system, and compare it with an end-to-end deep learning baseline model SUNSET and a smart persistence model.
Open-Source Ground-based Sky Image Datasets for Very Short-term Solar Forecasting, Cloud Analysis and Modeling: A Comprehensive Survey
In this study, we present a comprehensive survey of open-source ground-based sky image datasets for very short-term solar forecasting (i. e., forecasting horizon less than 30 minutes), as well as related research areas which can potentially help improve solar forecasting methods, including cloud segmentation, cloud classification and cloud motion prediction.
Sky-image-based solar forecasting using deep learning with multi-location data: training models locally, globally or via transfer learning?
With more and more sky image datasets open sourced in recent years, the development of accurate and reliable deep learning-based solar forecasting methods has seen a huge growth in potential.
Omnivision forecasting: combining satellite observations with sky images for improved intra-hour solar energy predictions
In this study, we integrate these two complementary points of view on the cloud cover in a single machine learning framework to improve intra-hour (up to 60-min ahead) irradiance forecasting.
SPIN: Simplifying Polar Invariance for Neural networks Application to vision-based irradiance forecasting
Translational invariance induced by pooling operations is an inherent property of convolutional neural networks, which facilitates numerous computer vision tasks such as classification.
ECLIPSE : Envisioning CLoud Induced Perturbations in Solar Energy
Efficient integration of solar energy into the electricity mix depends on a reliable anticipation of its intermittency.
Benchmarking of Deep Learning Irradiance Forecasting Models from Sky Images -- an in-depth Analysis
A number of industrial applications, such as smart grids, power plant operation, hybrid system management or energy trading, could benefit from improved short-term solar forecasting, addressing the intermittent energy production from solar panels.
A Temporally Consistent Image-based Sun Tracking Algorithm for Solar Energy Forecasting Applications
Improving irradiance forecasting is critical to further increase the share of solar in the energy mix.
Convolutional Neural Networks applied to sky images for short-term solar irradiance forecasting
This work presents preliminary results on the application of deep Convolutional Neural Networks for 2 to 20 min irradiance forecasting using hemispherical sky images and exogenous variables.
