A review on physical and data-driven based nowcasting methods using sky
images
- URL: http://arxiv.org/abs/2105.02959v1
- Date: Wed, 28 Apr 2021 10:20:52 GMT
- Title: A review on physical and data-driven based nowcasting methods using sky
images
- Authors: Ekanki Sharma and Wilfried Elmenreich
- Abstract summary: We present a review on short-term intra-hour solar prediction techniques known as nowcasting methods using sky images.
We also report and discuss which sky image features are significant for the nowcasting methods.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Amongst all the renewable energy resources (RES), solar is the most popular
form of energy source and is of particular interest for its widely integration
into the power grid. However, due to the intermittent nature of solar source,
it is of the greatest significance to forecast solar irradiance to ensure
uninterrupted and reliable power supply to serve the energy demand. There are
several approaches to perform solar irradiance forecasting, for instance
satellite-based methods, sky image-based methods, machine learning-based
methods, and numerical weather prediction-based methods. In this paper, we
present a review on short-term intra-hour solar prediction techniques known as
nowcasting methods using sky images. Along with this, we also report and
discuss which sky image features are significant for the nowcasting methods.
Related papers
- Computational Imaging for Long-Term Prediction of Solar Irradiance [14.339647548237838]
Real-time forecasting of cloud movement is necessary to schedule and allocate energy across grid-connected photovoltaic systems.
Previous works monitored cloud movement using wide-angle field of view imagery of the sky.
We design and deploy a catadioptric system that delivers wide-angle spatial resolution of the sky over its field of view.
arXiv Detail & Related papers (2024-09-18T14:29:43Z) - Towards an end-to-end artificial intelligence driven global weather forecasting system [57.5191940978886]
We present an AI-based data assimilation model, i.e., Adas, for global weather variables.
We demonstrate that Adas can assimilate global observations to produce high-quality analysis, enabling the system operate stably for long term.
We are the first to apply the methods to real-world scenarios, which is more challenging and has considerable practical application potential.
arXiv Detail & Related papers (2023-12-18T09:05:28Z) - Foundation Models for Weather and Climate Data Understanding: A
Comprehensive Survey [39.08108001903514]
We offer an exhaustive, timely overview of state-of-the-art AI methodologies specifically engineered for weather and climate data.
Our primary coverage encompasses four critical aspects: types of weather and climate data, principal model, model scopes and applications, and datasets for weather and climate.
arXiv Detail & Related papers (2023-12-05T01:10:54Z) - Sky Imager-Based Forecast of Solar Irradiance Using Machine Learning [0.41248472494152805]
This paper presents a new approach to estimate short-term solar irradiance from sky images.
The proposed algorithm extracts features from sky images and use learning-based techniques to estimate the solar irradiance.
Theperformance of proposed machine learning (ML) algorithm is evaluated using two publicly available datasets of sky images.
arXiv Detail & Related papers (2023-10-26T12:44:45Z) - Benchmarks and Custom Package for Energy Forecasting [55.460452605056894]
Energy forecasting aims to minimize the cost of subsequent tasks such as power grid dispatch.
In this paper, we collected large-scale load datasets and released a new renewable energy dataset.
We conducted extensive experiments with 21 forecasting methods in these energy datasets at different levels under 11 evaluation metrics.
arXiv Detail & Related papers (2023-07-14T06:50:02Z) - Counting Crowds in Bad Weather [68.50690406143173]
We propose a method for robust crowd counting in adverse weather scenarios.
Our model learns effective features and adaptive queries to account for large appearance variations.
Experimental results show that the proposed algorithm is effective in counting crowds under different weather types on benchmark datasets.
arXiv Detail & Related papers (2023-06-02T00:00:09Z) - 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) - 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) - A Temporally Consistent Image-based Sun Tracking Algorithm for Solar
Energy Forecasting Applications [0.0]
This study introduces an image-based Sun tracking algorithm to localise the Sun in the image when it is visible and interpolate its daily trajectory from past observations.
Experimental results show that the proposed method provides robust smooth Sun trajectories with a mean absolute error below 1% of the image size.
arXiv Detail & Related papers (2020-12-02T09:59:45Z)
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.