This repository presents an easy-to-implement yet effective algorithm for detecting clouds in ground-based sky images. The algorithm has been used in the study by Nie et al. (2020) [1] to identify cloud pixels in sky images for sky condition classification, which enables the development of tailored models for solar nowcasting specific to each sky condition. Here, we demonstrate the effectiveness of our algorithm using some sample data. The full dataset can be assessed via our SKIPP'D GitHub Repository.
Clouds can significantly impact PV panel output and pose challenges to the large-scale deployment of solar PV systems. Accurate detection of clouds is a critical step in cloud modeling, which is essential for reliable solar power forecasting.
Figure 1. Power output of a 30-kW roof-top PV system on a partly cloudy day. Note how PV output fluctuates during the cloud events.
Cloud detection algorithms essentially perform binary classification to distinguish each pixel in a sky image as either a cloud or non-cloud pixel. Various methods have been developed for this purpose, ranging from simple thresholding techniques to more complex approaches such as neural network models, Markov Random Fields models, and Gaussian Mixture Models [2].
We use the thresholding method here, favored for its simplicity (no demanding image labeling involved like machine/deep learning methods), low computational requirements (no GPU required), and capability to be implemented near real time. Our algorithm specifically integrates the normalized red blue ratio (NRBR) and clear sky library (CSL) methods. To better understand our proposed approach, let's first introduce NRBR and CSL.
Applying a
Figure 2. Cloud detection using NRBR method for different sky conditions: (a) sun entirely shrouded by clouds; (b) clear sky; and (c) sun partially shrouded by clouds. The original sky image is on the left, and the image showing identified cloud pixels is on the right. Red pixels in original image indicates the sun position. Green pixels in the image indicates the identified cloud pixels. Note the misclassification of the pixels in the circumsolar area for scenario (b) and (c).
Chauvin et al. [2] apply a background subtraction using a clear sky image to tackle the issue of misclassifying pixels in the circumsolar area. It sets a threshold to the difference of
We adopt this idea, but instead of generating clear sky image from the original image, we establish a clear sky library (CSL) by sampling 4 clear sky days from our dataset, i.e., May 20, August 15, September 23 and October 22 of 2017, with 5-minute frequency. These days are found to be sufficient for representing the track of the sun over a year 1. For background subtraction, we match the original image with a clear sky image from the CSL with a similar sun position. The sun position identification is a separate algorithm developed in this study based on camera projection model and the sun angle, which is detailed in the following subsection.
The difference of
The CSL method can handle the circumsolar area misclassification well for the clear sky condition. However, the method fails when there are clouds near the circumsolar area or when the sun is shrouded by clouds, as the circumsolar area is subtracted out from the clear sky image (see Figure 3 below). The cloud pixels in the circumsolar area are not identified as cloud pixels since the
Figure 3. Cloud detection using CSL method for different sky conditions: (a) sun entirely shrouded by clouds; (b) clear sky; and (c) sun partially shrouded by clouds. The original sky image is on the left, and the image showing identified cloud pixels is on the right. Red pixels in original image indicates the sun position. Green pixels in the image indicates the identified cloud pixels. Note that although clear sky condition is handled correctly, the circumsolar area is inadvertently substracted out when the sun is entirely obscured by clouds.
To correct this flaw of using NRBR and CSL indiviudally, we develop an algorithm that takes advantages of the two methods (referred to as NRBR+CSL hereafter). The process diagram of the NRBR+CSL method is shown in Figure 4 below.
Figure 4. The process flow of our proposed cloud detection algorithm. The algorithm makes use of both NRBR and CSL.
The method uses filters added to the output cloudiness of the CSL method. Specifically, if the cloudiness obtained is lower than a threshold value 0.045, CSL method is used. If it is higher than another threshold value 0.35, then we switch to the NRBR method. If it is in between these two thresholds, then we choose the NRBR method, but only apply it outside of the circumsolar area in the original image. The examples of results from NRBR+CSL method can be found in Figure 5, which show the effectiveness of the method for cloud detection.
Figure 5. Cloud detection using NRBR+CSL method for different sky conditions: (a) sun entirely shrouded by clouds; (b) clear sky; and (c) sun partially shrouded by clouds. The original sky image is on the left, and the image showing identified cloud pixels is on the right. Red pixels in original image indicates the sun position. Green pixels in the image indicates the identified cloud pixels. Note all three sky conditions are handled nicely by the proposed NRBR+CSL method.
A sun position identification algorithm is developed for use in the cloud detection. The major steps of the sun position identification algorithm is shown in Figure 6 below. Polar coordinates
Figure 6. Algorithm for identifying the sun position in a sky image.
The Python codes for the sun position identification (sun_position_identification.py) and cloud detection (cloud_detection.py) algorithms are provided in the codes directory. In the same folder, the notebook file (demo.ipynb) provides guidelines on how to use the functions within the two Python code files. All dependencies are listed in the file requirements.txt.
As our cloud detection method is non-parametric, no image labels are required. For demonstration purposes, we select sky images from 3 sunny days and 3 cloudys in 2017 with 1-minute temporal resolution 2, to validate the sun position identification and the cloud detection algorithms. The image data are from our curated sky image and PV power generation dataset SKIPP'D (Dataset paper and Github repo). Data for validating the algorithms are provided in the sample_data directory. A description of the data files are provided in the table below.
| File | Description |
|---|---|
sky_images_sunny.npy |
Python NumPy array consists of 3 days of sunny sky image data. |
time_stamps_sunny.npy |
Python NumPy array of the time stamps corresponding to the sunny sky image data. |
sky_images_cloudy.npy |
Python NumPy array consists of 3 days of cloudy sky image data. |
time_stamps_cloudy.npy |
Python NumPy array of the time stamps corresponding to the cloudy sky image data. |
clear_sky_library/csl_images.npy |
Python NumPy array of 4 days of clear sky image data contained in the clear sky libaray (for use in CSL method). |
clear_sky_library/csl_times.npy |
Python NumPy array of time stamps corresponding to the clear sky images. |
clear_sky_library/csl_sun_center.npy |
Python NumPy array of the sun position coordinates in formate of (X,Y) corresponding to the clear sky images. |
The videos below show case our sun position identification algorithm and cloud detection algorithm. Here we show only one sunny days and one cloudy days. Feel free to try more days with our data.
cloud_detection_ts_sunny.mp4
cloud_detection_ts_cloudy.mp4
Although our algorithm is simple, it shows fairly good performance in various sky conditions. However, there are a few limitations need to be noted.
First, the algorithm could struggle when there are thin/wispy clouds present in the sky image. Such clouds are naturally hard to be recognized by the proposed algorithm.
Second, if there are small amount of clouds in the sky image and these clouds are mostly present within the circumsolar region, given the CSL method applied by our alogrithm, these pixels will be subtracted out and not be recognized as the cloud pixels.
Besides, the algorithm is not self-adaptive. Parameters used in the algorithm need to be updated when a new set of images from a different sky camera is used. These parameters include but not limited to the geo-location paramters for sun position identification, the sky camera orientation, and different thresholding parameters used in cloud detection.
For future work, the generalization of the proposed cloud detection algorithm could be validated using other sky image datasets. Additionally, according to our review study [6], there are increasing open-source sky image datasets available in recent years, and some of them provide segmentation map of sky images. Deep learning models generally achieve superior performance over other methods [7]. The bottleneck is the availability of the labeled data. With large amount of diverse sky image samples available, training deep learning models become a viable option.
If you find this work useful to your research, please cite:
Nie, Y., Sun, Y., Chen, Y., Orsini, R., & Brandt, A. (2020). PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model. Journal of Renewable and Sustainable Energy, 12(4).
or
@article{nie2020pv,
title={PV power output prediction from sky images using convolutional neural network: The comparison of sky-condition-specific sub-models and an end-to-end model},
author={Nie, Yuhao and Sun, Yuchi and Chen, Yuanlei and Orsini, Rachel and Brandt, Adam},
journal={Journal of Renewable and Sustainable Energy},
volume={12},
number={4},
year={2020},
publisher={AIP Publishing}
}
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[2] Chauvin, R., Nou, J., Thil, S., Traore, A. and Grieu, S., 2015. Cloud detection methodology based on a sky-imaging system. Energy Procedia, 69, pp.1970-1980.
[3] Bohren, C.F. and Huffman, D.R., 2008. Absorption and scattering of light by small particles. John Wiley & Sons.
[4] Garcia-Gil, G. and Ramirez, J.M., 2019. Fish-eye camera and image processing for commanding a solar tracker. Heliyon, 5(3).
[5] Da Rosa, A.V. and Ordóñez, J.C., 2021. Fundamentals of renewable energy processes. Academic Press.
[6] Nie, Y., Li, X., Paletta, Q., Aragon, M., Scott, A. and Brandt, A., 2024. Open-source sky image datasets for solar forecasting with deep learning: A comprehensive survey. Renewable and Sustainable Energy Reviews, 189, p.113977.
[7] Hasenbalg, M., Kuhn, P., Wilbert, S., Nouri, B. and Kazantzidis, A., 2020. Benchmarking of six cloud segmentation algorithms for ground-based all-sky imagers. Solar Energy, 201, pp.596-614.