A Smart Image-Based Cloud Detection System for IntrahourSolar Irradiance Forecasts

YINGHAO CHU, HUGO T. C. PEDRO, LUKAS NONNENMACHER, RICH H. INMAN,ZHOUYI LIAO, AND CARLOS F. M. COIMBRA

Department of Mechanical and Aerospace Engineering, Jacobs School of Engineering, and Center for

Renewable Resource Integration, and Center for Energy Research, University of California, San Diego, La Jolla, California

(Manuscript received 3 October 2013, in final form 27 May 2014)

ABSTRACT

This study proposes an automatic smart adaptive cloud identification (SACI) system for sky imagery and

solar irradiance forecast. The system is deployed using off-the-shelf fish-eye cameras that offer substantial

advantages in terms of cost when compared to industry-standard sky imagers. SACI uses a smart image

categorization (SIC) algorithm that combines the sky images and solar irradiance measurements to classify

sky conditions into three categories: clear, overcast, and partly cloudy. A cloud detection scheme, optimized

for each image category, is used to quantify cloud cover from the sky images. SACI is optimized and validated

against manually annotated images. Results show that SACI achieves overall classification accuracy higher

than 90% and outperforms reference cloud detection methods. Cloud cover retrieved by SACI is used as an

input for an artificial neural network (ANN) model that predicts 1-min average global horizontal irradiance

(GHI), 5-, 10-, and 15-min ahead of time. The performance of theANN forecasting model is assessed in terms

of common error statistics (mean bias and root-mean-square error) and in terms of forecasting skill over

persistence. The model proposed in this work achieves forecasting skills above 14%, 18%, and 19% over the

persistence forecast for 5-, 10-, and 15-min forecasts, respectively.

1. Introduction

The rapid increase in electric power production from

solar irradiation in recent times dictated a growing need

for accurate tools to forecast solar irradiance and the

power output of solar farms (Inman et al. 2013). Given

that cloud cover is the most important factor that de-

termines the amount of solar irradiance at ground level,

forecasting tools for solar and power output depend on

good characterizations of cloud cover (cloud opacity,

cloud motion, etc.). Forecasting models for horizons

ranging from 30min to several hours often use cloud in-

formation retrieved from satellite images (Rossow and

Schiffer 1991; Zhao and Di Girolamo 2006; Marquez et al.

2013a).However, satellite images currently available [such

as images from National Oceanic and Atmospheric Ad-

ministration (NOAA)’s Geostationary Operational Envi-

ronmental Satellite] have limited spatial and temporal

resolutions that prevent their use for intrahour forecast

(Ghonima et al. 2012; Marquez and Coimbra 2013). To

overcome this issue, ground-based sky imagers have been

developed and deployed to capture local high-resolution

sky images at high temporal resolutions, thus enabling

intrahour irradiance and power output forecasts (Voss and

Zibordi 1989; Souza-Echer et al. 2006; Long et al. 2006;

Seiz et al. 2007; Calbo and Sabburg 2008; Cazorla et al.

2008; Chow et al. 2011; Marquez et al. 2013a,b; Marquez

and Coimbra 2013; Chu et al. 2013).

In general, images captured by these systems are recor-

ded as red–green–blue (RGB) images. On these images,

cloud pixels have different RGB intensities. Based on this

fact, many automatic cloud identification algorithms

(Souza-Echer et al. 2006; Long et al. 2006; Yang et al.

2009; Heinle et al. 2010; Mantelli Neto et al. 2010; Li

et al. 2011; Ghonima et al. 2012) have been developed to

distinguish cloud from sky pixels. The accuracy of these

algorithms can exceed 90%, depending on cloud cover

type. However, most commercial sky imagers are costly

and incorporate shading systems to block direct sunlight

from the sensors. This increases the sensor lifetime but

Corresponding author address:Carlos F.M. Coimbra, University

of California, San Diego, 9500 Gilman Drive, La Jolla, CA 92093.

E-mail: ccoimbra@ucsd.edu

SEPTEMBER 2014 CHU ET AL . 1995

DOI: 10.1175/JTECH-D-13-00209.1

� 2014 American Meteorological Society

can reduce greatly the ability to track clouds near the

sun (depending on the shading).

This study uses images obtained with a generic off-the-

shelf, high-resolution fish-eye domenetwork camera. Based

on availability, price, specifications, and quality, we chose

a Vivotek camera (model FE8171V). The advantages of

this camera include high-resolution, easy installation, low

cost, and absence ofmoving parts. On the other hand, given

that direct sunlight is not blocked, the circumsolar region is

affected by glare caused by forwardMie scattering and also

from light scattered from the dome. The image glare and

scattered light affect negatively the accuracy of the cloud

segmentation, as their RGB values can be very similar to

the ones from clouds.

Several cloud identification methods [fixed threshold

method (FTM), minimum cross entropy (MCE) method,

and clear-sky library (CSL) method, discussed in section

3] were used to detect clouds based on the proposed new

camera. However, their performance is very sensitive to

the type of cloud cover. For instance, neither FTM nor

MCE method is capable of addressing the image glare,

while the CSL method is not appropriate for cloud de-

tection during overcast periods. To overcome these is-

sues, we propose a smart adaptive cloud identification

(SACI) system that combines solar irradiance measure-

ments synchronized with sky images.

The main idea of SACI is that by combining global

horizontal irradiance (GHI) measurements and sky im-

ages, it will be possible to distinguish between image glare

(mostly seen during clear time periods) and clouds. Based

on GHI measurements obtained from a pyranometer,

each image was classified into three categories: clear,

overcast, and partly cloudy. Once the category of the sky

image was determined, SACI used the most appropriate

cloud detection scheme to classify the image pixels into

either cloud or sky. The classified binary cloud map was

then translated into numerical cloud indices by a grid-

cloud fraction method (Marquez et al. 2013a) in order to

forecast GHI. The GHI forecast was produced using ar-

tificial neural networks (ANNs) that use the cloud indices

and GHI lagged data as inputs. The longest forecast ho-

rizon considered was 15min. This limitation arises from

the fact that for longer forecast horizons the most rele-

vant information, in terms of cloud cover, is outside of the

field of view of the camera.

This paper is organized as follows: Section 2 covers the

deployment of cameras and pyranometers, the collec-

tion of images and irradiance data, and the separation of

data for training and validation; section 3 covers the

methods used to detect clouds and forecast GHI; section

4 covers the results and discussion for both cloud de-

tection and GHI forecast; and, finally, section 5 presents

the conclusions.

2. Data and instruments

a. Sky images

In this work we use sky images obtained from two fish-

eye cameras featuring a 3.1-megapixel complementary

metal oxide semiconductor (CMOS) sensor and a 3608panoramic view lens that were deployed in two locations

in California (Merced and Folsom). The fish-eye cameras

capture images every minute during daylight hours and

transfer them, via FTP, to a web server where they are in

a MySQL database. This process is fully automated, re-

quiring no on-site staff; however, occasional cleaning of

the fish-eye lens is necessary.

To develop the cloud detection algorithm, we used 80

images captured in Merced and 30 images captured in

Folsom between 5March and 5 April 2013. These images

were selected manually and represent atmospheric con-

ditions that can be categorized into three groups: clear,

overcast, and partly cloudy. Each pixel in these images

was manually classified into either clear or cloudy. These

images were used as the ground truth in the assessment of

the cloud detection algorithm accuracy.

The images fromMercedwere separated into a training

set (50 images) and a validation set (30 images). All the

images from Folsom were used as an independent vali-

dation set to study the generality of our models. The

partition of the images by cloud cover type is listed in

Table 1. The training set is used to optimize the various

parameters (introduced below) for all investigated cloud

identification methods. The optimized parameters were

then used to evaluate bothMerced and Folsom validation

images.

b. Irradiance data

To measure GHI, pyranometers have been deployed

next to the cameras. Because of availability, a LI-COR

LI-200 silicon photodiode pyranometer was installed in

Folsom and an Eppley Laboratory precision spectral

pyranometer (PSP) with a thermopile sensor was in-

stalled in Merced. The Eppley PSP is a secondary stan-

dard radiometer that meets the highest standards of the

World Meteorological Organization radiometer charac-

terizations. The LI-200 is less accurate but still meets the

TABLE 1. Partitions of the selected images.

Clear Overcast Partly cloudy

Merced

Training 20 20 10

Validation 10 10 10

Folsom

Validation 10 10 10

Total 40 40 30

1996 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 31

requirements for our purposes. For both instruments,

data was logged as 1-min averages with a Campbell Sci-

entific CR1000 datalogger. Data were accessed in real

time and stored in a MySQL database.

c. Data for GHI forecast

For the GHI forecast, irradiance data and images were

paired and collected in Folsom at 19 920 time points

during the winter and spring of 2013 (13 January–2 April

2013). During these seasons there are sufficient data for

clear, overcast, and partly cloudy periods. Data used for

this study considered only periods when the solar eleva-

tion angle was larger than 308. These data were randomly

separated in two subsets: 70% as the learning set (14 000

time points) and 30%as the testing set (5920 time points).

The learning set is used to optimize theANNmodels, and

the testing set is used to assess the performance of the

ANN models.

3. Methods

a. SACI procedure

SACI applies separate cloud identification schemes

from the literature according to the image categories to

maximize the accuracy of cloud identification. With a pro-

posed smart image categorization (SIC), SACI first clas-

sifies the sky image into three categories: clear, overcast,

and partly cloudy. A clear period is defined as a period of

time when clouds do not obscure the sun and the total sky

cloud coverage is less than 5%. Overcast is defined as

a period of timewhen the sun is obscured by clouds and the

total sky cloud coverage is higher than 90%. The remain-

ing data points are defined as partly cloudy. Based on the

discussion above, SACI uses FTM for overcast images, the

CSL method and FTM for clear images (CSL plus FTM),

and the CSL method with the MCE method for partly

cloudy images (CSL plus MCE). Figure 1 illustrates the

workflow for SACI.

b. SIC

To apply the correct method, we developed an

algorithm—SIC— that receives the images and the

most recent GHI data and that decides which cloud iden-

tification method is applied. SIC integrates the hybrid

thresholding algorithm (HYTA), which classifies images

based on their normalized red–blue ratio (NRBR) histo-

grams, with the clear-sky irradiance thresholding (CSIT)

method, which uses theGHI time series to detect clear-sky

periods. SIC classifies each input image into three atmo-

spheric categories. First, CSIT identifies whether the image

corresponds to a clear or cloudy period based on the irra-

diance. If the clear-sky irradiance thresholding returns true,

then the image is categorized as clear. Otherwise, HYTA is

used to analyze the NRBR histogram and classifies the

images as overcast or partly cloudy.

CSIT is a clear-sky detection method (Long and

Ackerman 2000; Younes and Muneer 2007; Reno et al.

2012) that uses five criteria computed from the irradi-

ance time series:

(i) mean GHI (G),

(ii) maximum GHI (M),

(iii) length of the GHI time series (L)

L5 �n

i51

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi(GHIi112GHIi)

21 (ti112 ti)2

q, (1)

(iv) variance of GHI changes (s)

s5

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

n2 1�n21

i51

(ki 2 k)2

s, (2)

where k is the clear-sky indices, and

(v) maximum deviation from clear-sky GHI slope (DS)

DS5max[j(GHIi112GHIi)2 (GHIclri11

2GHIclri )j]"i 2 f1, 2, . . . , ng , (3)

where the superscript clr represents the predicted

GHI from clear-sky model.

The first and second criteria are the simplest way to

differentiate a cloudy period from a clear-sky period.

The third and fourth criteria identify the clear-sky pe-

riods based on the irradiance time series shape, which

identify the increased irradiance variability caused by

different cloud genres. The fifth criterion ensures that

successive periods with cloud shadows occurring on the

period boundaries are classified as nonclear (Reno et al.

2012).

In this study, CSIT calculates the five criteria for the

portion of the GHI time series corresponding to the past

FIG. 1. Schematic of SACI. Dashed-line square encloses the SIC.

SEPTEMBER 2014 CHU ET AL . 1997

10min. Then the criteria are compared to their clear-sky

counterparts for the same period, which are computed

with Ineichen’s clear-sky irradiance model (Ineichen

2008). The period is classified as clear when all the five

criteria meet preset thresholds. The thresholds of CSIT

applied in this study, listed in Table 2, are similar to

those proposed by Reno et al. (2012).

The clear-sky model used for this study is described in

Ineichen and Perez (2002), Ineichen (2006), Gueymard

and Wilcox (2011), and Inman et al. (2013), which is

based on the following equation:

DNI5 I03 e2dcda

TLAM , (4)

where DNI stands for direct normal irradiance, I0 51360Wm22 is the extraterrestrial irradiance, AM is the air

mass derived based on solar geometry, d is the total optical

thickness for clear and dry atmosphere (cda) (depends on

AM), and TL is an airmass independent Linke turbidity

coefficient derived from broadband beam radiation mea-

surements. Therefore, to calculate the clear-sky values,

time, longitude, latitude, and average Linke turbidity

values have to be used as an input. Linke turbidity values

are available as monthly averages derived from global

beam radiation measurements and are publicly available.

For a detailed description of the calculation of Linke tur-

bidity values, please see Ineichen and Perez (2002).

HYTA, proposed by Li et al. (2011), categorizes im-

ages into two groups based on their NRBR distribution

histogram: unimodal or bimodal. Unimodal histograms,

characterized by a single and sharp peak, correspond to

uniform cloud cover (either clear or overcast); bimodal

histograms, characterized by more than one peak and

a large variance, correspond to partly cloudy sky. In

general, the standard deviation of a unimodal image is

significantly lower than that of a bimodal image. Thus,

unimodal images can be differentiated from bimodal

images by using a standard deviation threshold (SDT).

Examples of the NRBR histograms for different cloud

cover can be seen in Fig. 2. The overcast image yields

a sharp peak in its NRBR histogram with a low standard

deviation. The clear-sky image is characterized by an

NRBR histogram with a high deviation because of the

glare in the circumsolar region whose pixels have

a high red–blue ratio (RBR) similar to cloud pixels. As

a result, the standard deviation thresholding is unable

to distinguish between clear and cloudy skies. This

observation validates the introduction of the CSIT to

discriminate between clear images and partly cloudy

images.

The SIC thresholds employed in this work, which are

based on suggestions in the literature (Long and

Ackerman 2000; Younes and Muneer 2007; Ineichen

2008; Li et al. 2011; Reno et al. 2012; Marquez and

Coimbra 2013), correctly classified the sky condition for

each of the 110 sky images listed in Table 1. However, the

SIC thresholds may be sensitive to different local mi-

croclimates and not universally applicable. Therefore,

the SIC needs calibration with locally collected image

and irradiance data when applied to a new location.

c. Cloud identification methods

FTM is based on the fact that cloud pixels (in an RGB

image) have higher red (R) intensity values than sky

pixels. Generally, the ratio (RBR 5 R/B) or difference

(RBD 5 R 2 B) of red intensity to blue intensity is

calculated for every pixel in the image and compared

with a fixed threshold to determine whether the pixel

corresponds to cloud or clear sky. An alternative to

these two quantities is the NRBR [5(R2 B)/(R 1 B)].

NRBR shows improved robustness to noise because it

avoids extremely large RBRs when pixels have very low

blue intensities (Li et al. 2011). For this reason, the FTM

used in this study is based on the NRBR parameter. The

threshold of FTM is estimated by maximizing the FTM

cloud identification accuracy (defined below in section

3d) using the training set of images.

FTM performs very well for clear or overcast images,

but it is incapable of detecting thin clouds such as cirri-

form (Long et al. 2006; Li et al. 2011). Yang et al. (2009)

and Li et al. (2011) show that theMCEmethod achieved

better performance than the FTM for cumuliform and

cirriform cloud identification.

TABLE 2. Clear-sky irradiance thresholds (applied to a 10-min window). Symbol D represents the difference between the criteria of

measuredGHI and the criteria of clear-skymodel. The criteria values selected byCSIT followReno et al. (2012). The period is classified as

clear when all the five criteria meet the listed thresholds.

Thresholds Reno et al. report CSIT

Mean GHI difference (DG, Wm22) 75 75

Max GHI difference (DM; Wm22) 75 75

Length difference of GHI time series (DL) 25 to 110 25 to 15

GHI variance (s) 0.005 0.005

Max dev from clear-sky GHI slope (DS; Wm22) 8 8

1998 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 31

TheMCEmethod is an adaptive thresholding method

based on the Otsu algorithm (Otsu 1979; Li and Lee

1993; Li and Tam 1998; Li et al. 2011). Marquez and

Coimbra (2013) also used the MCE method and con-

cluded that the value of the MCE method threshold

must be confined within an interval to maintain a satis-

factory performance. The interval limits are estimated

from the training set. Once the threshold is determined,

pixels with higher RBR than that value are classified as

cloudy.

When glare is present (see Fig. 3), the RBR of cir-

cumsolar pixels increases, leading FTM and the MCE

method to misclassify those pixels as cloud. Glare is

highly dependent upon solar geometry and can be mostly

circumvented by utilizing the CSL method (Ghonima

et al. 2012).

The CSL method is a database of previously captured

clear-sky images for different solar zenith angles. It is

used to remove the geometric variation of clear-sky

RBRs that depend on the sun-pixel angle and the solar

zenith angle. This is accomplished by subtracting or

offsetting the RBR of the input image by the reference

CSL RBR that corresponds to the same zenith angle,

generating the Diff image: Diff5RBRminus CSL. The

cloud detection methods can then be applied to the

DIFF image. For the overcast images, the circumsolar

region cloud pixels are likely to be misidentified as clear

pixels because of the subtraction process of the CSL

method, as shown in Fig. 3 (middle column).

A second-order effect on the solar irradiance is caused

by aerosol, quantified by the aerosol optical depth. This

effect is not considered in the CSL method, but it also

affects the clear-sky RBRs and the accuracy of cloud

identification. To account for this effect, a haze correc-

tion factor (HCF) can be used to correct the images,

DiffHCF 5 RBR 2 (CSL)(HCF), before applying the

cloud identification. The HCF is computed iteratively

(Seiz et al. 2007; Ghonima et al. 2012):

1) Preliminary cloud identification is performed with

the CSL method, and HCF is set to 1.

2) The ratio of skymeanRBR to the CSLmethodmean

RBR (Prt) for each pixel is obtained as the tempo-

rary HCF.

3) The CSLmethod is multiplied by the Prt and another

round of cloud identification is initiated.

4) Steps 2 and 3 are repeated until Prt converges below

a threshold (which is set to 0.01 in this study).

5) The converged Prt is returned as the HCF.

Examples of DiffHCF are shown in Fig. 3 (bottom row).

d. Cloud detection performance

Each pixel from the cloud classification image is

compared to its counterpart in the manually annotated

cloud map. Using the confusion matrix shown in Table 3

for sky and cloud pixels, we quantify the hits and misses

in all the pixels in the image. Based on these values, the

accuracy is defined as the percentage of pixels that are

correctly classified:

Accuracy5TP1TN

TP1TN1FP1FN(100%). (5)

FIG. 2. Examples of (top) some original images and (bottom) their NRBR distribution histograms for the (left) clear, (middle) overcast,

and (right) partly cloudy periods.

SEPTEMBER 2014 CHU ET AL . 1999

e. Grid-cloud fraction method

Clouds contribute differently to the near-future be-

havior of GHI. For instance, clouds moving toward the

sun are much more relevant to the forecasting models

than clouds moving away from the sun. To extract in-

formation of clouds that may shade the sun in the near

future, we use the grid-cloud fraction method developed

by Marquez and Coimbra (2013).

This method can be summarized in four main steps.

First, all images are projected from the convex mirror

space onto a flat rectangular space (shown in Fig. 4b) to

remove the geometric distortion of the images. Second,

pairs of consecutive images are processed using particle

image velocimetry (PIV) to compute the flow direction of

the clouds. The PIV algorithm (Adrian and Westerweel

2010) partitions the image into interrogation windows

(32 pixels 3 32 pixels in this case). The correlation

between two consecutive interrogation windows is

analyzed through the minimum quadratic difference

method to determine the ‘‘particles’’ (the clouds) dis-

placement. The velocity field (shown inFig. 4b) is obtained

by dividing the displacements by the separation time of

the images and clustering using the k-means clustering

method. The velocity magnitudes usually cluster around

two means: one near or equal to zero and the other with

a largermagnitude. The first result is fromwindowswhere

there are no clouds or where they are stationary and is

therefore disregarded. The second result is selected as the

representative velocity for the cloud motion. Currently,

our algorithm provides binary cloud maps and a single

TABLE 3. Confusion matrix of automatic cloud detection for each

pixel.

Manual classificationAlgorithm

classification Cloud Sky

Cloud True positive (TP) False positive (FP)

Sky False negative (FN) True negative (TN)

FIG. 3. Examples of (top) original images, (middle) RBR images, and (bottom) CSL method–subtracted RBR images for the (left) clear,

(middle) overcast, and (right) partly cloudy periods. Color bars indicate RBR magnitudes for the pixels in each image.

2000 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 31

FIG. 4. Main steps of the grid cloud fractions method. (a),(b) Original consecutive image pair used in the analysis.

(c),(d) Image pair is projected onto a rectangular grid, and (e) velocity vectors computed by the PIV algorithm.

(f) Cloud classified image, where white pixels represent cloud; gray pixels represent clear sky; and black pixels are

obstacles, which are excluded from the analysis.

SEPTEMBER 2014 CHU ET AL . 2001

cloud motion vector. Therefore, it is unable to consider

multiple layer cases of clouds. Noise created by the PIV

algorithm is addressed by clustering all nonzero grid ve-

locities to obtain a representative velocity. Moreover,

based on the assumption that the cloud flow does not

significantly change in a short time, we use as the repre-

sentative velocity the median speed (for both x and y

directions) of the past 10 min. More details about the

method for the calculation of the cloud motion vector

can be found in Adrian andWesterweel (2010), Marquez

and Coimbra (2013), and Chu et al. (2013).

To qualitatively study the accuracy and robustness of

PIV in computing the cloud moving direction, we com-

pared the cloud moving directions from PIV with manu-

ally identified cloud moving directions on seven days.

An example of the comparison is illustrated in Fig. 5. We

found that, in about 95% of the tested instances, the PIV-

identified cloudmoving directions are in the error range of

the manually identified directions.

Third, the SACI is used to classify the sky images into

binary cloud images. A set of grid elements (X1, X2, . . . ,

X6) originating from the sun’s position and oriented in the

reverse direction of the representative velocity for the

clouds is placed over the binary cloud images (shown in

Fig. 4c) In this work we set the area of grid elements to 100

pixels 3 100 pixels. Finally, the cloud indices CIi are

computed as the percentage of pixels classified as cloud for

element i. This process is applied to all the images under

study, generating CIi time series that are used as potential

inputs for the ANN-based forecasting models.

f. Artificial neural network

ANNs are widely used nonlinear regression tools for ir-

radiance forecasts that require no prior assumptions con-

cerning data relationships (Bishop 1994; López et al. 2005).In this study, we use a multilayer feed-forward perceptron

that is widely used in forecasting and modeling of solar ir-

radiance (Mellit and Kalogirou 2008; Mellit and Pavan

2010; Marquez and Coimbra 2011; Pedro and Coimbra

2012; Marquez et al. 2013b). ANNs use interconnected

signal processing elements, which are called neurons. Neu-

rons are placed in layers, and the layers between the input

layer (the first) and the output layer (the last) are called

hidden layers. Neurons receive weighted inputs incoming

from the previous layers and add a bias or threshold to the

sums. After that, the sums are processed by the activation

function of the neurons (sigmoidal function in this study) to

generate outputs. The outputsYi are then used as the inputs

for neurons on the following layer:

Yi 5 f

�N

j51

wjXj 1bj

!, (6)

whereX and Y are the input and output, respectively; w

is the weight; b is the bias; and f is the activation func-

tion. Once the ANN structure (the number of layers and

neurons) is established, several free parameters corre-

sponding to the weight and bias values in each neuron

are found in the training process. The training adjusts

these parameters seeking to minimize the error between

the ANN’s outputs and the targets. This is an iterative

process that stops once discrepancy is lower than a pre-

set value. In this work, the Bayesian regularization

process that uses the Levenberg–Marquardt optimiza-

tion is used to train the ANNs. This operation is repre-

sented by

[ANNu(L,N), I,T]-/Training ANNf , (7)

where ANNu represents the untrained feed-forward neu-

ral network with L hidden layers andN neurons per layer.

The I represents the training inputs and T represents the

training targets in the training process.

In this study, two ANN models (ANNc and ANNnc)

were developed. ANNc considers as potential inputs the

time-lagged measured GHI values, ranging from 0 to

20min in 5-min steps, the total sky cloudiness, and the

CIi extracted by the grid fractionmethod. ANNnc, which

works as a control group, considers only time-lagged

measured GHI values.

g. ANN validation

Overfitting is a possible problem of ANN training.

Overfitting manifests when the optimal solution found for

the training data performs poorly when applied to new

data. In this work, the cross-validation method (CVM)

(Efron and Gong 1983; Geisser 1993; Kohavi 1995; Jain

et al. 2000; Lendasse et al. 2003), a popular method in

model estimation, is used to determine whether a hypoth-

esis ANNmodel fits the data of interest. CVM divides the

FIG. 5. Example time series of the PIV and the manually iden-

tified cloud moving directions on 1 Apr 2013. Error range (marked

as yellow) of themanually identified directions is 208. PIV-identified

directions that locate in the yellow region are considered accurate

tested instances.

2002 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 31

learning data intoK disjoint subsets Siwith i5 1, . . . ,K [K

is set to 10 as suggested in previous studies (Kohavi 1995;

McLachlan et al. 2004)]. After the data partition, one

subset is reserved for validation and the remaining subsets

are used to train theANNmodel. Root-mean-square error

(RMSE) is taken as the performance of the model on the

validation set. This process is repeated K times, each time

taking a different subset as the validation set and returning

an RMSE. The mean and variance of the K RMSEs are

taken as the CVM validation performance. Models with

high mean and variance of the validation RMSEs are very

dependent on the training data and therefore prone to be

overfitted.

h. ANN optimization

The performance of the forecasting model depends

greatly on its input variables and the ANN topology

(e.g., number of layers, number of neurons per layer).

To obtain the best possible forecast, we use a genetic

algorithm (GA) to determine these free parameters.

GA is an efficient tool (Holland 1992; Pai and Hong

2005; Marquez and Coimbra 2011; Pedro and Coimbra

2012) that iteratively scans potential solutions (in-

dividuals) of the search space to find the optimal so-

lution. GA optimization is initiated with randomly

selected individuals (the population) whose fitness is

evaluated according to an objective function. In this

study, the GA optimization objective function is the

average validation RMSE. The individuals with the lowest

validation RMSE are selected as parents and generate the

new generation through crossover and mutation. This it-

erative process is finished when the average fitness of the

population converges.

i. Evaluation of the GHI forecast

The quality of the forecasting is benchmarked against

the persistence model. The persistence model is the

simplest forecasting model and is very accurate for low-

variability periods. To remove the deterministic diurnal

solar variation from the persistence forecast, we used

the clear-sky index persistence model as a baseline. This

persistence model assumes that the clear-sky index of

GHI remains constant between t and t1 Dt, resulting inthe forecasting

Gp(t1Dt)5Gclr(t1Dt)

Gclr(t)[G(t)] , (8)

where the subscript p denotes persistence and Gclr is

provided by the clear-skymodel used above in the CSIT.

The forecast performance of the model is assessed

using three statistical metrics: mean biased error

(MBE),

MBE51

m�m

t51

[G(t)2G(t)] ; (9)

RMSE,

RMSE5

ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi1

m�m

t51

[G(t)2G(t)]2

s; (10)

and forecast skill (s), which measures the improvement

of the investigated forecast over the reference persis-

tence model:

s5 12RMSE

RMSEp

. (11)

TABLE 4. Optimal thresholds that maximize the cloud identifi-

cation accuracy on the learning dataset. The third column repre-

sents the threshold values used by the methods listed in the second

column. SACI integrates the three reference methods, and the

SDT represents the standard deviation threshold for the HYTA

method. All thresholds are dimensionless.

Methods Thresholds

Reference methods FTM 20.11

Bounds of MCE 0.15–0.40

CSL 0.1

SACI FTM 20.11

CSL 0.1

Bounds of MCE 0.08–0.2

SDT 0.085

TABLE 5. Average accuracy (%) for the validation images using Eq. (5). The values inside the parenthesis are the standard deviation.

Boldface values indicate best performance in each category.

Merced Folsom

Clear Overcast Partly cloudy Clear Overcast Partly cloudy

FTM 59.86 (7.6) 95.8 (1.5) 80.7 (10.6) 55.3 (5.7) 93.7 (3.0) 89.2 (3.0)

MCE 53.0 (6.1) 93.7(2.0) 81.8 (10.4) 65.0 (3.5) 92.2 (3.2) 88.2 (3.9)

CSL plus FTM 92.2 (2.6) 42.5 (3.0) 87.3 (3.7) 94.5 (1.8) 49.6 (8.8) 90.8 (7.2)

CSL plus MCE 87.4 (5.7) 50.0 (8.2) 90.6 (3.2) 89.5 (1.9) 54.9 (8.8) 93.6 (1.6)

SACI 92.2 (2.6) 95.8 (1.5) 90.6 (3.2) 94.5 (1.8) 93.7 (3.0) 93.6 (1.6)

SEPTEMBER 2014 CHU ET AL . 2003

4. Results and discussion

a. Cloud detection training

The training set, which contains 50manually annotated

images obtained at Merced, is used to determine the

optimal thresholds for the SACI algorithm. These are

1) the threshold of FTM, 2) the threshold of CSL plus

FTM, 3) the upper and lower bounds of theMCEmethod

threshold, 4) the upper and lower bounds of the CSL-

plus-MCE threshold, and 5) the SDT for the hybrid

thresholding. Because identifying cirriform/cumuliform

clouds is of primary interests inmany studies, particularly

for power generation forecasts, these parameters are

optimized to maximize the overall accuracy for partly

cloudy images. The optimal thresholds determined on the

training set are shown in Table 4.

b. Cloud detection validation

Images from the validation set were used to evaluate all

cloud identificationmodels with the optimized thresholds

listed in Table 4. The accuracy for the different models

applied to the validation images from Merced and Fol-

som is listed in Table 5.

The validation results show that the SACI categorizes

all validation images accurately and achieves the highest

overall accuracy for different weather conditions. FTM

and the MCE method cannot distinguish glare from

clouds during the clear period, while the CSL-based

methods misclassified nearly half of the pixels in the

overcast images. Given that SACI was optimized using

only images from Merced and that it achieves similar

accuracy when applied to Folsom (see Table 5), we can

conclude that this method performs consistently, in-

dependently of the location.

Cloud misclassification is usually due to three reasons:

1) saturated pixels in the circumsolar region because of

glare that are not completely offset by the CSL method,

2) diffraction of sunlight on the glass dome, and 3) dirt

specks that diffract the sunlight intensively during the

clear time. An example of clear-sky SACI cloud de-

tection is shown in Fig. 6a. The average accuracy of SACI

for overcast images is 94%. Most misclassified pixels are

FIG. 6. Examples of SACI detection results for (a) clear; (b) overcast and partly cloudy with (c) optically thick clouds; and (d) optically

thin clouds sky images, where the top row is the original image and the bottom row is the classified binary cloud maps. White pixels

represent cloud, gray pixels represent sky, and black pixels represent the masked region.

TABLE 6.ANN inputs and parameters selected by theGA for the

GHI forecast model. Indices represent selected ANN inputs: 1–5

are the time-laggedGHI values, 6 is the total sky cloudiness, and 7–

11 are the CIi extracted by the grid fractionmethod. The number of

hidden layers is represented by L, and N represents the number of

neurons per hidden layer.

Forecast horizon Inputs’ indices L N

ANNnc 1 2 3 4 5 1 10

ANNc 1 2 3 5 6 7 8 9 10 11 1 7

2004 JOURNAL OF ATMOSPHER IC AND OCEAN IC TECHNOLOGY VOLUME 31

located near the edge of the images (close to the horizon),

where R intensities are not as high as those in the center

of the images during overcast periods, which can be seen

in Fig. 6b.

Partly cloudy images usually contain both thick and thin

clouds. Thick cloud pixels generally have an RBR signif-

icantly higher than clear pixels and therefore are detected

easier by cloud detection algorithms. When the majority

of clouds in an image are optically thick, the algorithm is,

on average, over 95% accurate. An example can be seen

in Fig. 6c.On the other hand, thin clouds, whoseRBRs are

close to or overlap clear RBRs, are generally more diffi-

cult to be identified correctly. Therefore, thin cloud pixels

generally have low identification accuracy, and the SACI

algorithm is, on average, approximately 85% accurate.

Most of the misclassified pixels are false negative cases,

which indicate that the algorithm is more likely to classify

thin cloud pixels as sky pixel. An example of thin cloud

detection is shown in Fig. 6d.

c. GHI forecast results

The GA algorithm takes 50 generations until the av-

erage fitness converges. The optimal set of input vari-

ables, hidden layers, and the number of neurons per

layer selected are shown in the Table 6. These results

show that the total sky cloud coverage and cloud indices

are useful for GHI forecast. Table 6 also shows that the

last measured values of GHI are selected as forecast

inputs for all horizons. This indicated that the latest GHI

values are highly informative for short-term irradiance

forecast.

The GA optimized ANN models are evaluated on the

testing set that is independent of the training process and

compared to the persistence model. The forecasting per-

formancemetrics are presented inTable 7. In terms of bias,

all models exhibit small values. In terms of RMSE and

forecast skill, both ANN models significantly outperform

the persistence. The results show that ANNc benefits from

integrating the cloud cover information, achieving signifi-

cantly higher forecast skill than theANNnc. Improvements

over the ANNnc range from 2.9% to 8.1% depending on

the forecast horizon.

To further understand the ANNc model’s perfor-

mances, its error distributions for the 5-, 10- and 15-min

horizons are plotted in Fig. 7. These figures show that in

the low-error region (between 20.1 and 0.1 kWm22),

both persistence and ANNc have sharp peaks near the 0.

However, the persistence forecast has a higher frequency

of errors with high absolute values (.0.2kWm22), re-

sulting in a longer and heavier error distribution tail.

Compared to the persistence error distribution, theANNc

reduces the occurrence of high absolute value errors,

particularly for negative errors, and produces an error

distribution with shorter tails and rounder shoulders.

TABLE 7. Statistical error metrics for testing results of the different forecast models for 5-, 10-, and 15-min horizons. Bolded numbers

identify the best-performing method for a given metric. MBE and RMSE are in Wm22 and s is in %

5 min 10 min 15 min

MBE RMSE s MBE RMSE s MBE RMSE s

Persistence 0.9 90.6 0 20.5 101.3 0 1.4 106.5 0

ANNnc 0.8 80.1 11.5 23.5 90.9 10.3 20.3 93 12.5

ANNc 2.3 77.5 14.4 23 82.7 18.4 20.4 85.5 19.7

FIG. 7. Plots of error distributions for (a) 5-, (b) 10-, and (c) 15-min forecasts. The y axis of the plots is in logarithmic scale.

SEPTEMBER 2014 CHU ET AL . 2005

Consequently, the distributions of ANNc forecast errors

are narrower, resulting in lower RMSEs. Sample time

series of forecasted GHI and absolute errors for 10-min

horizon forecast are plotted in Fig. 8.

5. Conclusions

A high-resolution fish-eye dome network camera is

used as a low-cost alternative to sky imagers for cloud

and sky condition identification. Themain advantages of

the sky camera over sky imagers are resolution, porta-

bility, absence of moving parts, and substantially lower

cost than sky imagers. The main drawbacks of using this

type of camera for sky imaging relate to excessive glare.

Nonetheless, we presented a number of methods in this

work that help circumvent this deficiency.

A smart adaptive cloud identification (SACI) system

was proposed and deployed. This system integrates the

fixed thresholding method, minimum cross entropy

thresholding method, and the use of a clear-sky library.

The overall methods addresses glaring caused by for-

ward Mie scattering and lens flaring solely through im-

age processing algorithms. SACI uses SIC, which

analyzes global horizontal irradiance data and normal-

ized red-to-blue ratio distribution to categorize the input

images. The method then employs an optimal cloud

detection scheme for each categorized image. Optimi-

zation is performed on manually annotated images, and

the validation tests for different locations show that

SACI achieves robust, location independent, and the

most accurate cloud classification among all the refer-

ence models analyzed. Mean accuracy of this system is

over 92%, 94%, and 89% for clear, overcast, and partly

cloudy images, respectively.

By using the numerical cloud indices extracted from

the binary cloud classified images, an ANN model is

developed for 1-min-average GHI forecasts for 5-, 10-,

and 15-min horizons. Application of the SACI-ANN

model to an independent testing set shows that the

proposed forecasting methodology achieves forecasting

skill of 14.4%, 18.4%, and 19.7% for 5-, 10-, and 15-min

horizons, respectively.

Acknowledgments. Partial support from the National

Science Foundation (NSF) EECS-EPAS Award 1201986

(managed byDr. PaulWerbos) is gratefully acknowledged.

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