The accurate estimation of direct normal irradiance (DNI) under clear sky conditions plays an important role in the concentrated solar thermal plant. A hybrid model with adjustable inputs is proposed to calculate the clear-sky DNI, including a base clear-sky model and an error-correction model. The base clear-sky model is able to estimate the clear-sky DNI at any place with only the local date and location information, and the error-correction model serves as a supplementary to improve the calculating accuracy with available meteorological data. The error-correction model effectively integrates a linear part and a nonlinear part, and its inputs are adjustable according to the available meteorological observations. Several experiments have been conducted to evaluate the performance of the proposed model with data from three observation stations provided by the National Renewable Energy Laboratory open database. The results show that the hybrid model is able to provide great improvement over the base clear-sky model with 28%–70% on normalized root mean square error, and it also performs better than those using a linear or nonlinear error correction model. It is concluded that the performance of the hybrid model is comparable with other published methods in calculating the clear-sky DNI with concrete statistics.

1.
Bird
,
R. E.
and
Hulstrom
,
R. L.
, “
Simplified clear sky model for direct and diffuse insolation on horizontal surfaces
,”
Report No. SERI/TR-642-761
, Solar Energy Research Institute, Golden, CO, USA (
1981
).
2.
Bone
,
V.
,
Pidgeon
,
J.
,
Kearney
,
M.
, and
Veeraragavan
,
A.
, “
Intra-hour direct normal irradiance forecasting through adaptive clear-sky modelling and cloud tracking
,”
Sol. Energy
159
,
852
867
(
2018
).
3.
Chu
,
Y.
,
Pedro
,
H. T.
, and
Coimbra
,
C. F.
, “
Hybrid intra-hour DNI forecasts with sky image processing enhanced by stochastic learning
,”
Sol. Energy
98
,
592
603
(
2013
).
4.
Dai
,
Q.
and
Fang
,
X.
, “
A simple model to predict solar radiation under clear sky conditions
,”
Adv. Space Res.
53
(
8
),
1239
1245
(
2014
).
5.
Dube
,
A.
,
Rizwan
,
M.
, and
Jamil
,
M.
, “
New approach for maximizing generation and optimal utilization of available space for solar PV system
,”
J. Renewable Sustainable Energy
10
(
6
),
063703
(
2018
).
6.
Elminir
,
H. K.
,
Azzam
,
Y. A.
, and
Younes
,
F. I.
, “
Prediction of hourly and daily diffuse fraction using neural network, as compared to linear regression models
,”
Energy
32
(
8
),
1513
1523
(
2007
).
7.
Gueymard
,
C.
, “
A two-band model for the calculation of clear sky solar irradiance, illuminance, and photosynthetically active radiation at the earth's surface
,”
Sol. Energy
43
(
5
),
253
265
(
1989
).
8.
Gueymard
,
C.
, “
Critical analysis and performance assessment of clear sky solar irradiance models using theoretical and measured data
,”
Sol. Energy
51
(
2
),
121
138
(
1993
).
9.
Gueymard
,
C. A.
, “
Clear-sky irradiance predictions for solar resource mapping and large-scale applications: Improved validation methodology and detailed performance analysis of 18 broadband radiative models
,”
Sol. Energy
86
(
8
),
2145
2169
(
2012
).
10.
Guo
,
W.
,
Ong
,
Y. S.
,
Zhou
,
Y.
,
Hervas
,
J. R.
,
Song
,
A.
, and
Wei
,
H.
, “
Fisher information matrix of unipolar activation function-based multilayer perceptrons
,”
IEEE Trans. Cybern.
49
,
1
11
(
2018
).
11.
Ianetz
,
A.
and
Kudish
,
A.
, “
A method for determining the solar global and defining the diffuse and beam irradiation on a clear day
,”
Modeling Solar Radiation at the Earth's Surface
(
Springer
,
Berlin, Heidelberg
,
2008
), pp.
93
113
.
12.
Ineichen
,
P.
and
Perez
,
R.
, “
A new airmass independent formulation for the Linke turbidity coefficient
,”
Sol. Energy
73
(
3
),
151
157
(
2002
).
13.
Inman
,
R. H.
,
Edson
,
J. G.
, and
Coimbra
,
C. F.
, “
Impact of local broadband turbidity estimation on forecasting of clear sky direct normal irradiance
,”
Sol. Energy
117
,
125
138
(
2015
).
14.
Iqbal
,
M.
,
An Introduction to Solar Radiation
(
Elsevier
,
2012
).
15.
Janjai
,
S.
,
Sricharoen
,
K.
, and
Pattarapanitchai
,
S.
, “
Semi-empirical models for the estimation of clear sky solar global and direct normal irradiances in the tropics
,”
Appl. Energy
88
(
12
),
4749
4755
(
2011
).
16.
Kopp
,
G.
and
Lean
,
J. L.
, “
A new, lower value of total solar irradiance: Evidence and climate significance
,”
Geophys. Res. Lett.
38
(
1
),
541
551
, (
2011
).
17.
Laue
,
E. G.
, “
The measurement of solar spectral irradiance at different terrestrial elevations
,”
Sol. Energy
13
(
1
),
43
57
(
1970
).
18.
Law
,
E. W.
,
Prasad
,
A. A.
,
Kay
,
M.
, and
Taylor
,
R. A.
, “
Direct normal irradiance forecasting and its application to concentrated solar thermal output forecasting–A review
,”
Sol. Energy
108
,
287
307
(
2014
).
19.
Marquez
,
R.
and
Coimbra
,
C. F.
, “
Proposed metric for evaluation of solar forecasting models
,”
J. Sol. Energy Eng.
135
(
1
),
011016
(
2013
).
20.
Marquez
,
R.
and
Coimbra
,
C. F.
, “
Intra-hour DNI forecasting based on cloud tracking image analysis
,”
Sol. Energy
91
,
327
336
(
2013
).
21.
MIDC
, http://www.nrel.gov/midc/ for “
Measurement and Instrumentation Data Center
,”
2019
.
22.
Quesada-Ruiz
,
S.
,
Chu
,
Y.
,
Tovar-Pescador
,
J.
,
Pedro
,
H. T. C.
, and
Coimbra
,
C. F. M.
, “
Cloud-tracking methodology for intra-hour DNI forecasting
,”
Sol. Energy
102
,
267
275
(
2014
).
23.
Ruiz-Arias
,
J. A.
and
Gueymard
,
C. A.
, “
Worldwide inter-comparison of clear-sky solar radiation models: Consensus-based review of direct and global irradiance components simulated at the earth surface
,”
Sol. Energy
168
,
10
29
(
2018
).
24.
Sadhu
,
M.
,
Goswami
,
U.
,
Das
,
N.
,
Sadhu
,
P. K.
, and
Goswami
,
A.
, “
Improvement of energy forecasting model to safeguard energy security in India
,”
J. Renewable Sustainable Energy
10
(
6
),
065907
(
2018
).
25.
Shen
,
Y.
,
Wei
,
H.
,
Zhao
,
X.
,
Zhu
,
T.
, and
Zhang
,
K.
, “
A tree-based clear sky model for DNI forecasting
,” in
2018 Chinese Control and Decision Conference (CCDC)
(
2018
), pp.
4206
4211
.
26.
SoDa
, http://www.sodais.com/eng/services/index.html for “
Solar Energy Services for Professionals
,”
2019
.
27.
Struzewska
,
J.
,
Kaminski
,
J. W.
, and
Jefimow
,
M.
, “
Application of model output statistics to the GEM-AQ high resolution air quality forecast
,”
Atmos. Res.
181
,
186
199
(
2016
).
28.
Stoffel
,
T.
and
Andreas
,
A.
, “
Nrel solar radiation research laboratory (srrl): Baseline measurement system (bms); golden, colorado (data)
,”
Report No. NREL/DA-5500-56488
, National Renewable Energy Lab. (NREL), Golden, CO, USA (
1981
).
29.
Xia
,
X. L.
,
Li
,
D. F.
,
Sun
,
C.
, and
Ruan
,
L. M.
, “
Transient thermal behavior of stratospheric balloons at float conditions
,”
Adv. Space Res.
46
(
9
),
1184
1190
(
2010
).
30.
Xie
,
L.
,
Tao
,
D.
, and
Wei
,
H.
, “
Early expression detection via online multi-instance learning with nonlinear extension
,”
IEEE Trans. Neural Networks Learn. Syst.
30
(
5
),
1486
1496
(
2018
).
31.
Weitemeyer
,
S.
,
Kleinhans
,
D.
,
Wienholt
,
L.
,
Vogt
,
T.
, and
Agert
,
C.
, “
A European perspective: Potential of grid and storage for balancing renewable power systems
,”
Energy Technol.
4
(
1
),
114
122
(
2016
).
32.
Zhu
,
T.
,
Wei
,
H.
,
Zhao
,
X.
,
Zhang
,
C.
, and
Zhang
,
K.
, “
Clear-sky model for wavelet forecast of direct normal irradiance
,”
Renewable Energy
104
,
1
8
(
2017
).
33.
Zhu
,
T.
,
Zhou
,
H.
,
Wei
,
H.
,
Zhao
,
X.
,
Zhang
,
K.
, and
Zhang
,
J.
, “
Inter-hour direct normal irradiance forecast with multiple data types and time-series
,”
J. Mod. Power Syst. Clean Energy
1
9
(
2019
).
You do not currently have access to this content.