Smart model for accurate estimation of solar radiation

doi:10.1007/s11708-017-0505-3

Front. Energy

2020, Vol. 14

Issue (2) : 383-399 https://doi.org/10.1007/s11708-017-0505-3

RESEARCH ARTICLE

Smart model for accurate estimation of solar radiation

Lazhar ACHOUR¹(

), Malek BOUHARKAT¹, Ouarda ASSAS², Omar BEHAR³

¹. Electrical Engineering Department, University of Batna 2, Fesdis 05110, Algeria
². Laboratory Pure and Applied Mathematics (LPAM), University of M’sila, M’sila 28000, Algeria
³. University of M’Hammed Bougara, UMBB, Boumerdes 35000, Algeria

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Abstract

Prediction of solar radiation has drawn increasing attention in the recent years. This is because of the lack of solar radiation measurement stations. In the present work, 14 solar radiation models have been used to assess monthly global solar radiation on a horizontal surface as function of three parameters: extraterrestrial solar irradiance (G₀), duration sunshine (S) and daylight hours (S₀). Since it has been observed that each model is adequate for some months of the year, one model cannot be used for the prediction of the whole year. Therefore, a smart hybrid system is proposed which selects, based on the intelligent rules, the most suitable prediction model of the 14 models listed in this study. For the test and evaluation of the proposed models, Tamanrasset city, which is located in the south of Algeria, is selected for this study. The meteorological data sets of five years (2000–2004) have been collected from the Algerian National Office of Meteorology (NOM), and two spatial databases. The results indicate that the new hybrid model is capable of predicting the monthly global solar radiation, which offers an excellent measuring accuracy of R² values ranging from 93% to 97% in this location.

Keywords global solar radiation statistical indicator hybrid model spatial database correlation coefficients

Corresponding Author(s): Lazhar ACHOUR

Just Accepted Date: 27 September 2017 Online First Date: 14 November 2017 Issue Date: 22 June 2020

Cite this article:

Lazhar ACHOUR,Malek BOUHARKAT,Ouarda ASSAS, et al. Smart model for accurate estimation of solar radiation[J]. Front. Energy, 2020, 14(2): 383-399.

URL:

https://academic.hep.com.cn/fie/EN/10.1007/s11708-017-0505-3
https://academic.hep.com.cn/fie/EN/Y2020/V14/I2/383

Tab.1 General information of study area

Meteonorm V7. 1.6.26237. Global meteorological database. 2013–03, http://meteonorm.com/

Fig.1 Global solar radiation and extraterrestrial radiation from 2000 to 2004

Fig.2 Daily and monthly mean sunshine duration throughout five years (2000–2004)

Tab.2 Regression models used in literature

New models	Regression equations	Number of terms
Eq. (9) (Fourier series)	${G g / G 0 = a 0 + ∑ i = 1 j a i ? cos ? (i ? (S / S 0) ? w) + b i ? sin ? (i ? (S / S 0) ? w) 1 ≤ j ≤ 8$	j=7
Eq. (10) (Weibull model)	$G g / G 0 = a 1 ? b ? (S / S 0) (b 1 − 1) ? exp ? (− a 1 ? (S / S 0) b 1)$	–
Eq. (11) (Sin equations)	${G g / G 0 = ∑ i = 1 j a i ? sin ? (b i ? (S / S 0) ? c i) 1 ≤ j ≤ 8$	j=7
Eq. (12) (Power series)	${G g / G 0 = ∑ i = 0 j a i ? (S / S 0) i 0 ≤ j ≤ 7$	j=6
Eq. (13) (Rational model)	${G g / G 0 = ∑ i = 1 j + 1 a i ? (S / S 0) j + 1 − i (S / S 0) k ? ∑ i = 1 k b i ? (S / S 0) k − i 0 ≤ j ≤ 5, 1 ≤ k ≤ 5$	j=3 k=3
Eq. (14) (Gaussian model)	${G g / G 0 = ∑ i = 1 j a i ? exp ? [− ((S / S 0) − b i c i) 2] 1 ≤ j ≤ 8$	j=7

Tab.3 Proposed regression models

Algorithm:	Chart of the proposed methodology
Input:	Calculate (G₀, G_t, δ_s, ω_s, S₀) using Eqs.(1–5) Month(t) ∈{1,2,…,12} Model(m) ∈{1,2,…,14}
Output:	G_HYBRID= The prediction of the output of the GSR
	Establish and train models by training set G Calculate Error\|₍_t_,_m₎(%) of the cretirea: MBE\|_t_=1,…12, RMSE\|_t_=1,…,12, R²\|_t_=1,…,12 For i in {1,2,…,t} do For j in {1,2,…,m} do Select optimum model (model\|₍_i_,_j₎ by training set G end end Construct: G_HYBRID={G\|_1,…,12} Return G

Tab.4

Model #		Regression coefficients				RPE_min%	RPE_max%	MPE	MAPE	MBE	MABE	RMSE	R²	TT	U₉₅	GPI
Model #		a	b	c	d	RPE_min%	RPE_max%	MPE	MAPE	MBE	MABE	RMSE	R²	TT	U₉₅	GPI
Station #:	Eq. (1)	0.596	0.127	–	–	–9.8176	12.5664	–0.3710	5.1723	0.0004	0.0352	0.0445	0.3205	0.0649	4.2890	3.1669E–06
	Eq. (2)	0.396	0.69	0.385	–	–9.8279	11.5426	–0.4040	5.1031	0.0001	0.0348	0.0441	0.3436	0.0159	4.6300	1.9427E–07
	Eq. (3)	1.29	–3.05	4.43	–1.98	–15.8505	7.6161	4.1904	6.4015	0.0316	0.0452	0.0552	0.3198	5.3722	11.1657	0.0712
	Eq. (4)	0.72	0.0933	–	–	–9.7720	11.9338	–0.4649	5.1403	–0.0003	0.0350	0.0443	0.3295	0.0506	4.4239	–1.9456E–06
	Eq. (5)	0.6027	0.1832	–	–	–9.7507	12.7314	–0.4268	5.1768	0.0000	0.0352	0.0445	0.3191	0.0008	4.2619	1.0528E–10
	Eq. (6)	0.719	0.136	–	–	–9.9889	11.7500	–0.2359	5.1427	0.0013	0.0351	0.0444	0.3285	0.2228	4.4341	3.7871E–05
	Eq. (7)	0.29	0.52	–	–	–22.7743	11.6665	4.5915	8.5264	0.0335	0.0588	0.0725	0.3205	4.0064	19.9949	0.1323
	Eq. (8)	0.233	0.591	–	–	–22.0436	15.0635	1.8579	8.0791	0.0145	0.0552	0.0720	0.3205	1.5798	20.3751	0.0229
NASA–SSE #:	Eq. (1)	0.668	–0.0248	–	–	–13.3753	10.2434	–0.4263	5.3543	0.0001	0.0347	0.0431	0.0678	0.0175	8.4543	5.8393E–07
	Eq. (2)	0.537	0.346	0.254	–	–13.4497	10.9128	–0.5253	5.2914	–0.0006	0.0342	0.0430	0.1117	0.1003	8.4209	1.8089E–05
	Eq. (3)	1.48	–3.72	5.47	–2.64	–13.8272	11.0891	–0.2134	5.2843	0.0014	0.0342	0.0427	0.1552	0.2549	8.3727	–1.0927E–04
	Eq. (4)	0.645	–0.0158	–	–	–13.2409	10.3043	–0.4827	5.3644	–0.0003	0.0347	0.0432	0.0608	0.0469	8.4582	4.2293E–06
	Eq. (5)	0.6683	–0.0379	–	–	–13.3521	10.2685	–0.4493	5.3578	–0.0001	0.0347	0.0431	0.0676	0.0089	8.4545	1.5214E–07
	Eq. (6)	0.642	–0.029	–	–	–13.6043	9.9043	–0.1532	5.3280	0.0019	0.0346	0.0432	0.0605	0.3318	8.4589	–2.1230E–04
	Eq. (7)	0.29	0.52	–	–	–18.3537	22.5468	–1.8769	10.0279	–0.0089	0.0636	0.0780	–0.0679	0.8811	15.1881	0.0099
	Eq. (8)	0.233	0.591	–	–	–18.2111	26.2748	–4.8247	11.4415	–0.0279	0.0722	0.0891	–0.0679	2.5333	16.5794	0.1114
HelioClim1 #:	Eq. (1)	0.525	0.185	–	–	–9.1652	12.5458	–0.4567	5.3479	–0.0001	0.0347	0.0434	0.4491	0.0094	8.5121	–1.4687E–07
	Eq. (2)	0.586	0.012	0.118	–	–9.4256	12.7389	–0.3475	5.3342	0.0007	0.0347	0.0434	0.4506	0.1181	8.5049	2.3161E–05
	Eq. (3)	1.28	–3.11	4.96	–2.46	–6.7368	15.8996	–4.9151	6.5888	–0.0293	0.0414	0.0533	0.4083	5.0512	8.7245	–0.0573
	Eq. (4)	0.703	0.13	–	–	–9.1088	12.1369	–0.4759	5.3858	–0.0002	0.0350	0.0436	0.4430	0.0284	8.5408	–1.3642E–06
	Eq. (5)	0.5375	0.2806	–	–	–9.1821	12.6252	–0.4589	5.3438	–0.0001	0.0347	0.0434	0.4496	0.0122	8.5093	–2.4915E–07
	Eq. (6)	0.702	0.195	–	–	–9.3560	11.8866	–0.2431	5.3834	0.0014	0.0350	0.0436	0.4446	0.2444	8.5338	1.0100E–04
	Eq. (7)	0.29	0.52	–	–	–21.6360	20.0927	0.6092	7.1234	0.0057	0.0461	0.0591	0.4491	0.7449	11.5213	0.0023
	Eq. (8)	0.233	0.591	–	–	–21.4811	24.9780	–2.2279	8.0954	–0.0133	0.0523	0.0661	0.4491	1.5792	12.6876	–0.0141

Tab.5 Correlation coefficients and statistical performances of the eight regression models

Regcoeff	Models of station data						Models of NASA–SSE data						Models of HelioClim–1 data
Regcoeff	Mo. 9	Mo. 10	Mo. 11	Mo. 12	Mo. 13	Mo. 14	Mo. 9	Mo. 10	Mo. 11	Mo. 12	Mo. 13	Mo. 14	Mo. 9	Mo. 10	Mo. 11	Mo. 12	Mo. 13	Mo. 14
a₀	0.6963	–	–	4402	–	–	0.6458	–	–	1294	–	–	0.6534	–	–	29.79	–
a₁	–0.00074	0.7422	0.8563	–1172	11.58	0.1217	–0.0025	0.7535	7.548	–385.3	–22.23	0.3022	0.01131	0.686	1.438	–7.682	0.6701	0.4122
a₂	0.02253	–	0.178	128.9	–96.05	0.6242	–0.02884	–	6.902	48.03	31.08	0.6405	0.01168	–	0.7934	1.751	–1.336	0.05613
a₃	0.01606	–	0.09682	–7.475	228.1	0.6872	–0.00013	–	0.0122	–3.178	–39.61	0.2254	0.02589	–	0.02395	–0.1923	0.906	0.09885
a₄	0.003151	–	–0.0087	0.2411	52.52	0.1285	0.009874	–	0.0165	0.1177	37.54	24.59	–0.0054	–	0.02291	0.01068	–0.2057	0.1153
a₅	–0.0115	–	0.02008	–0.0041	–	8.04E+ 11	0.001152	–	–0.0044	–0.0023	–	0.2433	0.01102	–	0.03334	–0.00029	–	0.102
a₆	0.01066	–	0.01588	0.00003	–	2925000	0.003111	–	0.0193	0.00002	–	0.3812	–0.01144	–	0.01143	2.962E–06	–	1.769
a₇	0.02115	–	0.1068	–	–	0.06147	0.00835	–	0.0138	–	–	0.3109	–0.01135	–	0.01332	–	–	–0.1321
b₁	0.00219	1.888	4.41	–	15.08	0.7144	0.009219	1.699	3.761	–	–20.19	0.717	–0.03141	1.899	6.209	–	–2.009	1.008
b₂	–0.0092	–	11.32	–	86.32	0.9052	0.002379	–	3.969	–	–18.45	0.9054	–0.01288	–	8.912	–	1.373	0.8028
b₃	–0.01583	–	35.8	–	176.6	0.6086	0.002423	–	39.71	–	49.14	0.7551	0.01185	–	32.61	–	–0.3138	0.7064
b₄	0.005144	–	72.99	–	–	0.7598	0.00031	–	73.03	–	–	–0.4112	0.01217	–	59.46	–	–	0.761
b₅	0.00462	–	78.42	–	–	4.135	0.009481	–	47.45	–	–	0.7825	0.005365	–	41.72	–	–	0.6211
b₆	0.00673	–	52.33	–	–	–0.1472	0.001067	–	208.5	–	–	0.6594	0.007182	–	381.3	–	–	–1.037
b₇	–0.0051	–	38.09	–	–	0.8107	0.02605	–	379	–	–	0.5835	0.002506	–	221.2	–	–	0.6939
c₁	–	–	4.703	–	–	0.01086	–	–	5.624	–	–	0.02825	–	–	3.386	–	–	0.284
c₂	–	–	3.03	–	–	0.1335	–	–	8.639	–	–	0.165	–	–	4.618	–	–	0.02634
c₃	–	–	–4.667	–	–	0.1986	–	–	–10.68	–	–	0.008536	–	–	–2.899	–	–	0.04647
c₄	–	–	–5.141	–	–	0.049	–	–	–13.74	–	–	0.4753	–	–	–3.996	–	–	0.002403
c₅	–	–	2.029	–	–	0.5788	–	–	–5.234	–	–	0.04859	–	–	0.2423	–	–	0.02338
c₆	–	–	1.573	–	–	0.1578	–	–	1.886	–	–	0.04607	–	–	–0.3453	–	–	1.501
c₇	–	–	3.227	–	–	0.01254	–	–	1.007	–	–	0.05624	–	–	–9.633	–	–
w	96.42	–	–	–	–	–	212	–	–	–	–	–	13.85	–	–	–	–

Tab.6 Regression coefficients of six proposed models

Meteorology data	Statistical indicators	New equations of proposed models
Meteorology data	Statistical indicators	Eq. (9)	Eq. (10)	Eq. (11)	Eq. (12)	Eq. (13)	Eq. (14)
Station	RPEmin% RPEmax% MPE MAPE MBE MABE RMSE R² TT U₉₅ GPI	–8.7868 10.8271 –0.3111 4.5037 0.0000 0.0306 0.0376 0.5981 0.0072 8.0037 –3.0582E–08	–12.2603 11.6486 –0.3112 5.2925 0.0007 0.0361 0.0452 0.3059 0.1118 6.0573 1.3992E–05	–8.8033 10.5034 –0.3011 4.8479 0.0003 0.0332 0.0402 0.5160 0.0623 6.8463 2.7062E–06	–9.6444 11.3849 –0.4218 5.1232 0.0001 0.0349 0.0442 0.3365 0.0023 4.5149 3.0183E–08	–9.7259 11.5121 –0.4310 5.1138 –0.0001 0.0348 0.0441 0.3414 0.0161 4.6272 –1.9963E–07	–9.7897 11.3622 –0.3457 4.5551 –0.0001 0.0310 0.0384 0.5763 0.0285 7.4887 –4.9416E–07
NASA–SSE	RPEmin% RPEmax% MPE MAPE MBE MABE RMSE R² TT U₉₅ GPI	–8.8038 7.3424 –0.2431 3.9024 0.0002 0.0252 0.0338 0.6225 0.0381 6.6317 –5.4049E–07	–12.5628 11.4088 –0.3536 5.3700 0.0005 0.0347 0.0438 0.0966 0.0898 8.5784 –1.5570E–05	–9.2753 13.1436 –0.6665 4.5771 –0.0020 0.0294 0.0383 0.4675 0.4098 7.4913 1.2759E–04	–13.6595 10.8567 –0.4395 5.3002 –0.0000 0.0343 0.0428 0.1398 0.0045 8.3906 3.4402E–08	–13.6008 11.1812 –0.4709 5.2580 –0.0002 0.0340 0.0428 0.1467 0.0409 8.3825 2.8482E–06	–12.2355 11.8519 –0.3677 4.6116 –0.0001 0.0298 0.0387 0.4458 0.0142 7.5854 1.6525E–07
HelioClim–1	RPEmin% RPEmax% MPE MAPE MBE MABE RMSE R² TT U₉₅ GPI	–8.8906 11.3943 –0.3680 4.8063 0.0000 0.0313 0.0392 0.5920 0.0068 7.6779 –4.5816E–08	–13.6466 12.1245 –0.3351 5.7196 0.0008 0.0372 0.0463 0.3487 0.1356 9.0795 4.0944E–05	–10.3020 9.8321 –0.3585 4.8717 0.0000 0.0316 0.0393 0.5882 0.0040 7.7061 1.6329E–08	–9.3159 12.8458 –0.4539 5.3350 0.0000 0.0346 0.0434 0.4506 0.0067 8.5047 –7.5570E–08	–15.2999 10.4957 4.1193 7.8330 0.0287 0.0507 0.0980 0.2961 2.3556 18.3664 0.1112	–8.7121 12.6391 –0.3141 4.1810 0 0.0274 0.0366 0.6573 0.0004 7.1796 –1.112E–10

Tab.7 Statistical performances of new models

Fig.3 Measurements and estimates of eight models of typical months from 2000 to 2004

Fig.4 Measurements and estimates of six proposed models of typical months from 2000 to 2004

Tab.8 Ranking of models according to each statistic for three meteorological databases

Tab.9 Accuracy of models for computing monthly global solar radiation at Tamanrasset

Fig.5 Measured GSR on training and testing data using SHBM1, SHBM2, SHBM3, and SHBM4

Fig.6 Scatter plots of measured data against predicted monthly GSR by using SHBM1, SHBM2, SHBM3, and SHBM4 for the year 2004, at Tamanrasset in Algeria

Fig.7 Relative percentage error in different months throughout the five years for the hybrid model

Tab.10 Regression coefficients of new smart hybrid models

1	A Angstrom. Solar and terrestrial radiation. Report to the international commission for solar research on actinometric investigations of solar and atmospheric radiation. Quarterly Journal of the Royal Meteorological Society, 1924, 50(210): 121–126 https://doi.org/10.1002/qj.49705021008
2	J A Prescott. Evaporation from water surface in relation to solar radiation. Transactions of the Royal Society of Australia, 1940, 64: 114–125
3	J Glover, J S J McCulloch. The empirical relation between solar radiation and hours of sunshine. Quarterly Journal of the Royal Meteorological Society, 1958, 84(360): 172–175 https://doi.org/10.1002/qj.49708436011
4	M Chegaar, A Chibani. Global solar radiation estimation in Algeria. Energy Conversion and Management, 2001, 42(8): 967–973 https://doi.org/10.1016/S0196–8904(00)00105–9
5	M Salmi, M C P Mialhe. A collection of models for the estimation of global solar radiation in Algeria. Energy Sources Part B Economics Planning & Policy, 2011, 6(2): 187–191
6	M Koussa, A Malek, M Haddadi. Statistical comparison of monthly mean hourly and daily diffuse and global solar irradiation models and a Simulink program development for various Algerian climates. Energy Conversion and Management, 2009, 50(5): 1227–1235 https://doi.org/10.1016/j.enconman.2009.01.035
7	M S Mecibah, T E Boukelia, R Tahtah, K Gairaa. Introducing the best model for estimation the monthly mean daily global solar radiation on a horizontal surface (case study: Algeria). Renewable & Sustainable Energy Reviews, 2014, 36(5): 194–202 https://doi.org/10.1016/j.rser.2014.04.054
8	T Khatib, A Mohamed, K Sopian. A review of solar energy modeling techniques. Renewable & Sustainable Energy Reviews, 2012, 16(5): 2864–2869 https://doi.org/10.1016/j.rser.2012.01.064
9	A Mellit, S A Kalogirou, S Shaari, H Salhi, A Hadj Arab. Methodology for predicting sequences of mean monthly clearness index and daily solar radiation data in remote areas: application for sizing a stand-alone PV system. Renewable Energy, 2008, 33(7): 1570–1590 https://doi.org/10.1016/j.renene.2007.08.006
10	R Yacef, A Mellit, S Belaid, Z Şen. New combined models for estimating daily global solar radiation from measured air temperature in semi-arid climates: application in Ghardaia, Algeria. Energy Conversion and Management, 2014, 79: 606–615
11	C Voyant, M Muselli, C Paoli, M L Nivet. Numerical weather prediction (NWP) and hybrid ARMA/ANN model to predict global radiation. Energy, 2012, 39(1): 341–355 https://doi.org/10.1016/j.energy.2012.01.006
12	Y Wu, J Z Wang. A novel hybrid model based on artificial neural networks for solar radiation prediction. Renewable Energy, 2016, 89: 268–284 https://doi.org/10.1016/j.renene.2015.11.070
13	Y S Güçlü, I Dabanli, E Sisman, Z Sen. HARmonic-LINear (HarLin) model for solar irradiation estimation. Renewable Energy, 2015, 81: 209–218 https://doi.org/10.1016/j.renene.2015.03.035
14	G E Hassan, M E Youssef, Z E Mohamed, M A Ali, A A Hanafy. New temperature-based models for predicting global solar radiation. Applied Energy, 2016, 179: 437–450 https://doi.org/10.1016/j.apenergy.2016.07.006
15	T R Ayodele, A S O Ogunjuyigbe, H Lund, M J Kaiser. Prediction of monthly average global solar radiation based on statistical distribution of clearness index. Energy, 2015, 90: 1733–1742
16	S Belaid, A Mellit. Prediction of daily and mean monthly global solar radiation using support vector machine in an arid climate. Energy Conversion and Management, 2016, 118: 105–118 https://doi.org/10.1016/j.enconman.2016.03.082
17	L Zou, L Wang, L Xia, A Lin, B Hu, H Zhu. Prediction and comparison of solar radiation using improved empirical models and Adaptive Neuro–Fuzzy Inference Systems. Renewable Energy, 2017, 106(5): 343–353 https://doi.org/10.1016/j.renene.2017.01.042
18	L Zou, L Wang, A Lin, H Zhu, Y Peng, Z Zhao. Estimation of global solar radiation using an artificial neural network based on an interpolation technique in southeast China. Journal of Atmospheric and Solar–Terrestrial Physics, 2016, 146: 110–122 https://doi.org/10.1016/j.jastp.2016.05.013
19	J A Duffie, W A Beckman, J Mcgowan. Solar engineering of thermal processes. Journal of Solar Energy Engineering, 1980, 116(1): 549
20	B G Akinoğlu, A A Ecevit. A further comparison and discussion of sunshine based models to estimate global solar radiation. Energy, 1990, 15(10): 865–872 https://doi.org/10.1016/0360–5442(90)90068–D
21	V Bahel, H Bakhsh, R Srinivasan. A correlation for estimation of global solar radiation. Energy, 1987, 12(2): 131–135 https://doi.org/10.1016/0360–5442(87)90117–4
22	D B Ampratwum, A S S Dorvlo. Estimation of solar radiation from the number of sunshine hours. Applied Energy, 1999, 63(3): 161–167 https://doi.org/10.1016/S0306–2619(99)00025–2
23	N A Elagib, M G Mansell. New approaches for estimating global solar radiation across Sudan. Energy Conversion and Management, 2000, 41(5): 419–434 https://doi.org/10.1016/S0196–8904(99)00123–5
24	K Bakirci. Correlations for estimation of daily global solar radiation with hours of bright sunshine in Turkey. Energy, 2009, 34(4): 485–501 https://doi.org/10.1016/j.energy.2009.02.005
25	V Badescu, C A Gueymard, S Cheval, C Oprea, M Baciu, A Dumitrescu, F Iacobescu, I Milos, C Rada. Accuracy and sensitivity analysis for 54 models of computing hourly diffuse solar irradiation on clear sky. Theoretical and Applied Climatology, 2013, 111(3–4): 379–399 https://doi.org/10.1007/s00704–012–0667–1
26	O Behar, A Khellaf, K Mohammedi. Comparison of solar radiation models and their validation under Algerian climate–the case of direct irradiance. Energy Conversion and Management, 2015, 98(1): 236–251 https://doi.org/10.1016/j.enconman.2015.03.067

[1]	Muyiwa S. ADARAMOLA. Distribution and temporal variability of the solar resource at a site in south-east Norway[J]. Front. Energy, 2016, 10(4): 375-381.
[2]	O. S. OHUNAKIN, M. S. ADARAMOLA, O. M. OYEWOLA, R. O. FAGBENLE. Correlations for estimating solar radiation using sunshine hours and temperature measurement in Osogbo, Osun State, Nigeria[J]. Front Energ, 2013, 7(2): 214-222.

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