Table 22  Parameters estimated by the algorithms (Scenario 4) under various temperature conditions.

From: Leveraging opposition-based learning for solar photovoltaic model parameter estimation with exponential distribution optimization algorithm

Temperature

Algorithm

\({I}_{ph}\)(A)

\({I}_{sd}\)(A)

\({R}_{se}\)(Ω)

\({R}_{sh}\)(Ω)

\(n\)

RMSE

60 °C

OBEDO

3.4946

6.91E−06

0.3187

484.88

1.4051

3.7804E−03

EDO

3.4785

5.75E−06

0.3335

4811.41

1.3853

8.6436E−03

ADHHO

3.4903

3.82E−05

0.2248

5000.00

1.6138

1.2592E−02

OBMPA

3.4829

1.03E−05

0.3064

4980.33

1.4486

6.0617E−03

MGTO

3.4946

6.91E−06

0.3187

484.88

1.4051

3.7804E−03

IAOA

3.4933

3.22E−05

0.2400

1863.55

1.5905

1.1925E−02

HDE

3.4905

1.15E−05

0.2954

875.64

1.4609

4.9433E−03

OBGBO

3.4946

6.91E−06

0.3187

484.88

1.4051

3.7804E−03

40 °C

OBEDO

3.4691

1.15E−06

0.3131

533.07

1.4178

3.7888E−03

EDO

3.4674

9.19E−06

0.2120

1577.15

1.6465

1.4522E−02

ADHHO

3.4649

1.08E−05

0.2048

5000.00

1.6668

1.5092E−02

OBMPA

3.4596

3.88E−06

0.2602

4987.71

1.5429

8.2396E−03

MGTO

3.4686

1.20E−06

0.3113

548.28

1.4221

3.7986E−03

IAOA

3.4752

1.09E−05

0.1825

604.29

1.6684

1.9305E−02

HDE

3.4593

3.39E−06

0.2696

5000.00

1.5282

7.5246E−03

OBGBO

3.4664

1.23E−06

0.3110

629.96

1.4244

3.9181E−03

25 °C

OBEDO

3.4501

1.71E−07

0.3291

483.90

1.3958

1.1462E−03

EDO

3.4496

1.18E−05

0.1258

3571.99

1.8625

2.5065E−02

ADHHO

3.4459

6.94E−06

0.1566

5000.00

1.7871

2.0923E−02

OBMPA

3.4404

1.80E−06

0.2347

5000.00

1.6210

1.2037E−02

MGTO

3.4435

5.91E−07

0.2834

1320.00

1.5055

5.8714E−03

IAOA

3.4644

4.28E−06

0.1941

431.22

1.7257

2.4107E−02

HDE

3.4400

1.81E−06

0.2285

5000.00

1.6218

1.2356E−02

OBGBO

3.4457

2.26E−07

0.3207

658.48

1.4191

2.2319E−03

  1. Significant values are in [bold].