Table 4 Under sunny days, forecasting performance comparison across different season.

From: The development of CC-TF-BiGRU model for enhancing accuracy in photovoltaic power forecasting

Forecasting model

Evaluation index

Different seasons

Spring

Summer

Autumn

Winter

Average

#1

MAE

3.7874

3.3705

3.7874

3.0290

3.4935

RMSE

4.0621

4.0681

4.0621

3.424

3.9040

R2

0.9054

0.9072

0.9054

0.9167

0.9086

#2

MAE

3.0198

4.7597

1.931

3.3194

3.2574

RMSE

3.595

5.8637

2.5392

3.7818

3.9449

R2

0.926

0.7877

0.963

0.8985

0.8938

#3

MAE

2.5932

1.3329

2.0387

1.8144

1.9448

RMSE

3.103

1.9001

2.93

2.2253

2.5396

R2

0.9448

0.9798

0.9508

0.9648

0.9600

#4

MAE

1.0181

0.611

0.95

1.3684

0.9868

RMSE

1.6216

0.8586

1.3427

1.9525

1.4438

R2

0.9849

0.9858

0.9886

0.9719

0.9828

#5

MAE

0.95

1.45

1.0181

1.2188

1.1592

RMSE

1.3427

1.7243

1.6216

1.8638

1.6381

R2

0.9897

0.9833

0.9849

0.9753

0.9833

#6

MAE

1.9387

1.9849

2.0197

2.3645

2.0769

RMSE

2.8301

2.9213

2.9703

3.1289

2.9626

R2

0.9608

0.9587

0.9548

0.9359

0.9525

#7

MAE

1.5888

2.455

2.4494

2.4413

2.2336

RMSE

2.0993

3.1126

3.1029

3.0565

2.8428

R2

0.9547

0.9457

0.9447

0.9337

0.9447

#8

MAE

2.0494

1.4445

2.4494

2.3751

2.0796

RMSE

2.9021

1.8177

3.1021

3.0902

2.7280

R2

0.9548

0.9615

0.9448

0.9484

0.9523

#9

MAE

1.3308

1.0469

1.5887

1.5251

1.3728

RMSE

1.9391

1.8818

2.0993

2.6349

2.1387

R2

0.973

0.9801

0.9747

0.9507

0.9696

#10

MAE

0.8845

0.3506

0.8845

0.8207

0.7350

RMSE

1.2459

0.5184

1.2461

1.0931

1.0258

R2

0.9911

0.9914

0.9911

0.9889

0.9906

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