Table 5 Under cloudy days, forecasting performance comparison.

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

3.7711

4.6299

2.9612

3.5588

3.7302

RMSE

5.3943

5.1636

5.1325

5.5549

5.3113

R2

0.7445

0.7777

0.8436

0.8112

0.794

#2

MAE

3.4513

3.3685

2.6124

3.5409

3.2432

RMSE

4.5973

4.6213

4.7952

5.4677

4.8703

R2

0.8324

0.8462

0.8716

0.8171

0.8418

#3

MAE

2.3317

2.4023

1.8651

2.0326

2.1579

RMSE

3.1707

3.3718

3.0854

3.8459

3.3684

R2

0.9117

0.9052

0.9497

0.9197

0.9215

#4

MAE

1.3951

2.0594

1.6067

1.9654

1.7566

RMSE

1.9471

2.7138

2.8608

2.9985

2.6300

R2

0.9652

0.9386

0.9567

0.9368

0.9493

#5

MAE

1.8038

1.9849

1.6997

1.8876

1.8440

RMSE

2.1222

3.0391

2.6935

2.9283

2.6957

R2

0.9604

0.9231

0.9616

0.9475

0.9481

#6

MAE

3.3275

2.7471

2.2336

2.2426

2.6377

RMSE

4.0496

3.8679

3.5726

4.3139

3.9510

R2

0.8561

0.8753

0.9325

0.8861

0.8875

#7

MAE

2.4522

2.5701

2.3129

2.0554

2.3476

RMSE

3.2704

3.6044

4.3978

3.6138

3.7216

R2

0.9034

0.8917

0.8977

0.9201

0.9032

#8

MAE

2.1327

2.3518

2.5333

2.5157

2.3833

RMSE

2.9861

4.0381

4.3576

4.2435

3.9063

R2

0.9235

0.8641

0.8996

0.8937

0.8952

#9

MAE

1.7825

2.2411

1.5858

1.6325

1.8104

RMSE

2.6146

3.4098

2.5239

2.4137

2.7405

R2

0.9399

0.9031

0.9663

0.9513

0.9401

#10

MAE

1.2013

1.0331

1.4677

1.4669

1.2922

RMSE

1.5224

1.3312

1.9215

2.1252

1.7250

R2

0.9787

0.9772

0.9805

0.9723

0.9771

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