Table 9 Baseline forecasting performance of deep learning models before feature selection (solar forecasting).

From: Optimizing solar and wind forecasting with iHow optimization algorithm and multi-scale attention networks

Model

MSE

RMSE

MAE

MBE

\(\varvec{r}\)

\(\varvec{R^2}\)

RRMSE

NSE

WI

MSAN

0.0976

0.0508

0.0501

0.0474

0.8173

0.8299

22.48

0.8480

0.8433

LSTM

0.2677

0.1186

0.1102

0.1720

0.7774

0.7900

24.68

0.8387

0.7240

GRU

0.7186

0.1308

0.1209

0.1914

0.6701

0.6827

25.52

0.7858

0.6681

GANT

0.7539

0.1429

0.1316

0.1969

0.6338

0.6464

26.10

0.7617

0.6041

ARN

0.7927

0.1468

0.1355

0.9816

0.6153

0.6278

26.53

0.7358

0.5377

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