A Hybrid Deep Learning-Based Network for Photovoltaic Power Forecasting

Table 5

The performance of the proposed model is compared with an existing state-of-the-art model for PV power forecasting. The best performance is shown in bold.

Dataset	Methods	RMSE	MSE	MAE	MBE
DKASC-AS-1B [68]	LSTM [45]	0.709	—	0.327	—
	CNN [45]	0.822	—	0.304	—
	CNN-LSTM [45]	0.693	—	0.294	—
	LSTM-CNN [45]	0.621	—	0.221	—
	GRU-CNN	0.1727	0.0298	0.0923	0.0235
DKASC-AS-2Eco [69]	LSTM [44]	1.0382	—	—	−0.084
	GRU [44]	1.0351	—	—	0.1206
	RNN [44]	1.0581	—	—	−0.1442
	MLP [44]	1.0861	—	—	0.1995
	WPD-LSTM [44]	0.2357	—	—	0.0067
	GRU-CNN	0.1646	0.0271	0.1157	−0.0641
DKASC-Yulara-SITE-3A [70]	RCC-BPNN [71]	1.173	—	—	—
	RCC-RBFNN [71]	1.37	—	—	—
	RCC-Elman [71]	1.158	—	—	—
	LSTM [71]	1.017	—	—	—
	RCC-LSTM [71]	0.94	—	0.587	—
	GRU-CNN	0.1715	0.0294	0.1126	0.0099