@proceedings {7246,
	title = {Residual Networks with Long Short Term Memory for Hourly Solar Radiation Forecasting},
	journal = {International Conference on Artificial Intelligence in Renewable Energetic Systems (IC-AIRES{\textquoteright}21) },
	year = {2021},
	address = {Tipasa, Algeria},
	abstract = {
	This paper describes a new approach for hourly global solar radiation forecasting based on a hybrid artificial neural network technique combining a residual neural network (RESNET) for powerful feature extraction of the most relevant moments of the past, and a long short-term memory (LSTM) technique for efficient projection into the future. Based on 11 years of solar irradiance measurements at Tamanrasset, Algeria, four evaluation metrics are used to demonstrate the efficiency of the proposed method: coefficient of determination (R{\texttwosuperior}), root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE). These metrics are also used to evaluate the performance of the model in comparison with two existing forecasting models used as benchmark: a particular technique of convolutional neural network (CNN) called 1-dimensional convolutional neural network (1D-CNN) and a conventional LSTM. The present results indicate that the proposed RESNET-LSTM model outperforms the other models in terms of all statistical indicators.

},
	url = {https://www.researchgate.net/publication/355916572_Residual_Networks_with_Long_Short_Term_Memory_for_Hourly_Solar_Radiation_Forecasting},
	author = {Louchene, Houssem-Eddine and Bouzgou, Hassen and Gueymard, Chris}
}
