Hybrid deep learning CNN-LSTM model for forecasting direct normal irradiance: a study on solar potential in Ghardaia, Algeria

Abstract

Abstract This paper provides an in-depth analysis and performance evaluation of four Solar Radiance (SR) prediction models. The prediction is ensured for a period ranging from a few hours to several days of the year. These models are derived from four machine learning methods, namely the Feed-forward Back Propagation (FFBP) method, Convolutional Feed-forward Back Propagation (CFBP) method, Support Vector Regression (SVR), and the hybrid deep learning (DL) method, which combines Convolutional Neural Networks and Long Short-Term Memory networks. This combination results in the CNN-LSTM model. Additionally, statistical indicators use Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), Mean Absolute Percentage Error (MAPE), and Normalized Root Mean Squared Error (nRMSE). Each indicator compares the predicted output by each model above and the actual output, pre-recorded in the experimental trial. The experimental results consistently show the power of the CNN-LSTM model compared to the remaining models in terms of accuracy and reliability. This is due to its lower error rate and higher detection coefficient (R2 = 0.99925).

Keywords

• Artificial neural networks
• Convolutional neural network
• Convolutional feed-forward back propagation
• Deep learning
• Feed-forward back propagation
• Long short-term memory