Application of soft sensor modeling based on SSA-CNN-LSTM in solar thermal power collection subsystem

To address the stochasticity and nonlinearity of solar collector power systems, a soft sensor prediction model with a hybrid convolutional neural network (CNN) and long short-term memory network (LSTM) was constructed, and the hyperparameter optimization of the hybrid neural network (CNN-LSTM) was carried out by using the sparrow search algorithm (SSA). The model utilized the powerful feature extraction and non-linear mapping capabilities of deep learning to effectively handle the complex relationship between input and target variables. The batch normalization technique was used to speed up the training and improve the stability of the soft-sensing model, and the random discard technique was used to prevent the soft-sensing model from overfitting. Finally, the mean absolute error (MAE) was used to assess the accuracy of the soft sensor model predictions. This study compared the proposed model with soft sensor prediction models like Bp, Elman, CNN, LSTM, and CNN-LSTM, using dynamic thermal performance data from the solar collector field of the molten salt linear Fresnel photovoltaic demonstration power plant. The deep learning-based soft sensor model outperformed the other models according to the experimental data. Its coefficients of determination (namely R²) are higher by 6.35%, 8.42%, 5.69%, 6.90%, and 3.67%, respectively. The accuracy and robustness have been significantly improved.