Comparative study of modelling the thermal efficiency of a novel straight through evacuated tube collector with MLR, SVR, BP and RBF methods. (April 2021)

• Record Type: Journal Article
• Authors: Du, Bin; Lund, Peter D.; Wang, Jun; Kolhe, Mohan; Hu, Eric
• Is Part Of: Sustainable energy technologies and assessments. Volume 44(2021)
• Publication Date: 2021-04
• Subjects: Solar collector -- Thermal efficiency -- Performance prediction -- Artificial neural network -- Support vector regression -- BP -- RBF; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2021.101029
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Performance prediction of all-glass straight through evacuated tube collector. SVR, BP and RBF models were successfully predict the thermal efficiency of the tube. The ANN models have better accuracy than the regression model. RBF model had the best accuracy for collector efficiency prediction. The effects of main input parameters on the efficiency of the tube have been investigated by RBF model prediction. Abstract: Data-based methods are useful for accurate modelling of solar thermal systems. In this work, several artificial neural network (ANN) techniques are proposed to predict the thermal performance of an all-glass straight through evacuated tube solar collector. These are compared to support vector regression analysis. Extensive experimental data sets were collected for training the ANN models. Solar radiation intensity, ambient temperature, wind speed, mass flow rate and collector inlet temperature were selected as the input layer to predict the thermal efficiency of the solar collector. The prediction precision of the ANN models was compared to the multiple linear regression and support vector regression model using different criteria. The Radial Basis Function (RBF) neural network method gave the best prediction accuracy followed by the Back Propagation (BP) model. The sensitivity of the model to changes in the input variables (solar radiation intensity, collector inlet temperature, fluid flow rate and wind speed) was also investigated showing the largest dependency on solar radiation.