Experimental based multilayer perceptron approach for prediction of evacuated solar collector performance in humid subtropical regions

• Published in: Renewable energy Vol. 143; pp. 1566 - 1580
• Main Authors: Bhowmik, Mrinal, Muthukumar, P., Anandalakshmi, R.
• Format: Journal Article
• Language: English
• Published: Elsevier Ltd 01.12.2019
• Subjects: Experimental analysis; Multilayer perceptron; Thermal efficiency; Trade-off analysis; U tube-evacuated solar collectors; Useful heat gain; Useful heat gain; Multilayer perceptron; Thermal efficiency; Trade-off analysis; U tube-evacuated solar collectors; Experimental analysis
• ISSN: 0960-1481; 1879-0682
• DOI: 10.1016/j.renene.2019.05.093

Solar collectors are efficient in utilising solar thermal energy for heating applications as their efficiency is quite high even in the medium temperature range, which motivated to design a high efficient collector system. In this study, an experimental investigation is carried out by developing a series of evacuated tube solar collectors with U-tube configuration using water as a working fluid. The performance of the collector system is continuously measured throughout the day. A trade-off study is carried out considering all the performance parameters. On the basis of experimental datasets, a multilayer perceptron (MLP) architecture is developed to predict thermal efficiency, useful heat gain and water outlet temperature of the evacuated tube collector as a function of solar irradiation, mass flow rate of water and water inlet temperature. It is demonstrated that the MLP model has an excellent agreement with experimental data as the mean square error is very low (<0.001). Test results showed that the MLP architecture gives a precise prediction of the actual collector performance parameters for different operating conditions. Test results also indicate that MLP model is a robust prediction platform for evaluating the solar collector performance. •Performance of U-tube evacuated solar collectors with aluminium fins is studied.•Variation in thermal efficiency and useful heat gain are studied throughout a day.•AI-based MLP model is developed between operational and performance parameters.•RMSE, MSE, nRMSE, MSRE, U2 THEIL, MAPE and R, NSE, KGE, R2 metrics are calculated.•Feasibility of AI-based MLP model is validated using measured output datasets.