6533b821fe1ef96bd127b736

RESEARCH PRODUCT

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

Bin DuPeter LundPeter LundJun WangMohan KolheEric Hu

subject

Thermal efficiencyArtificial neural networkRenewable Energy Sustainability and the Environment020209 energyEnergy Engineering and Power Technology02 engineering and technologyMechanicsWind speedBackpropagationSupport vector machine020401 chemical engineeringThermalLinear regression0202 electrical engineering electronic engineering information engineeringMass flow rateEnvironmental science0204 chemical engineering

description

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.

yearjournalcountryeditionlanguage
2021-04-01Sustainable Energy Technologies and Assessments
https://doi.org/10.1016/j.seta.2021.101029