Forecasting of thermal energy storage performance of Phase Change Material in a solar collector using soft computing techniques

摘要

The performance of a solar collector system using sodium carbonate decahydrate (Na2CO3·10H2O) as Phase Change Material (PCM) was experimentally investigated during March and collector efficiency was compared with those of convectional system including no PCM. We also made a series of predictions by using three different soft computing techniques as Artificial Neural Networks (ANN), Adaptive-Network-Based Fuzzy Inference System (ANFIS) and Support Vector Machines (SVM). It was found that the solar collector system with PCM is more effective than convectional systems. Soft computing techniques can be used to model of a solar collector with PCM. Furthermore, analysis of soft computing showed that SVM technique gives the best results than that of ANFIS and ANN.