In this study, machine learning methods of artificial neural networks (ANNs), least squares support vector machines (LSSVM), and neuro-fuzzy are used for advancing prediction models for thermal performance of a photovoltaic-thermal solar collector (PV/T). In the proposed models, the inlet temperature, flow rate, heat, solar radiation, and the sun heat have been considered as the input variables. Data set has been extracted through experimental measurements from a novel solar collector system. Different analyses are performed to examine the credibility of the introduced models and evaluate their performances. The proposed LSSVM model outperformed the ANFIS and ANNs models. LSSVM model is reported suitable when the laboratory measurements are costly and time-consuming, or achieving such values requires sophisticated interpretations.

在这项研究中，人工神经网络（ANN），最小二乘支持向量机（LSSVM）和神经模糊的机器学习方法被用于推进光伏热太阳能收集器（PV / T）的热性能预测模型。在提出的模型中，入口温度，流速，热量，太阳辐射和太阳热已被视为输入变量。通过实验测量从新型太阳能收集器系统中提取了数据集。进行了不同的分析，以检查所引入模型的可信度并评估其性能。提出的LSSVM模型优于ANFIS和ANNs模型。当实验室测量成本高昂且耗时，或者要达到这样的值需要复杂的解释时，则认为LSSVM模型适用。