论文标题
评估光伏热太阳能收集器的电效率
Evaluation of electrical efficiency of photovoltaic thermal solar collector
论文作者
论文摘要
太阳能是一种可再生能源的资源，可在可再生能源中广泛使用，并且发射最少。在这项研究中，使用人工神经网络（ANN），最小二乘支持向量机（LSSVM）和神经模糊的机器学习方法用于推进光伏热量太阳能收集器（PV/T）热性能的预测模型。在提议的模型中，入口温度，流速，热量，太阳辐射和太阳热已被视为输入变量。数据集已通过新型太阳能收集器系统的实验测量提取。进行了不同的分析以检查引入方法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，或达到此类值需要复杂的解释时，LSSVM模型的报告适用于。
Solar energy is a renewable resource of energy that is broadly utilized and has the least emissions among renewable energies. 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 the 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 inputs 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 approaches and evaluate their performance. The proposed LSSVM model outperformed 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.
