In this paper, an artificial neural network (ANN) is developed to assess hybrid photovoltaic thermal (PVT) systems for grid-connected (GC) electricity generation, space heating and domestic hot water providing in heating dominated regions of Iran. To do so, monthly and annual performance of a 5 kWp GCPVT system is simulated for a single-family house. The simulation results show that the GCPVT system is very promising whereas the annual yield factor varies from 1506 kWh/kWp to 1891 kWh/kWp. Also, an appropriate solar fractions for covering hot water are achieved in a range from 74.5% to 49.4%. A multilayered perceptron feed-forward neural network which is trained by Levenberg-Marquardt algorithm is used to predict AC electrical energy and solar thermal output of the GCPVT system. The developed ANN is based on global horizontal irradiance, ambient temperature, ambient relative humidity and wind speed as inputs. The proposed configuration of ANN presents a high accuracy in predicting output energy of the GCPVT system according to minimum mean square error and maximum correlation coefficient. Analysis of variance is performed to determine the significant control parameters influencing the output energy of the GCPVT system.

在本文中，开发了一个人工神经网络（ANN）来评估混合光伏热（PVT）系统，用于伊朗主要供热地区的并网（GC）发电，空间供暖和生活热水供应。为此，针对单户住宅模拟了5 kWp GCPVT系统的月度和年度性能。仿真结果表明，GCPVT系统非常有前途，而年发电系数从1506 kWh / kWp到1891 kWh / kWp不等。另外，用于覆盖热水的合适的太阳能分数在74.5％至49.4％的范围内实现。由Levenberg-Marquardt算法训练的多层感知器前馈神经网络用于预测GCPVT系统的交流电能和太阳热能输出。所开发的人工神经网络基于整体水平辐照度，环境温度，环境相对湿度和风速作为输入。拟议的人工神经网络配置根据最小均方误差和最大相关系数，在预测GCPVT系统的输出能量方面具有很高的准确性。进行方差分析以确定影响GCPVT系统输出能量的重要控制参数。