基于BP神经网络的光伏阵列温度预测

针对现有太阳能光伏阵列仿真实验中因采用环境温度代替光伏组件温度而导致的光伏阵列建模不正确问题,指出应在光伏电池仿真模型中区分环境温度和组件的实际工作温度;分析了光伏组件温度与环境温度和输出功率的关系,给出了一种基于BP神经网络的光伏阵列组件温度预测方法,并将预测结果与实测结果进行比较,得出结论:该方法可有效预测光伏阵列组件温度,且采用前一天数据和前三天数据都有较好的预测效果,因此实际应用时可采用前一天的数据来预测当天的组件温度。

Temperature Prediction of Photovoltaic Array Based on BP Neural Network

For problem of incorrect modeling method which takes environmental temperature instead of photovoltaic array temperature in existing simulation of solar photovoltaic arrays,the paper pointed out environmental temperature and actual working temperature of photovoltaic array should be distinguished in modelling of photovoltaic arrays.It analyzed relationship between temperature of photovoltaic array and environmental temperature as well as output power,and proposed a temperature prediction method of photovoltaic array based on BP neural network.At last,it compared predicting result with the measured one and got following conclusions: the method can predict temperature of photovoltaic array effectively and get better effect by use of data of the last day and the last three days.According to the conclusion,temperature of photovoltaic array of the day can be predicted by use of data of the last day in actual application.