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针对光伏光热综合利用(PV/T)系统热电协调控制中组件温度控制非线性大惯性系统的温度控制问题,文中提出对PV/T组件进行短期温度预测,以使PV/T系统控制器根据短期预测情况提前动作,从而优化PV/T系统控制效果。文中分析了A类天气类型(晴天、晴间多云、多云间晴)下组件温度的变化情况,并结合RBF神经网络对组件温度数据建立预测模型。仿真实例分析结果表明,该预测方法在A类天气类型下预测精度较高,最大相对误差为13.22%,最大平均相对误差为3.6%。研究结果可为后续PV/T系统研究提供技术支撑。
Aiming at the temperature control of component temperature control nonlinear inertia system in photovoltaic thermoelectric utilization (PV / T) system, a short-term temperature prediction of PV / T module is proposed in this paper, so that PV / T system controller can predict the short- Short-term prediction of the situation in advance action, thus optimizing the PV / T system control effect. In this paper, the changes of the temperature of the components under the type A weather (sunny, sunny and cloudy) are analyzed, and the prediction model of the component temperature is established by combining the RBF neural network. The simulation results show that the prediction accuracy of this prediction method is higher under Type A weather conditions, with the maximum relative error of 13.22% and the maximum average relative error of 3.6%. The results provide technical support for the subsequent PV / T system research.