针对现有方法在光伏组件温度预测中预测精度不足、季节适应性差及物理可解释性弱的问题，本文提出一种融合非稳态热模型与TRANSFORMER的混合预测方法。该方法通过结合物理机制与数据驱动建模，在显著提升预测精度的同时，增强了模型的季节适应性与鲁棒性。首先，基于热平衡方程建立光伏组件-通风流道-屋顶的非稳态集成传热模型，解析光伏组件与周围环境的非稳态热过程；随后，将计算得到的背板温度与通风流道温度作为物理特征输入TRANSFORMER模型，并与气象数据联合训练，以修正非稳态模型的系统误差并增强非线性时序建模能力。实验对比了经验模型、传热模型、神经网络模型及混合模型的预测性能。结果表明，所提混合模型相比未将物理特征输入的神经网络模型，在光伏组件温度预测中均方根误差（RMSE）与平均绝对误差（MAE）分别降低了25.81~66.24%与10.53~65.63%。该方法可为光伏发电功率的精准预测与组件运行策略的优化调整提供更可靠的指导。

Aiming at the problems of insufficient prediction accuracy and poor seasonal adaptability of the existing methods in the temperature prediction of photovoltaic modules, this paper proposes a hybrid prediction method that integrates a transient thermal model with a TRANSFORMER. This method, by integrating physical mechanisms with data-driven modeling, significantly improves the performance while enhancing the seasonal adaptability and robustness. Initially, a transient integrated heat transfer model for the PV module-ventilation channel-roof system is established based on the energy balance equation, and the transient thermal process between the PV module and its surrounding environment is solved. Subsequently, the calculated backplane temperature and ventilation channel temperature are used as feature input for the TRANSFORMER, which is jointly trained with meteorological data to correct the systematic errors of the transient model and enhance the nonlinear time-series modeling capability. The prediction performance of empirical models, theoretical models, neural network models, and the hybrid model is reviewed. The results indicate that the proposed hybrid model reduces the root mean square error (RMSE) and mean absolute error (MAE) in photovoltaic module temperature prediction by 25.81–66.24% and 10.53–65.63%, respectively, compared to the neural network model without physical feature inputs. This method can provide more reliable support for the precise prediction of photovoltaic power and the optimization and adjustment of module operation strategies.