Abstract: A real-time computing approach for photovoltaic module temperature via first-order inertial elementsis proposed. First, the heat transmission characteristics of photovoltaic modules are comprehensively analyzed, leading to the derivation of a simplified computation model for photovoltaic module temperature based on first-order inertial elements, utilizing solutions from one-dimensional unsteady heat conduction analysis. Next, employing a genetic algorithm and the Quasi-Newton method in serial optimization, the model parameters are expeditiously identified using a data-centric methodology. Ultimately, adopting the proposed model, a temperature prediction model founded on BP and LSTM and orthodox empirical formulas, the component temperatures of a photovoltaic station are studied and predicted. Comparative results demonstrate that this approach yields excellent predictive precision, boasting an root mean square error less than 2 ℃, and necessitates a more modest computational scale for model deployment, with an operational pace surpassing neural networks by tenfold, thereby facilitating practical control system application. When juxtaposed with neural network methodologies, it is superiorly interpretable and serves as an efficacious method for real-time assessment of photovoltaic module temperature.