Abstract: The heat collection of the solar collector is influenced by various factors, such as light intensity, ambient temperature, wind speed and so on, so its prediction model is difficult to meet the user needs both in terms of the prediction accuracy and the real-time performance. A hybrid modeling method to predict the real-time heat collection of solar collector systems is proposed in this paper. The method first starts from energy conservation to derive the theoretical model of the solar collection system according to its heat transfer mechanism, and the empirical parameters, such as the heat dissipation coefficient, the transmissivity, the absorptivity, etc. and the geometric parameters, such as the lighting area, the cooling area, etc. are lumped as the unknown parameters of the model, then the structure of the hybrid model is presented. The TRNSYS simulation software is employed to build a solar collector simulation system, and the simulation experiments on the different operating conditions were performed on the simulation system so as to obtain the steady-state data to identify the unknown parameters of the hybrid model. Finally, the particle optimization algorithm (PSO) is selected as the model parameters identification method to identify the unknown parameters of the model using the obtained steady-state data. The results compared with the model predicted values and the simulation experimental data show that the model is simple and accurate, and can predict the real-time heat collection of solar collector systems with a mean relative error of 2.02% in various working conditions. The model will be widely used in the optimal control of solar heat pumps, solar water heaters and other systems.

Key words: solar heat collection systems, hybrid modeling, PSO, parameter identification, solar energy