The stratospheric airship is a kind of aircraft that completely relies on the cycle of photovoltaic energy systems to achieve long duration flight. The accurate estimation of the operating temperature of solar cell modules on stratospheric airship is extremely important for the design of photovoltaics system (PV system), the output power calculation of PV system, and the calculation of energy balance. However, the related study has been rarely reported. A support vector machine prediction method based on particle swarm optimization algorithm (PSO-SVM) was established to predict the operating temperature of solar cell modules on stratospheric airship. The PSO algorithm was used to dynamically optimize the SVM’s parameters between the operating temperature of the solar cell modules and the measured data such as atmospheric pressure, solar radiation intensity, flight speed, and ambient temperature. The operating temperature data of the two sets of solar cell modules measured in the flight test were used to verify the accuracy of the temperature prediction model, and the prediction results were compared with a back propagation neural network (BPNN) method and the simulation results calculated by COMSOL Multiphysics of COMSOL, Inc., Columbus, MA, USA. The results shown that the PSO-SVM model realized the accurate prediction of the operating temperature of solar cell modules on stratospheric airship, which can guide the design of PV system, the output power calculation of PV system, and the calculation of energy balance.
Mean Empirical Likelihood Inference for Response Mean with Data Missing at Random
