| Citation: | LIU H，LIN J Q. Energy consumption prediction of aircraft ground air conditioning based on IPSO-AM-LSTM[J]. Journal of Beijing University of Aeronautics and Astronautics，2024，50（11）：3595-3602 （in Chinese） doi: 10.13700/j.bh.1001-5965.2022.0872
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As a complex thermal system, the energy consumption of aircraft ground air conditioning is affected by many factors, including various weather data and time characteristics. In order to improve the prediction accuracy of ground air conditioning energy consumption when the aircraft cabin is cooled by ground air conditioning, a ground air conditioning energy consumption prediction model based on a long-short-term memory (LSTM) network is proposed. The prediction component of the model, which is utilized to extract and make use of the time series information in the data, is built by integrating the long-short-term memory network with the attention mechanism. The prediction accuracy is used as the fitness function of the algorithm. The hyperparameter optimization based on the improved particle swarm optimization (IPSO) algorithm is proposed. Compared with the standard particle swarm optimization (PSO) algorithm, the improved particle swarm optimization algorithm combines the number of iterations with the fitness to construct a dynamic adjustment function of the inertia weight. The distance from the particle to the global most optimal position is introduced and a particle intersection strategy is proposed to improve the diversity of particle swarms. The mean square error of the prediction result is 4.394. The mean absolute percentage error is 0.774%, and the coefficient of determination is 0.968. The results indicate that the prediction approach has a greater accuracy when compared to other prediction methods.
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