| Citation: | ZHOU X，LIN J Q. Prediction of ground air conditioner energy consumption based on improved long short-term memory neural network[J]. Journal of Beijing University of Aeronautics and Astronautics，2023，49（10）：2750-2760 （in Chinese） doi: 10.13700/j.bh.1001-5965.2021.0715
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Ground air conditioners are the main equipment for cooling and dehumidification of airplane cabins, so accurate prediction of their energy consumption in the working process plays an important role in building green airports. The energy consumption of the ground air conditioner is affected by multidimensional factors. To improve the accuracy of energy consumption prediction, this study presents a method based on an improved bidirectional long short-term memory (BiLSTM) neural network. This method uses BiLSTM neural network and attention mechanism to construct the predictive part of the model, which can extract and utilize the time series characteristics of the data. Taking the optimal prediction accuracy as the index, this study also proposes a hyperparameter optimization method based on the improved ant lion optimization algorithm. Compared with the standard algorithm, the improved ant lion optimization (IALO) algorithm improves the shrinkage factor in the random walk space reduction mechanism, giving the shrinkage coefficient some randomness. It also introduces the dynamic adjustment mechanism of the ordinary ant lion weight coefficient, which improves the rate of convergence and optimization capabilities of the algorithm. The mean square error of the prediction result is 6.045, the mean absolute percentage error is 0.928%, and the coefficient of determination is 0.956. Compared with other prediction methods, the proposed method has higher accuracy and stronger adaptation.
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