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Environmental parameters such as carbon dioxide concentration and light intensity), remote collection of equipment information, data storage and data analysis functions, and intelligent control of smart classrooms. Data analysis uses an improved LSTM model to predict energy consumption. The model uses LSTM and bidirectional LSTM and uses the ELU activation function instead of the sigmoid and tanh activation functions of the LSTM. Compared with the standard LSTM model and the LSTM model without the ELU activation function, the model improves the prediction accuracy, better avoid the gradient disappearance, and reduces the over-fitting. 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