Air conditioning load prediction based on deep learning considering human behavior

In this paper, with the purpose of improving the accuracy of air conditioning load prediction, firstly, the entry and exit behavior patterns of personnel are obtained through k-means clustering to determine the number of people in the room. Secondly, the movement of people in each indoor room is described based on the Markov movement model to determine the presence or absence of people in indoor rooms. Then, the indoor parameters are predicted by LSTM and the indoor thermal environment is evaluated by the PMV-PPD method to determine the behavior of turning on and off the air conditioner by the personnel in the rooms. Finally, the outdoor parameters, indoor parameters and air conditioning switch status parameters are used as input features to predict the air conditioning load using LSTM, GRU and BiLSTM. The case verification shows that the introduction of air conditioning switch status parameters in the prediction can better explore the characteristics and rules of air conditioning switch, the introduction of indoor parameters can inhibit the poor prediction accuracy caused by the drastic changes of air conditioning loads, and the introduction of the two types of parameters at the same time can significantly reduce the prediction error. Compared with LSTM and GRU, BiLSTM has higher prediction accuracy.