Research on deep learning prediction model based on balanced circle search for air conditioner start-up time

In view of the fact that the data-driven modeling methods only focus on feature mapping without concerning on the long-term interdependence between process variables, lack contextual information between data and ignore the importance of different variables, which leads to poor prediction performance, a prediction model of air conditioner start-up time combining temporal convolutional network, bidirectional gated recurrent unit and attention mechanism is proposed, and four important parameters are searched in a balanced circle to improve the prediction performance of the model. Subsequently, experiments based on a cigarette factory dataset are conducted to verify the superiority of the presented model compared to other models. The results show that compared with the benchmark model, the prediction performance of the circle search model and the balanced circle search model is improved by 28.98% and 37.91%, respectively. For some abnormal working conditions, the start-up time obtained by the presented model is shortened by about 45% compared with the start-up time obtained by manual experience, thereby reducing the energy consumption of air conditioning.