Improving prediction performance for indoor temperature in public buildings based on a novel deep learning method

This study presents a case study of public buildings using a novel deep learning method to forecast indoor air temperature. The aim is to explore the potential of long short-term memory (LSTM) model in forecasting indoor temperature, and a novel LSTM model modified by error correction model is established. The performance of the two models is compared with popular prediction methods in the building field. Results show that the proposed novel LSTM model has slight advantages in level indoor temperature prediction performance comparing with other common machine learning methods. However, it outperforms other models including original LSTM in terms of directional prediction accuracy, and accurately predicts the indoor temperature variation trend. This work is enlightening and may have a further reference to the feasibility study of indoor air temperature prediction model.