MITP-Net: A deep-learning framework for short-term indoor temperature predictions in multi-zone buildings - Xing, Tian; Sun, Kailai; Zhao, Qianchuan (Building and environment, 2023)
Indoor temperature prediction is an essential component of building control and energy saving. Although existing indoor temperature prediction frameworks have achieved remarkable progress, they struggle to achieve high performance due to information, method, application, and sim-to-real gaps. Aiming to fill these gaps, we propose a novel deep-learning framework for short-term indoor temperature prediction in multi-zone buildings. In particular, we expand the sensing information and formulate the multi-zone indoor temperature prediction (MITP) problem. To improve the prediction performance, we employ information fusion and encoder–decoder architecture to the MITP problem and propose MITP-Net. We set up 11 ablation experiments to compare the prediction performance of relative frameworks. To evaluate frameworks’ performance, we publicly release a dataset including 2-week real operating data in a multi-zone office with a 1-min sampling interval (829,440 digits in total). Compared with existing deep-learning frameworks, MITP-Net significantly raises the prediction accuracy and can flexibly adjust the lengths of input and prediction sequences for different requirements. We provide the usage steps of MITP-Net and publish the operating data and codes on the GitHub repository: https://github.com/XingTian1994/MITP-Net. •We formulate the MITP problem and propose a novel deep-learning prediction framework.•MITP-Net utilizes a two-stage information fusion method for multi-modal data.•MITP-Net adopts the encoder–decoder architecture for variable sequences length.•We publicly release a real multi-zone office dataset and verify MITP-Net.•MITP-Net significantly improves the performance compared to existing methods.