Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction-Reference-Cited by-同舟云学术

Spatio-Temporal Consistency Enhanced Differential Network for Interpretable Indoor Temperature Prediction

Published:2024-08-24 Issue: Volume:42 Page:5590-5601
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Container-title:Proceedings of the 30th ACM SIGKDD Conference on Knowledge Discovery and Data Mining
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Author:

Qi Dekang¹^ORCID,Yi Xiuwen²^ORCID,Guo Chengjie³^ORCID,Huang Yanyong⁴^ORCID,Zhang Junbo²^ORCID,Li Tianrui⁵^ORCID,Zheng Yu²^ORCID

Affiliation:

1. Southwest Jiaotong University & JD iCity, JD Technology, Chengdu, China

2. JD iCity, JD Technology & JD Intelligent Cities Research, Beijing, China

3. Xidian University, Xi'an, China

4. Southwestern University of Finance and Economics, Chengdu, China

5. Southwest Jiaotong University, Chengdu, China

Funder

National Natural Science Foundation of China

Beijing Nova Program

Natural Science Foundation Project of Sichuan Province

Youth Fund Project of Humanities and Social Science Research of Ministry of Education

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3637528.3671608

Reference39 articles.

1. Zakia Afroz, Tania Urmee, GM Shafiullah, and Gary Higgins. 2018. Real-time prediction model for indoor temperature in a commercial building. Applied energy, Vol. 231 (2018), 29--53.

2. Aylin Alin. 2010. Multicollinearity. Wiley interdisciplinary reviews: computational statistics, Vol. 2, 3 (2010), 370--374.

3. Development and validation of a gray box model to predict thermal behavior of occupied office buildings

4. Theory-guided deep-learning for electrical load forecasting (TgDLF) via ensemble long short-term memory

5. Energy plus: energy simulation program;Crawley Drury B;ASHRAE journal,2000