Research on Online Temperature Prediction Method for Office Building Interiors Based on Data Mining

Jiale Tang, Kuixing Liu, Weijie You, Xinyu Zhang () and Tuomi Zhang
Additional contact information
Jiale Tang: State Key Laboratory of Building Safety and Built Environment, Beijing 100013, China
Kuixing Liu: School of Architecture, Tianjin University, Tianjin 300072, China
Weijie You: School of Architecture, Tianjin University, Tianjin 300072, China
Xinyu Zhang: State Key Laboratory of Building Safety and Built Environment, Beijing 100013, China
Tuomi Zhang: Tianjin International Engineering Institute, Tianjin University, Tianjin 300072, China

Energies, 2023, vol. 16, issue 14, 1-19

Abstract: Indoor environmental parameters are closely related to the energy consumption and indoor thermal comfort of office buildings. Predicting these parameters, especially indoor temperature, can contribute to the management of energy consumption and thermal comfort levels in office buildings. An accurate indoor temperature prediction model is the basis for implementing this process. To this end, this paper first discusses the input and output parameters of the model, and then it compares the prediction effects of mainstream prediction model algorithms based on data mining under the same data conditions. The superiority of the XGBoost integrated learning algorithm is verified, and a further XGBoost-based indoor temperature online prediction method is designed. The effectiveness of the method is validated using actual data from a commercial office building in Haidian District, Beijing. Finally, optimization methods for the prediction method are discussed with regard to the scheduler mechanism proposed in this paper. Overall, this work can assist building operators in optimizing HVAC equipment running strategies, thus improving the indoor thermal comfort and energy efficiency of the building.

Keywords: temperature prediction; meteorological parameters; XGBoost; online operation; scheduler; error (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/14/5570/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/14/5570/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:14:p:5570-:d:1200772

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().