A simulation approach of indoor temperature in existing buildings driven by short-term field measured data

ABSTRACT

Simulation of indoor temperature provides important references for thermal environment not only for buildings at design stage but also for existing buildings. The current thermal environment simulation software tools suit for buildings at design stage, however not for an existing building. A model is proposed to simulate indoor temperature combining Optimization multivariable grey prediction model (OGM(1,N)) and Elman neural network. The proposed model is trained by short-term field measured data. A unit is assembled to measure and record thermal parameters in a case natural ventilated building at half-hourly intervals during 7:00 May 29 and 6:30 June 2010. Programming in Matlab implements the proposed model and referenced models. The maximum mean deviation is 0.46°C, the maximum standard mean square deviation is 0.65°C. Three referenced indoor temperature simulation models, OGM(1,N), Elman neural network, and Designer’s Simulation Toolkit are executed, respectively, in case building to provide comparison. Compared with referenced models, the proposed model has higher accuracy and stronger robustness. It is expected that this study provides important references for thermal environment assessment in existing buildings using short-term field measured data.

KEYWORDS:

• Indoor temperature
• simulation
• data driven
• grey prediction
• Elman neural network

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This research is financially supported by Zhejiang Provincial Natural Science Foundation of China under Grant No. LY18E080035, National Social Science Fund of China under Grant No. 20BZX132.

Notes on contributors

Yulan Yang

Yulan Yang,Phd,Associate Professor, she is interested in research of building indoor environment,building energy efficiency and historic building conservation.

Huixin Tai

Huixin Tai,Phd,Associate Professor, he is interested in research of architectural acoustics and historic building conservation.

Lingzhi Liu

Lingzhi Liu,Phd, Professor, she is interested in research of built environment and historic building conservation.

Beier Yu

Beier Yu,he is a postgraduate student.

Wenlong Song

Wenlong Song,he is a postgraduate student.