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

Abstract

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.