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Hybrid Model for Building Performance Diagnosis and Optimal Control
Abstract
Modern buildings require continuous performance
monitoring, automatic diagnostics and optimal
supervisory control. For these applications, simplified
dynamic building models are needed to predict the
cooling and heating requirement viewing the building
as a whole system. This paper proposes a new hybrid
model. Half of the model is represented by detailed
physical parameters and another half is described by
identified parameters. 3R2C thermal network model,
which consists of three resistances and two
capacitances, is used to simulate building envelope
whose parameters are determined in frequency
domain using the theoretical frequency characteristics
of the envelope. Internal mass is represented by a
2R2C thermal network model, which consists of three
resistances and two capacitances. The resistances and
capacitances of the 2R2C model are assumed to be
constant. A GA (genetic algorithm)-based method is
developed for model parameter identification by
searching the optimal parameters of 3R2C models of
envelopes in frequency domain and that of the 2R2C
model of the building internal mass in time domain.
As the model is based on the physical characteristics,
the hybrid model can be used to predict the cooling
and heating energy consumption of buildings
accurately in wide range of operation conditions.
Citation
Wang, S.; Xu, X. (2003). Hybrid Model for Building Performance Diagnosis and Optimal Control. Energy Systems Laboratory (http://esl.tamu.edu); Texas A&M University (http://www.tamu.edu). Available electronically from https : / /hdl .handle .net /1969 .1 /5225.