  In this study, a novel application of neural networks that predict thermal
comfort states of occupants is proposed with accuracy over 95%, and two
optimization algorithms are proposed and evaluated under two real cases
(general offices and lecture theatres/conference rooms scenarios) in Singapore.
The two optimization algorithms are Bayesian Gaussian process optimization
(BGPO) and augmented firefly algorithm (AFA). Based on our earlier studies, the
models of energy consumption were developed and well-trained through neural
networks. This study focuses on using novel active approaches to evaluate
thermal comfort of occupants and so as to solves a multiple-objective problem
that aims to balance energy-efficiency of centralized air-conditioning systems
and thermal comfort of occupants. The study results show that both BGPO and AFA
are feasible to resolve this no prior knowledge-based optimization problem
effectively. However, the optimal solutions of AFA are more consistent than
those of BGPO at given sample sizes. The best energy saving rates (ESR) of BGPO
and AFA are around -21% and -10% respectively at energy-efficient user
preference for both Case 1 and Case 2. As a result, an potential benefit of
S$1219.1 can be achieved annually for this experimental laboratory level in
Singapore.
