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.

利用新型神经网络模型预测占用者的热舒适状态, 分别应用了 Bayesian Gaussian Process Optimization (BGPO) 和 Augmented Firefly Algorithm (AFA) 优化算法来平衡空调系统的能效和占用者的热舒适感, 实验结果表明, 在样本数量一定时,AFA算法比BGPO算法更稳定,并且在能效用户偏好条件下, BGPO 和 AFA 的最佳节能率分别为 -21％ 和 -10％，可在新加坡实验室节约 S$1219.1。

基于计算智能的能效ACMV系统预测与优化