论文标题
预测和优化节能ACMV系统：计算智能方法
Predicting and Optimizing for Energy Efficient ACMV Systems: Computational Intelligence Approaches
论文作者
论文摘要
在这项研究中，提出了一种高度超过95％的居住者热舒适状态的神经网络的新应用，并在新加坡提出了两种优化算法，并在两种真实情况（通用办公室和讲座剧院/会议室情景）下提出了两种优化算法。两种优化算法是贝叶斯高斯工艺优化（BGPO）和增强的萤火虫算法（AFA）。根据我们较早的研究，通过神经网络开发了能源消耗模型。这项研究重点是使用新型的主动方法来评估乘员的热舒适度，以解决一个多目标问题，旨在平衡集中空调系统的能源效率和乘员的热舒适度。研究结果表明，BGPO和AFA都是可行的，无法有效解决这个先验的基于知识的优化问题。但是，在给定样本量下，AFA的最佳解决方案比BGPO的最佳解决方案更一致。 BGPO和AFA的最佳节能率（ESR）分别在案例1和案例2时分别为-21％和-10％。结果，对于新加坡的这一实验实验室水平，每年可以获得1219.1新元的潜在益处。
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.
