Predicting and Optimizing for Energy Efficient ACMV Systems: Computational Intelligence Approaches

TL;DR

This paper presents neural network-based predictions of thermal comfort and introduces two optimization algorithms, BGPO and AFA, to enhance energy efficiency and occupant comfort in ACMV systems in Singapore.

Contribution

It develops novel active approaches using neural networks and two optimization algorithms to balance energy efficiency and thermal comfort in HVAC systems.

Findings

AFC achieves more consistent solutions than BGPO.

Energy savings of around 21% with BGPO and 10% with AFA.

Potential annual cost savings of S$1219.1.

Abstract

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.