Balancing indoor thermal comfort and energy consumption of ACMV systems via sparse swarm algorithms in optimizations

• Published in: Energy and buildings Vol. 149; pp. 1 - 15
• Main Authors: Zhai, Deqing, Soh, Yeng Chai
• Format: Journal Article
• Language: English
• Published: Lausanne Elsevier B.V 15.08.2017; Elsevier BV
• Subjects: Air conditioners; Air conditioning; Air-conditioning and mechanical ventilation systems (ACMV); Algorithms; Augmented firefly algorithm (AFA); Computer applications; Energy conservation; Energy consumption; Extreme learning machines (ELM); Firefly algorithm (FA); Functions (mathematics); Heuristic methods; Indoor environments; Intelligence; Mathematical analysis; Mathematical models; Mechanical ventilation; Neural networks; Neural networks (NN); Offices; Optimization; Predicted mean vote (PMV); Thermal comfort; Ventilation; Energy consumption; Thermal comfort; Augmented firefly algorithm (AFA); Extreme learning machines (ELM); Air-conditioning and mechanical ventilation systems (ACMV); Firefly algorithm (FA); Neural networks (NN); Predicted mean vote (PMV)
• ISSN: 0378-7788; 1872-6178
• DOI: 10.1016/j.enbuild.2017.05.019

This paper proposes a systematic modelling and optimizing of energy consumption and indoor thermal comfort for air-conditioning and mechanical ventilation (ACMV) systems. The models of extreme learning machines (ELM) and neural networks (NN) are established and evaluated. These well-trained models are then integrated with the computational intelligence techniques of sparse firefly algorithm (sFA) and sparse augmented firefly algorithm (sAFA). The sFA and sAFA aim to locate the global optimal operating points of the ACMV systems in real-time and predict energy saving rate (ESR) with a third order polynomial regression based on minimizing the mean squared errors (MSE) of the cost functions. This study also covers different indoor scenarios, such as general offices, lecture theatres and conference rooms. Given the well trained models, the maximum prediction of potential ESR can be −30% via the sparse AFA optimizations while maintaining indoor thermal comfort in the pre-defined comfort zone.