Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm

Nassif, Nabil; Kajl, Stanislaw; Sabourin, Robert

doi:10.1080/10789669.2005.10391148

Cited by 148 publications

(71 citation statements)

References 28 publications

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“…CI is a rapidly advancing research field and includes a collection of various computation techniques, including but not limited to: expert systems, genetic algorithm (GA), artificial neural network (ANN), support vector machines (SVM). The most commonly used CI techniques for HVAC applications are fuzzy logic (Chu et al 2005;So et al 1997;Zheng and Xu 2004), ANN (Argiriou et al 2000;Curtiss et al 1994;Kanarachos and Geramanis 1998), GA (Lu et al 2005;Mossolly et al 2009;Nassif et al 2005;Wang and Jin 2000;Wright et al 2002), multi-agent systems (Hagras et al 2008;Rutishauser et al 2005;Yang and Wang 2013) and pattern recognitionbased methods (Du et al 2007a;Hu et al 2012;Naja. et al 2012;Wang and Cui 2005;Wang and Xiao 2004a;Zhao et al 2013b).…”

Section: Algorithmsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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Buildings are responsible for 40% of global energy use and contribute towards 30% of the total CO 2 emissions. The drive to reduce energy use and associated greenhouse gas emissions from buildings has acted as a catalyst in the development of advanced computational methods for energy efficient design, management and control of buildings and systems. Heating, ventilation and air-conditioning (HVAC) systems are the major source of energy consumption in buildings and ideal candidates for substantial reductions in energy demand. Significant advances have been made in the past decades on the application of computational intelligence (CI) techniques for HVAC design, control, management, optimization, and fault detection and diagnosis. This article presents a comprehensive and critical review on the theory and applications of CI techniques for prediction, optimization, control and diagnosis of HVAC systems. The analysis of trends reveals that the minimisation of energy consumption was the key optimization objective in the reviewed research, closely followed by the optimization of thermal comfort, indoor air quality and occupant preferences.Hardcoded Matlab program was the most widely used simulation tool, followed by TRNSYS, EnergyPlus, DOE-2, HVACSim+ and ESP-r. Metaheuristic algorithms were the preferred CI method for solving HVAC related problems and in particular genetic algorithms were applied in most of the studies. Despite the low number of studies focussing on multi-agent systems (MAS), as compared to the other CI techniques, interest in the technique is increasing due to their ability of dividing and conquering an HVAC optimization problem with enhanced overall performance. The paper also identifies prospective future advancements and research directions.

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Section: Algorithmsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…According to the studies of Zitzler [137] and Deb [128], the elitist Non-dominated Sorting Genetic Algorithm (NSGA-II) seems to be the most efficient GAs. The NSGA-II is implemented to find trade-off relations between energy consumption and investment cost or thermal comfort level of buildings [70,71,72,86,106,111,138,139]. The NSGA-II [128] could be one of the most suitable optimisation algorithms to handle multi-objective multivariate building and HVAC design problems with discrete, non-linear, and highly constrained characteristics.…”

Section: Bpo Objectives (Single-objective and Multi-objective Functions)mentioning

confidence: 99%

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Attia

Hamdy

O’Brien³

et al. 2013

Energy and Buildings

359

192

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This paper summarizes a study undertaken to reveal potential challenges and opportunities for integrating optimisation tools in Net Zero Energy Buildings (NZEB) Design. The paper reviews current trends in simulationbased Building Performance Optimisation (BPO) and outlines major criteria for optimisation tools selection and evaluation. This is based on analyzing user's needs for tools capabilities and requirement specifications. The review is carried out by means of a literature review of 165 publications and interviews with 28 optimisation experts. The findings are based on an inter-group comparison between experts. The aim is to assess the gaps and needs for integrating BPO tools in NZEB Design. The findings indicate existing limitations including model uncertainty, computation time, difficulty of use and steep learning curve. Some future directions anticipated or needed for improvement of current tools are presented.

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“…In another study [101], they reduced the number of design variables by using 5 zone temperature set points instead of 70 ones. Their MOGA result showed 16% energy savings for 2 summer months while assuring the minimum requirements of zone airflow and thermal comfort over the actual energy consumption.…”

Section: Energy Conservationmentioning

confidence: 99%

Energy Efficiency of Distributed Environmental Control Systems

Khalifa¹,

Işık²,

Dannenhoffer³

2011

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Wilcoxen of the Syracuse University Maxwell School, and three engineering graduate students who performed the analytical and computational work under the supervision of Professors Khalifa, Isik, Dannenhoffer and Wilcoxen, namely: Mr. Ian Cosden who developed and implemented the thermal model of the building and its environmental control system; Mr. Seckin Ari who developed the comfort model and performed the optimization and control analyses; and Mr. Xuanhang (Simon) Zhang, who developed the CFD model of intra-and inter-cubicle air and energy transport and refined the thermal and comfort models accordingly.Report No. DOE\ER63694-1 iii A. ABSTRACTRecent research has indicated that allowing building occupants to customize their own local environment increases satisfaction and workplace performance. However, concern about the possible increase of energy consumption associated with the implementation of distributed localized environmental control has limited the widespread adoption of such systems. In this report, we present an analytical evaluation of the potential of occupant-regulated distributed environmental control systems (DECS) to enhance individual occupant thermal comfort in an office building with no increase, and possibly even a decrease in annual energy consumption. To this end we developed and applied several analytical models that allowed us to optimize comfort and energy consumption in partitioned office buildings equipped with either conventional central HVAC systems or occupant-regulated DECS. Our approach involved the following interrelated components:1. Development of a simplified lumped-parameter thermal circuit model to compute the annual energy consumption. This was necessitated by the need to perform tens of thousands of optimization calculations involving different US climatic regions, and different occupant thermal preferences of a population of ~50 office occupants. Yearly transient simulations using TRNSYS, a time-dependent building energy modeling program, were run to determine the robustness of the simplified approach against time-dependent simulations. The simplified model predicts yearly energy consumption within approximately 0.6% of an equivalent transient simulation. Simulations of building energy usage were run for a wide variety of climatic regions and control scenarios, including traditional "one-size-fits-all" (OSFA) control; providing a uniform temperature to the entire building, and occupantselected "have-it-your-way" (HIYW) control with a thermostat at each workstation. The thermal model shows that, un-optimized, DECS would lead to an increase in building energy consumption between 3-16% compared to the conventional approach depending on the climate regional and personal preferences of building occupants. Variations in building shape had little impact in the relative energy usage. The model also allowed us to study the effect of the thermal resistance of the partitions between adjacent workstations, which can have a significant influence on the energy cost (3% to 78% increas...

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Optimization of HVAC Control System Strategy Using Two-Objective Genetic Algorithm

Cited by 148 publications

References 28 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Assessing gaps and needs for integrating building performance optimization tools in net zero energy buildings design

Energy Efficiency of Distributed Environmental Control Systems

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