An HVAC fuzzy logic zone control system and performance results

Lea, Robert N.; Dohmann, E.; Prebilsky, W.; Jani, Yashvant

doi:10.1109/fuzzy.1996.553537

Cited by 11 publications

(8 citation statements)

References 2 publications

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“…The module was evaluated by experts and the evaluation results were satisfactory. Moreover, HVAC fuzzy principles are given by Lea et al (1996). The performance of the proposed control system from a subjective point of view of people living in the facility was evaluated.…”

Section: Fuzzy Logic Energy Consumption and Hvac Systems State Of Thementioning

confidence: 99%

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Κολοκότσα¹

2007

Advances in Building Energy Research

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Section: Fuzzy Logic Energy Consumption and Hvac Systems State Of Thementioning

confidence: 99%

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Κολοκότσα¹

2007

Advances in Building Energy Research

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“…* External heat sources such as human bodies and appliances are simulated as an uncontrolled input. Under these assumptions, the dynamics of the zone temperature is described by (1)(2)(3)(4). Table I Cw2 s+2d' III.…”

Section: Introductionmentioning

confidence: 99%

“…INTRODUCTION There is an increasing demand for Heat Ventilating and Air Conditioning (HVAC) systems due to the increasing population and global warming and many other serious reasons. Energy conservation becomes a very important problem to be addressed as it affects the local and global quality of life.Researches have made efforts to improve air quality produced by HVAC units [1][2][3]. However, to date there is no cost efficient commercial utilization of this technology.…”

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confidence: 99%

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Minimizing HVAC Energy Consumption Using a Wireless Sensor Network

Tachwali¹,

Refai²,

Fagan³

2007

IECON 2007 - 33rd Annual Conference of the IEEE Industrial Electronics Society

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This paper presents a conceptual design of a multizone hvac controller based on a wireless sensor network that aims to optimize energy consumption while maintaining the comfort level of users. The proposed system is capable of learning the occupancy pattern of the residents in the building. Moreover, it is transparent (requires no manual configuration). Wireless sensor network technology is used to collect the required measurements for the control system. simulation results are used to demonstrate the system performance in terms of power consumption and comfort level. Additionally, a comparison between the proposed system and other hvac control systems is presented. The results show that the proposed system is capable of providing a major energy savings.I. INTRODUCTION There is an increasing demand for Heat Ventilating and Air Conditioning (HVAC) systems due to the increasing population and global warming and many other serious reasons. Energy conservation becomes a very important problem to be addressed as it affects the local and global quality of life.Researches have made efforts to improve air quality produced by HVAC units [1][2][3]. However, to date there is no cost efficient commercial utilization of this technology. HVAC systems are large consumers of energy, resulting in high electricity bills. HVAC systems consume around 5000 of the total electrical energy generated in the world [4]. Hence, it is very important to optimize the energy consumption of the HVAC system by maximizing energy utilization. Recent studies showed that commercial buildings could reduce energy usage between 15% and 400O by closer monitoring and managing its energy usage [5]. The centralized control of HVAC system has an inherited weakness by allowing the energy to be distributed equally around the serviced zones. This causes a considerable amount of energy to be wasted. Therefore, an intelligent HVAC control is required to minimize the energy consumption and optimize the comfort level.Several studies have been conducted to optimize the energy consumption of HVAC systems [6][7][8][9]. However, these systems are not adaptive to user behaviors [6,7] or they are too complicated to be implemented in a simple embedded system if they are used for multizone buildings [8,9].Although there are some advanced control solutions that provides programmable configuration such as operation time and multiple set points, these systems fail to achieve their main purpose, energy saving, for several reasons. First, these programmable controllers require product education. The limited interface associated with these systems drives the customers to read the product operating manual to figure out how to configure the system to work according to their preferences. Moreover, these controllers are not adaptive. They adhere to the manufacturer's configuration and do not adapt to the customers changing schedule or other new activities that require reconfiguring the system to accommodate these updates. Therefore, many people end up using these programmable system...

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“…Lea et al introduced a fuzzy logic controller for regulating the airflow to the separate zones [66] [67].…”

Section: Temperature and Airflow Controlmentioning

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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An HVAC fuzzy logic zone control system and performance results

Cited by 11 publications

References 2 publications

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Artificial Intelligence in Buildings: A Review of the Application of Fuzzy Logic

Minimizing HVAC Energy Consumption Using a Wireless Sensor Network

Energy Efficiency of Distributed Environmental Control Systems

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