Fuzzy control system for thermal and visual comfort in building

Kristl, Živa; Košir, Mitja; Lah, Mateja Trobec; Krainer, Aleš

doi:10.1016/j.renene.2007.03.020

Cited by 45 publications

(27 citation statements)

References 3 publications

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“…A similar attempt has been made by Kristl et al (2008) for real time harmonization of operations of thermal and illumination system in a test chamber located in Ljubljana, Slovenia. In the experiment a fuzzy logic regulator was programmed to adjust the position of roller blinds and heating/cooling system in variation with outdoor solar radiation and outdoor air temperature.…”

Section: Dynamic Window Shading Devicesmentioning

confidence: 95%

Prospective techniques of effective daylight harvesting in commercial buildings by employing window glazing, dynamic shading devices and dimming control—a literature review

et al. 2008

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Over a few decades, daylighting has been perceived to possess good potential for energy conservation. In this perspective, there have been significant advances in research methodologies and technologies for optimizing energy consumption through daylight harvesting in commercial buildings. In light of this, a thorough understanding of the application of available technology is very important for daylighting practices for building energy management. The objective of this paper is to examine the status of published research on three key building parameters: window glazing area, dynamic shading devices, and daylighting controls playing a rule on energy conservation. This article may serve as a coherent literature survey that would provide better understanding of the subjacent issues and possibly rejuvenate research interest in this immensely potential field of energy engineering.

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Section: Dynamic Window Shading Devicesmentioning

confidence: 95%

Prospective techniques of effective daylight harvesting in commercial buildings by employing window glazing, dynamic shading devices and dimming control—a literature review

et al. 2008

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“…As Naidu and Rieger [8] reported, assigning the required set of membership function in fuzzy logic control strategy regarding to tuning and optimizing the data base and pre-defined rule base would be very time consuming and difficult, specifically for optimization of energy consumption and high thermal comfort as multi-objective functions. There are pure fuzzy logic models [79], and semi-fuzzy systems [80], and incorporated PID and Proportional-derivative (PD) controllers into the rule-based body of the system [81]. A similar principle is applied to all the types of fuzzy controllers, where input variables are usually entered into a block of expert-defined rules which acts as the inference mechanism and outputs are provided based on the existing rules in the inference mechanism and the implemented defuzzification method.…”

Section: Fuzzy Logic (Fl)mentioning

confidence: 99%

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

et al. 2018

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Heating, Ventilating, and Air Conditioning (HVAC) systems are the major energy-consuming devices in buildings. Nowadays, due to the high demand for HVAC system installation in buildings, designing an effective controller in order to decrease the energy consumption of the devices while meeting the thermal comfort demands in buildings are the most important goals of control designers. The purpose of this article is to investigate the different control methods for Heating, Ventilating, and Air Conditioning and Refrigeration (HVAC & R) systems. The advantages and disadvantages of each control method are discussed and finally the Fuzzy Cognitive Map (FCM) method is introduced as a new strategy for HVAC systems. The FCM method is an intelligent and advanced control technique to address the nonlinearity, Multiple-Input and Multiple-Output (MIMO), complexity and coupling effect features of the systems. The significance of this method and improvements by this method are compared with other methods.

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“…However, most often, fuzzy logic controllers were employed because of their flexibility and intuitive uses. Basically, they have two control loops, one regulating the lighting and the other, the thermal aspects (Kristl et al, 2008). In this case, the physical model of the chamber test with the measuring-regulation equipment was www.intechopen.com constructed attempting to develop a control system using fuzzy logic control support, which would enable the harmonious operation of both the thermal and lighting systems.…”

Section: Prior Researchmentioning

confidence: 99%

“…In their research, Nicol and Humphrey challenged the steady-state comfort theories by introducing the adaptive comfort theory (Kristl et al, 2008). The theory proposes that occupants of an indoor ambience can support conditions over steady-state as they can adapt to their environment.…”

Section: Adaptive Modelsmentioning

confidence: 99%

A New HVAC Control System for Improving Perception of Indoor Ambiences

Jose¹

2010

Air Quality

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Fuzzy control system for thermal and visual comfort in building

Cited by 45 publications

References 3 publications

Prospective techniques of effective daylight harvesting in commercial buildings by employing window glazing, dynamic shading devices and dimming control—a literature review

Prospective techniques of effective daylight harvesting in commercial buildings by employing window glazing, dynamic shading devices and dimming control—a literature review

Review of Control Techniques for HVAC Systems—Nonlinearity Approaches Based on Fuzzy Cognitive Maps

A New HVAC Control System for Improving Perception of Indoor Ambiences

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