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

Κολοκότσα, Δ.

doi:10.1080/17512549.2007.9687268

Cited by 30 publications

(12 citation statements)

References 76 publications

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“…Katipamula and Brambley (2005) reviewed different methods applied for fault detection and diagnosis in HVAC systems. A review of fuzzy logic systems applied to building research was presented by Kolokotsa (2007), where the author presented fuzzy logic applications for indoor visual and thermal comfort, and indoor air quality, as well as fuzzy logic control systems. Various methods for control were also discussed by Shaikh et al (2014).…”

Section: Study Contentsmentioning

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: Study Contentsmentioning

confidence: 99%

Computational intelligence techniques for HVAC systems: A review

et al. 2016

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“…Advanced control techniques based on artificial intelligence (neural networks, fuzzy logic, genetic algorithms, etc.) and distributed control networks offer numerous benefits towards that direction [1][2][3][4][5].…”

Section: Introductionmentioning

confidence: 99%

Predictive control techniques for energy and indoor environmental quality management in buildings

Κολοκότσα

Pouliezos

Stavrakakis

et al. 2009

Building and Environment

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167

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“…A corresponding review of fuzzy logic applications to building research was investigated by Kolokotsa. 6 The review has included the extensive use of fuzzy logic in the buildings' improvement in terms of indoor comfort and energy conservation. The main focuses of related improvements include thermal comfort, indoor air quality, indoor visual comfort, energy consumption, HVAC system status and stable operation and outdoor climate parameters.…”

Section: Introductionmentioning

confidence: 99%

Performance analysis on an air-conditioning system installed with and without a fuzzy logic thermostat in an office building

Yau

Chang

2018

Proceedings of the Institution of Mechanical Engineers, Part A:

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The current research presents the study of the proposed thermostat setting developed in the previous work through a numerical simulation project using transient system simulation tool. The transient system simulation tool code is used to integrate the fuzzy logic to optimise a room thermostat with the multi-zone building model (Type56a). A set of equations is designed in the transient system simulation tool project to represent the fuzzy logic control system of the proposed thermostat setting. The performance analysis for the airconditioning system installed with and without the application of the control system in terms of the operative zone temperature, the sensible cooling demand, the solar radiation, and the internal gains for a thermal zone for recent years or 2000s, and for the years 2020, 2050 and 2080 were comprehensively examined. Estimations of both predicted mean vote and predicted percentage of dissatisfied persons of the zone were also investigated. The findings indicate that a considerable amount of sensible cooling demand of approximately 8420 kJ/h or equivalent to 3.54% can be saved with the application of the proposed thermostat setting. The predicted mean vote is in the range from À0.22 to 0.72 with its average between 0.20 and 0.26 for the AC system installed with fuzzy logic control. These results further suggest that the thermal comfort of occupants can be improved taking into account their activity level, clothing insulation, indoor air temperature, air velocity, mean radiant temperature and relative humidity.

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

Cited by 30 publications

References 76 publications

Computational intelligence techniques for HVAC systems: A review

Computational intelligence techniques for HVAC systems: A review

Predictive control techniques for energy and indoor environmental quality management in buildings

Performance analysis on an air-conditioning system installed with and without a fuzzy logic thermostat in an office building

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