Design and simulation of a thermal comfort adaptive system based on fuzzy logic and on-line learning

Bermejo, Pablo; Redondo, Luis; Ossa, Luis de la; Rodríguez, Daniel; Flores, M. Julia; Urea, Carmen; Gámez, José A.; Puerta, José M.

doi:10.1016/j.enbuild.2012.02.032

Cited by 63 publications

(29 citation statements)

References 15 publications

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“…Research shows that tracking occupant behavior with thermal control devices (e.g., thermostats, fans) can be non-intrusive yet provide additional data points that can be used to infer individuals' thermal comfort [28]. Individuals interact with thermal control devices available in the space to meet their cooling and heating needs; hence, the resulting behavior can be regarded as an expression of one's thermal preference.…”

mentioning

confidence: 99%

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Kim

Zhou

Schiavon

et al. 2018

Building and Environment

350

162

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mentioning

confidence: 99%

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Kim

Zhou

Schiavon

et al. 2018

Building and Environment

350

162

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“…Human occupant comfort in buildings has been attributed to many factors such as temperature, humidity CO 2 levels, etc. [15]. However, thermal comfort has been identified as the driving factor of overall comfort within buildings [15].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

“…[15]. However, thermal comfort has been identified as the driving factor of overall comfort within buildings [15].…”

Section: Introduction (Heading 1)mentioning

confidence: 99%

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Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Wijayasekara

Manic

Rieger

2013

2013 6th International Symposium on Resilient Control Systems (ISRCS)

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Abstract-Significant portion of world energy production is consumed by building Heating, Ventilation and Air Conditioning (HVAC) units. Thus along with occupant comfort, energy efficiency is also an important factor in HVAC control. Modern buildings use advanced Multiple Input Multiple Output (MIMO) control schemes to realize these goals. However, since the performance of HVAC units is dependent on many criteria including uncertainties in weather, number of occupants, and thermal state, the performance of current state of the art systems are sub-optimal. Furthermore, because of the large number of sensors in buildings, and the high frequency of data collection, large amount of information is available. Therefore, important behavior of buildings that compromise energy efficiency or occupant comfort is difficult to identify. This paper presents an easy to use and understandable framework for identifying such behavior. The presented framework uses human understandable knowledge-base to extract important behavior of buildings and present it to users via a graphical user interface. The presented framework was tested on a building in the Pacific Northwest and was shown to be able to identify important behavior that relates to energy efficiency and occupant comfort.

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“…Various factors may influence human's thermal feelings, in which temperature and relative humidity take the largest part for indoor users [31]. In general, humans feel more comfortable when the temperature is between 24 °C and 27 °C, while relative humidity is between 55% and 70% in summer [32][33][34][35][36]. Accounting for the user's thermal comfort, the accurate description of conditioned space is required.…”

Section: Introductionmentioning

confidence: 99%

Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort

et al. 2015

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Air conditioners have great potential to participate in power system frequency control. This paper proposes a control strategy to facilitate the active participation of air conditioners. For each air conditioner, a decentralized control law is designed to adjust its temperature set point in response to the system frequency deviation. The decentralized control law accounts for the user's thermal comfort that is evaluated by a fuzzy algorithm. The aggregation of air conditioners' response is conducted by using the Monte Carlo simulation method. A structure preserving model is applied to the multi-bus power system, in which air conditioners are aggregated at certain load buses. An inner-outer iteration scheme is adopted to solve power system dynamics. An experiment is conducted on a test air conditioner to examine the performance of the proposed decentralized control law. Simulation results on a test power system verify the effectiveness of the proposed strategy for air conditioners participating in frequency control.

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Design and simulation of a thermal comfort adaptive system based on fuzzy logic and on-line learning

Cited by 63 publications

References 15 publications

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Personal comfort models: Predicting individuals' thermal preference using occupant heating and cooling behavior and machine learning

Fuzzy linguistic knowledge based behavior extraction for building energy management systems

Active Participation of Air Conditioners in Power System Frequency Control Considering Users’ Thermal Comfort

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