Predicting Thermal Comfort of HVAC Building Using 6 Thermal Factors

Salleh, Faridah Hani Mohamed; Saripuddin, Mulyana; Omar, Ridha

doi:10.1109/icimu49871.2020.9243466

Cited by 8 publications

(4 citation statements)

References 5 publications

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“…In ref. [18], accuracy indicators of 50.51% were obtained for SVC, 66% for DT, 43.3% for NB, 80.41% for KNC, 55.67% for NN, and 76.3% for BT. In ref.…”

Section: Background and Contextmentioning

confidence: 94%

“…In refs. [15][16][17][18], the ASHRAE databases are used [19], currently a source open to the scientific community. Other studies such as [7,[20][21][22][23][24][25] are based on evidence from their field studies in which they have collected data on environmental conditions and information on subjects from comfort surveys in conditioned (HVAC) office (O) buildings, residential (R) buildings, and universities (U), except for the study [24] where the data are for a naturally ventilated (NV) building.…”

Section: Background and Contextmentioning

confidence: 99%

“…Studies have been carried out in user datasets with a minimum size of 6 subjects up to a maximum of 34. The majority of them are located in Asia, in countries with predominantly subtropical and humid continental climates, such as China [7,[23][24][25], Singapore [21,26], and Malaysia [18].…”

Section: Background and Contextmentioning

confidence: 99%

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Analysis of Variables Affecting Indoor Thermal Comfort in Mediterranean Climates Using Machine Learning

Aparicio-Ruiz,

Barbadilla-Martín,

Guadix

et al. 2023

Buildings

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To improve the energy efficiency and performance of buildings, it is essential to understand the factors that influence indoor thermal comfort. Through an extensive analysis of various variables, actions can be developed to enhance the thermal sensation of the occupants, promoting sustainability and economic benefits in conditioning systems. This study identifies eight key variables: indoor air temperature, mean radiant temperature, indoor globe temperature, CO2, age, outdoor temperature, indoor humidity, and the running mean temperature, which are relevant for predicting thermal comfort in Mediterranean office buildings. The proposed methodology effectively analyses the relevance of these variables, using five techniques and two different databases, Mediterranean climate buildings published by ASHRAE and a study conducted in Seville, Spain. The results indicate that the extended database to 21 variables improves the quality of the metrics by 5%, underscoring the importance of a comprehensive approach in the analysis. Among the evaluated techniques, random forest emerges as the most successful, offering superior performance in terms of accuracy and other metrics, and this method is highlighted as a technique that can be used to assist in the design and operation or control of a building’s conditioning system or in tools that recommend adaptive measures to improve thermal comfort.

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“…In ref. [18], accuracy indicators of 50.51% were obtained for SVC, 66% for DT, 43.3% for NB, 80.41% for KNC, 55.67% for NN, and 76.3% for BT. In ref.…”

Section: Background and Contextmentioning

confidence: 94%

Section: Background and Contextmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Variables Affecting Indoor Thermal Comfort in Mediterranean Climates Using Machine Learning

Aparicio-Ruiz,

Barbadilla-Martín,

Guadix

et al. 2023

Buildings

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“…People spend more than 90 % of their day in indoor environments, such as offices, schools, colleges, commercial buildings, industrial buildings and homes. The concern with analysing the internal habitats seeks to increase people's quality of life and health as, under normal conditions, the environment should not cause feelings of discomfort and stress for human beings [1], [2].…”

Section: Introductionmentioning

confidence: 99%

Thermal Comfort of the Environment with Internet of Things, Big Data and Machine Learning

Nascimento¹,

Lopes²

2021

Web, Internet Engineering &Amp; Signal Processing

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This research proposes to evaluate the level of thermal comfort of the environment in real time using Internet of Things (IoT), Big Data and Machine Learning (ML) techniques for collecting, storage, processing and analysis of the concerned information. The search for thermal comfort provides the best living and health conditions for human beings. The environment, as one of its functions, must present the climatic conditions necessary for human thermal comfort. In the research, wireless sensors are used to monitor the Heat Index, the Thermal Discomfort Index and the Temperature and Humidity Index of remote indoor environments to intelligently monitor the level of comfort and alert possible hazards to the people present. Machine learning algorithms are also used to analyse the history of stored data and formulate models capable of making predictions of the parameters of the environment to determine preventive actions or optimize the environment control for reducing energy consumption.

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