scholarly journals Assessment of Air Quality Perception and Its Effects on Users’ Thermal Comfort in Office Buildings

Sci ◽  
2021 ◽  
Vol 3 (4) ◽  
pp. 47
Author(s):  
Candi Citadini de Oliveira ◽  
Ricardo Forgiarini Rupp ◽  
Enedir Ghisi
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Mixed Mode ◽  
Co2 Concentration ◽  
Subtropical Climate ◽  
Indoor Environments ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Quality Perception ◽  
Central Air Conditioning

Since people spend most of their time in indoor environments, the objective of this work was to study indoor air quality and its effects on users’ thermal comfort. Based on previous data from a building with a central air-conditioning system and two mixed-mode buildings located in the humid subtropical climate of Florianópolis, southern Brazil, statistical analyses were performed. Each user’s subjective answer obtained through a questionnaire was combined with the corresponding environmental conditions measured by instruments. Results showed that improvement in air quality was associated with the reduction of air temperature and humidity ratio. Also, there was a significant influence of thermal and humidity sensation on air quality satisfaction. Users felt more satisfied or neutral with air quality for being in thermal comfort, and not because of the CO2 concentration—which means that air quality perception is influenced by factors other than CO2. This study recommends implementing an air exchange device in split air-conditioners with air recirculation commonly used in mixed-mode buildings in Brazil. It is important to provide suitable indoor ventilation to reduce pollutant concentration, ensure good air quality and prevent respiratory diseases.

scholarly journals Assessment of Air Quality Perception and Its Effects on Users’ Thermal Comfort in Office Buildings

2021 ◽  
Author(s):  
Candi Citadini de Oliveira ◽  
Ricardo Forgiarini Rupp ◽  
Enedir Ghisi
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Mixed Mode ◽  
Subtropical Climate ◽  
Indoor Environments ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Quality Perception ◽  
Central Air Conditioning ◽  
Co2 Level

Since people spend most of their time in indoor environments, the objective of this work was to study indoor air quality perception and its effects on users’ thermal comfort. Based on previous data from a building with a central air-conditioning system and two mixed-mode buildings located in the humid subtropical climate of Florianópolis, southern Brazil, statistical analyses were performed. Each user subjective answer obtained through a questionnaire was combined with the corresponding environmental conditions – measured using microclimate stations, a portable thermo-anemometer and a CO2 analyser. Results showed that improvement in air quality was associated with the reduction of air temperature and humidity ratio. Also, there was a significant influence of thermal, air movement and humidity sensation and acceptability of air quality perception. Users felt more satisfied or neutral with air quality for being in thermal comfort, and not because of the CO2 level – which means that air quality perception is influenced by factors other than CO2. This study recommends the implementation of an air exchange device in split air-conditioners with air recirculation used in mixed-mode buildings in Brazil. It is important to provide suitable indoor ventilation to reduce pollutants concentration, ensure good air quality and prevent respiratory diseases.

AI Based Energy Optimization in Association With Class Environment

2020 ◽  
Author(s):  
Kuan-Heng Yu ◽  
Emanuel Jaimes ◽  
Chi-Chuan Wang
Keyword(s):  
Air Quality ◽  
Thermal Comfort ◽  
Energy Saving ◽  
Energy Optimization ◽  
Average Energy ◽  
Co2 Concentration ◽  
Air Conditioning System ◽  
Indoor Space ◽  
Class Environment ◽  
Environment Control

Abstract This study investigates the performance of an optimal indoor environment in a campus classroom. The control system is able to regulate and balance the needs for illuminance, thermal comfort, air quality, and energy saving. By incorporating with Machine Learning and illumination algorithm associated with Internet of Things, wireless communication and adapted control, optimal energy saving and environment control can be achieved. Additionally, by using Video Image Detection to analyze the number of occupants and distribution in the classroom offers better energy optimization. In this study, the split-type air conditioning system has been used which is different from that in most literatures. About 30 tests are conducted and the occupant numbers range from 1 to 2 hours and each hour is 50 minutes. The class types include normal lecture and examination which shows completely different characteristics. The proposed AI agent contains the benefits not only for small or medium indoor space, but also for residences. In order to adjust the indoor illuminance, wireless and adjustable illuminance level LED were installed. Under the control of the illumination algorithm, the illuminance of each area of the classroom can be optimized according to the occupant distribution. The test results indicate that, by maintaining thermal comfort and air quality, when comparing with fixed setting point control 25 degrees, the average energy saving is 19%, and the average CO2 concentration is decreased by 21.3%. When comparing with setting point temperature of 26 degrees, the average energy saving is 15% the average CO2 is decreased by 12.9%.

A dynamic model for human thermal comfort for smart building applications

2019 ◽  
Vol 234 (4) ◽  
pp. 472-483
Author(s):  
Ghezlane Halhoul Merabet ◽  
Mohamed Essaaidi ◽  
Driss Benhaddou
Keyword(s):  
Thermal Comfort ◽  
Air Conditioning ◽  
Research Work ◽  
Comfort Level ◽  
Indoor Environments ◽  
Air Conditioning System ◽  
Human Thermal Comfort ◽  
Local Quality ◽  
Set Up ◽  
Air Conditioning Systems

Thermal comfort is closely related to the evaluation of heating, ventilation, and air conditioning systems. It can be seen as the result of the perception of the occupants of a given environment, and it is the product of the interaction of a number of personal and environmental factors. Otherwise, comfort issues still do not play an important role in the daily operation of commercial buildings. However, in the workplace, local quality effects, in addition to the health, the productivity that has a significant impact on the performance of the activities. In this regard, researchers have conducted, for decades, investigations related to thermal comfort and indoor environments, which includes developing models and indices through experimentations to establish standards to evaluate comfort and factors and set-up parameters for heating, ventilation, and air conditioning systems. However, to our best knowledge, most of the research work reported in the literature deals only with parameters that are not dynamically tracked. This work aims to propose a prototype for comfort measuring through a wireless sensor network and then presenting a model for thermal comfort prediction. The developed model can be used to set up a heating, ventilation, and air conditioning system to meet the expected comfort level. In particular, the obtained results show that there is a strong correlation between users’ comfort and variables such as age, gender, and body mass index as a function of height and weight.

scholarly journals Personal Atmosphere: Estimation of Air Conditioner Parameters for Personalizing Thermal Comfort

2020 ◽  
Vol 10 (22) ◽  
pp. 8067
Author(s):  
Tomohiro Mashita ◽  
Tetsuya Kanayama ◽  
Photchara Ratsamee
Keyword(s):  
Machine Learning ◽  
Thermal Comfort ◽  
Air Temperature ◽  
Fine Tuning ◽  
Air Conditioner ◽  
Data Set ◽  
Air Conditioning System ◽  
Air Conditioners ◽  
Uniform Distributions ◽  
Machine Learning Model

Air conditioners enable a comfortable environment for people in a variety of scenarios. However, in the case of a room with multiple people, the specific comfort for a particular person is highly dependent on their clothes, metabolism, preference, and so on, and the ideal conditions for each person in a room can conflict with each other. An ideal way to resolve these kinds of conflicts is an intelligent air conditioning system that can independently control air temperature and flow at different areas in a room and then produce thermal comfort for multiple users, which we define as the personal preference of air flow and temperature. In this paper, we propose Personal Atmosphere, a machine learning based method to obtain parameters of air conditioners which generate non-uniform distributions of air temperature and flow in a room. In this method, two dimensional air-temperature and -flow distributions in a room are used as input to a machine learning model. These inputs can be considered a summary of each user’s preference. Then the model outputs a parameter set for air conditioners in a given room. We utilized ResNet-50 as the model and generated a data set of air temperature and flow distributions using computational fluid dynamics (CFD) software. We then conducted evaluations with two rooms that have two and four air conditioners under the ceiling. We then confirmed that the estimated parameters of the air conditioners can generate air temperature and flow distributions close to those required in simulation. We also evaluated the performance of a ResNet-50 with fine tuning. This result shows that its learning time is significantly decreased, but performance is also decreased.

scholarly journals A Review of Thermal Comfort Applied in Bus Cabin Environments

2020 ◽  
Vol 10 (23) ◽  
pp. 8648
Author(s):  
Matheus das Neves Almeida ◽  
Antonio Augusto de Paula Xavier ◽  
Ariel Orlei Michaloski
Keyword(s):  
Thermal Comfort ◽  
Thermal Sensation ◽  
Co2 Concentration ◽  
Indoor Environments ◽  
Air Velocity ◽  
Research Papers ◽  
Current State ◽  
Energy Economy ◽  
Standard Models ◽  
The Impact

As of 2020, it has been 50 years since the publication of Fanger’s predictive model of thermal comfort that was designed for indoor environments and attention worldwide is directed at the COVID-19 pandemic and discussions around recommendations for these indoor environments. In this context, many environments and their occupants will suffer consequences related to thermal comfort due to the necessary indoor air changes. In bus cabins, the impact might be even greater, seeing that they are responsible for the mass transportation of people. Thus, this paper intends to review the studies on thermal comfort that analyzed bus cabin environments. It adapts the PRISMA methodology and, as a result, it includes 22 research papers published in journals. Among those, 73% focused on approaching the occupants’ thermal sensation, followed by fuel/energy economy (18%), and driver productivity (9%). The current state-of-the-art indicates that air temperature and air velocity were the parameters most employed by the included studies, but eight papers analyzed all six parameters of the standard models of thermal comfort. The most employed model of thermal comfort was Fanger’s, but there has not been an investigation that assesses its consistency in predicting the occupants’ thermal sensation in the explored environment. Nevertheless, the analyzed studies recommended constant air change inside closed buses or keeping them open to minimize adverse effects on the occupants’ health, especially due to airborne diseases and CO2 concentration possibly being a suitable indicator to identify the need for air change.

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