scholarly journals Analysis of Thermal Effects of Roof Material on Indoor Temperature and Thermal Comfort

2020 ◽  
Vol 10 (5) ◽  
pp. 2068
Author(s):  
Remon Lapisa ◽  
Arwizet K ◽  
Martias ◽  
Purwantono ◽  
Wakhinuddin ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Thermal Effects ◽  
Indoor Temperature ◽  
Roof Material

scholarly journals Analysis of EEG, Cardiac Activity Status, and Thermal Comfort According to the Type of Cooling Seat during Rest in Indoor Temperature

2020 ◽  
Vol 11 (1) ◽  
pp. 97
Author(s):  
Yunchan Shin ◽  
Minjung Lee ◽  
Honghyun Cho
Keyword(s):  
Thermal Comfort ◽  
Human Body ◽  
Cold Water ◽  
Cardiac Activity ◽  
Satisfaction Level ◽  
Water Cooling ◽  
Indoor Temperature ◽  
Sensorimotor Rhythm ◽  
Electroencephalogram Eeg ◽  
Wave Reduction

In this study, electroencephalogram (EEG) and cardiac activity status of the human body while using various types of seats during rest were analyzed in indoor summer conditions. Thermal comfort was also evaluated through a subjective survey. The EEG, cardiac activity status, and subjective survey during rest indicated that the use of ventilation and cold water-cooling seats was effective. This effectiveness was because of the θ-wave and α-wave activation, sensorimotor rhythm, β-wave reduction, and left hemisphere activation, demonstrating that the conditions applied were suitable for rest. According to the analysis of the subjective questionnaire survey, the use of ventilation and cold water-cooling seats provided a more pleasant state than the basic seat, improving the subject’s warmth and comfort, and also the concentration. In addition, the use of a cold water-cooling seat provided the highest satisfaction level, being the most favorable condition for rest.

scholarly journals Study and Application of Industrial Thermal Comfort Parameters by Using Bayesian Inference Techniques

2021 ◽  
Vol 11 (24) ◽  
pp. 11979
Author(s):  
Patricia I. Benito ◽  
Miguel A. Sebastián ◽  
Cristina González-Gaya
Keyword(s):  
Bayesian Networks ◽  
Thermal Comfort ◽  
Interior Design ◽  
Intelligent Systems ◽  
Design Parameters ◽  
Data Sets ◽  
Work Time ◽  
Indoor Temperature ◽  
Inference Techniques ◽  
Comfort Parameters

This paper focuses on the use of Bayesian networks for the industrial thermal comfort issue, specifically in industries in Northern Argentina. Mined data sets that are analyzed and exploited with WEKA and ELVIRA tools are discussed. Thus, networks giving the predictive value of thermal comfort for different pairs of indoor temperature and humidity values according to activity, time, and season, verified in the workplace, were obtained. The results obtained were compared to other statistical models of linear regression used for thermal comfort, thus observing that comfort temperature values are within a same range, yet the network offered more information since a range of options for interior design parameters (temperature/relative humidity) was offered for different work, time, and season conditions. Additionally, if compared with static models of heat exchange, the contribution of Bayesian networks is noted when considering a context of actual operability and adaptability conditions to the environment, which is promising for developing thermal comfort intelligent systems, especially for the development of sustainable settings within the Industry 4.0 paradigm.

scholarly journals Determination of the Effect of Green Roofs on Indoor Temperature by the Use of Simulation in a Tropical Landscape

2020 ◽  
pp. 12-28
Author(s):  
D. Nyame-Tawiah ◽  
L. Attuah ◽  
C. Koranteng
Keyword(s):  
Thermal Comfort ◽  
Leaf Area ◽  
Study Design ◽  
Soil Depth ◽  
Real Life ◽  
Green Roofs ◽  
Simulation Design ◽  
Indoor Temperature ◽  
Soil Thickness ◽  
Construction Methods

Aims: To use a simulation base exploration to carry out 6 scenarios of green roof construction methods to determine the most efficient in improving indoor thermal comfort. Study Design: Simulation Design was used as the study design. Place and Duration of Study: The study was conducted at the Department of Horticulture – Kwame Nkrumah University of Science and Technology located at Kumasi-Ghana between 2016 and 2019. Methodology: A simulation experimental setup was done to run for 1 year to cover the two seasons in Ghana. Version 5.0.2 Design Builder and Energy Plus 5.8 was used to work on 6 scenarios using leaf area indexes (LAI) of 2 and 5 as well as soil depth (thickness) of (70-150 mm), 200 mm, 300 mm and 500 mm. Also a real life experiment was done at the Department of Horticulture by constructing 9 test cells and using treatments such as Portulaca grandiflora and Setcreasea purpurea to validate the results for the simulation. The time setup for the simulation was from 12.00 am to 11.59 pm. Results: A leaf area indexes (LAI) of 5 and soil depth of 70 mm-150 mm recorded the lowest simulated temperature ranging from 26.26°C to 29.30°C for scenario one. For scenario two, a leaf area indexes (LAI) of 5 and a soil depth of 200mm recorded the lowest significantly (P≤0.05) indoor temperature in August (26.20°C) and the highest (29.26°C) in March. In February, June and August, significant differences (P≤0.05) were achieved by leaf area indexes (LAI) 5 and soil thickness 500 mm for scenario three. January, March to July indicated significant differences (P≤0.05) between the treatments leaf area indexes (LAI) 2 and soil thickness 300 mm and leaf area indexes (LAI) 5 and soil depth of 300 mm recorded 26.32°C to 29.33°C for August and March respectively for scenario four. A soil depth of 500 mm and leaf area indexes (LAI) of 2 gave significantly (P≤0.05) low temperatures indoors all year (26.27 to 29.32°C) for scenario five and in August leaf area indexes (LAI) 5 and soil thickness of 500 mm recorded the least temperature all year for scenario six. Conclusion: From the exploration, a soil depth of 70 mm – 150 mm and a LAI of 5, LAI of 5 and soil depth of 200 mm and LAI of 2 and soil depth of 500 mm achieved the lowest temperature and performed better in terms of temperature reduction which will lead to thermal comfort of occupants.

scholarly journals Energy Use and Perceived Indoor Environment in a Swedish Multifamily Building before and after Major Renovation

2018 ◽  
Vol 10 (3) ◽  
pp. 766 ◽  
Author(s):  
Lina La Fleur ◽  
Patrik Rohdin ◽  
Bahram Moshfegh
Keyword(s):  
Energy Efficiency ◽  
Thermal Comfort ◽  
Indoor Environment ◽  
Energy Use ◽  
Ventilation System ◽  
Heating Season ◽  
Indoor Temperature ◽  
Exhaust Ventilation ◽  
Heat Demand ◽  
Before And After

Improved energy efficiency in the building sector is a central goal in the European Union and renovation of buildings can significantly improve both energy efficiency and indoor environment. This paper studies the perception of indoor environment, modelled indoor climate and heat demand in a building before and after major renovation. The building was constructed in 1961 and renovated in 2014. Insulation of the façade and attic and new windows reduced average U-value from 0.54 to 0.29 W/m2·K. A supply and exhaust ventilation system with heat recovery replaced the old exhaust ventilation. Heat demand was reduced by 44% and maximum supplied heating power was reduced by 38.5%. An on-site questionnaire indicates that perceived thermal comfort improved after the renovation, and the predicted percentage dissatisfied is reduced from 23% to 14% during the heating season. Overall experience with indoor environment is improved. A sensitivity analysis indicates that there is a compromise between thermal comfort and energy use in relation to window solar heat gain, internal heat generation and indoor temperature set point. Higher heat gains, although reducing energy use, can cause problems with high indoor temperatures, and higher indoor temperature might increase thermal comfort during heating season but significantly increases energy use.

Research and Investigation on Present Energy Conservation in Northen Rural Houses

2012 ◽  
Vol 608-609 ◽  
pp. 1709-1715
Author(s):  
Gang Li ◽  
Zhen Li ◽  
Guo Hui Feng ◽  
Qian Liu ◽  
Qian Wang
Keyword(s):  
Energy Consumption ◽  
Energy Conservation ◽  
Thermal Comfort ◽  
Rural Areas ◽  
Field Investigation ◽  
Temperature Uniformity ◽  
Water Heater ◽  
Indoor Temperature ◽  
Indoor Thermal Comfort ◽  
Effective Use

More researchers pay attention to energy consumption and conservation in cities before .This paper finds out the current structure of rural houses, the type and utilizing situation of energy and the typical heating methods in current northen rural areas of china, and points out exist problems in these aspects by Field investigation and analysis.The author proposes that insulation layers of external walls and windows are preferred measures of energy consrevation and the additional southen sunspace is also a highly effective method in northen rural areas; the hanging kang has a much better performance than landing kang which should be strongly recommended,and a new kang with PCM helps to save fuel consumption, increase temperature uniformity of kang surface , stabilize indoor temperature and improve indoor thermal comfort,which should be paid enough attention; the effective use of excess heat in solar energy water heater should be paid sufficient attention,and biogas should be strongly advocated and properly guided.

scholarly journals Estimating Smart Wi-Fi Thermostat-Enabled Thermal Comfort Control Savings for Any Residence

2021 ◽  
Vol 3 (4) ◽  
pp. 743-760
Author(s):  
Abdulelah D. Alhamayani ◽  
Qiancheng Sun ◽  
Kevin P. Hallinan
Keyword(s):  
Thermal Comfort ◽  
Energy Savings ◽  
Short Term Memory ◽  
Control Strategies ◽  
Residential Buildings ◽  
Real Data ◽  
Accurate Estimation ◽  
Indoor Temperature ◽  
Vote Model ◽  
Neural Network Algorithm

Nowadays, most indoor cooling control strategies are based solely on the dry-bulb temperature, which is not close to a guarantee of thermal comfort of occupants. Prior research has shown cooling energy savings from use of a thermal comfort control methodology ranging from 10 to 85%. The present research advances prior research to enable thermal comfort control in residential buildings using a smart Wi-Fi thermostat. “Fanger’s Predicted Mean Vote model” is used to define thermal comfort. A machine learning model leveraging historical smart Wi-Fi thermostat data and outdoor temperature is trained to predict indoor temperature. A Long Short-Term-Memory neural network algorithm is employed for this purpose. The model considers solar heat input estimations to a residence as input features. The results show that this approach yields a substantially improved ability to accurately model and predict indoor temperature. Secondly, it enables a more accurate estimation of potential savings from thermal comfort control. Cooling energy savings ranging from 33 to 47% are estimated based upon real data for variable energy effectiveness and solar exposed residences.

scholarly journals Trade-off between Energy Consumption and Comfort Experience in Smart Buildings

2015 ◽  
Vol 44 (4) ◽  
pp. 420-432 ◽  
Author(s):  
Domen Zupančič ◽  
Mitja Luštrek ◽  
Matjaž Gams
Keyword(s):  
Energy Consumption ◽  
Thermal Comfort ◽  
Prediction Algorithm ◽  
Reactive Control ◽  
Indoor Temperature ◽  
Point Temperature ◽  
Set Point ◽  
Multi Agent ◽  
The Difference ◽  
Occupancy Prediction

The thermal comfort experience in conditioned environments is closely related to the indoor temperature andvaries mainly due to the dynamics of occupant state and the environmental state. The heating or cooling required to achievethe desired temperature and comfort influences the energy consumption. This article presents a multi-agent control systemthat primarily regulates thermal comfort rather than the indoor temperature. We developed this comfort regulator that basedon (i) the difference between the desired level of comfort and the current level of comfort and (ii) the difference between thecurrent temperature and the set point temperature adapts the set point temperature in order to achieve the desired comfort.An occupancy prediction algorithm and expert rules were designed to efficiently reduce unnecessary energy consumptionduring periods when the home is not occupied and the comfort experience is therefore not important. The results of experimentsare presented in a comfort/energy-consumption space. The comfort/energy-consumption space shows how the finalresult is influenced by (i) different versions of learning algorithms and (ii) different comfort threshold values. Comparingthe comfort/energy-consumption spaces for different occupancy patterns shows that the rule settings have similar impact oncontrol performance, which indicates that the rules are general. In nearly all experiments, the proposed multi-agent controlsystem assured better comfort experience with small increase of energy consumption compared to reactive control system.DOI: http://dx.doi.org/10.5755/j01.itc.44.4.10139

scholarly journals Models for Prediction of Daily Mean Indoor Temperature and Relative Humidity: Education Building in Izmir, Turkey

2011 ◽  
Vol 21 (6) ◽  
pp. 772-781 ◽  
Author(s):  
Türkan Göksal Özbalta ◽  
Alper Sezer ◽  
Yusuf Yıldız
Keyword(s):  
Relative Humidity ◽  
Thermal Comfort ◽  
Mean Squared Error ◽  
System Size ◽  
Climatic Conditions ◽  
Ann Model ◽  
Data Logger ◽  
Indoor Temperature ◽  
Indoor Thermal Comfort ◽  
Comfort Parameters

In this research, several models were developed to forecast the daily mean indoor temperature (IT) and relative humidity values in an education building in Izmir, Turkey. The city is located at a hot–humid climatic region. In order to forecast the IT and internal relative humidity (IRH) parameters in the building, a number of artificial neural networks (ANN) models were trained and tested with a dataset including outdoor climatic conditions, day of year and indoor thermal comfort parameters. The indoor thermal comfort parameters, namely, IT and IRH values between 6 June and 21 September 2009 were collected via HOBO data logger. Fraction of variance ( R2) and root-mean squared error values calculated by the use of the outputs of different ANN architectures were compared. Moreover, several multiple regression models were developed to question their performance in comparison with those of ANNs. The results showed that an ANN model trained with inconsiderable amount of data was successful in the prediction of IT and IRH parameters in education buildings. It should be emphasized that this model can be benefited in the prediction of indoor thermal comfort conditions, energy requirements, and heating, ventilating and air conditioning system size.

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