Indoor Temperature Variations in Swedish Households: Implications for Thermal Comfort

2018 ◽  
pp. 835-845
Author(s):  
Despoina Teli ◽  
Sarka Langer ◽  
Lars Ekberg ◽  
Jan-Olof Dalenbäck
Keyword(s):  
Thermal Comfort ◽  
Indoor Temperature ◽  
Temperature Variations

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 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 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 Development and Application of a Thermal Comfort Model in Buildings

2022 ◽  
Vol 7 ◽  
pp. 1
Author(s):  
Andrés Vilaboa Díaz ◽  
Pastora M. Bello Bugallo
Keyword(s):  
Mathematical Model ◽  
Thermal Comfort ◽  
Thermal Inertia ◽  
Design Parameters ◽  
System Response ◽  
Thermal Mass ◽  
The European Union ◽  
External Temperature ◽  
Temperature Variations ◽  
The Mathematical Model

Buildings are one of the systems that more energy consumed in the European Union. The study of the thermal envelope is interesting in order to reduce the energy losses. For that, a mathematical model able to predict the system response to external temperature variations is developed. With the mathematical model, different thermal envelope elements of a building based on the lag and the cushioning of the resultant wave can be characterized. In addition, it is important to analyse where the insulation is placed, because when the insulation is outside and the thermal mass is inside, the system produces a response with smooth temperature variations than when the insulation is inside. Therefore, placing the outside insulation generates more steady indoor temperatures, increasing the thermal comfort inside the building. To complete the mathematical model that allows predicting the temperature inside a building taking into account the solar inputs and the thermal inertia of the building. This study will help to establish the optimum design parameters in order to build sustainable and comfortable buildings. Furthermore, it will take one step forward in the construction of nearly Zero-Energy Buildings.

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.

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