scholarly journals Determining Favourable and Unfavourable Thermal Areas in Seoul Using In-Situ Measurements: A Preliminary Step towards Developing a Smart City

Energies ◽  
2019 ◽  
Vol 12 (12) ◽  
pp. 2320 ◽  
Author(s):  
You Jin Kwon ◽  
Dong Kun Lee ◽  
Kiseung Lee
Keyword(s):  
Land Cover ◽  
Thermal Comfort ◽  
Thermal Environment ◽  
Smart Cities ◽  
Distribution Data ◽  
Sensible Heat ◽  
Residential Areas ◽  
Coverage Ratio ◽  
Study Results

Urban heat island effects (UHIE) are becoming increasingly widespread, thus, there is an urgent need to address thermal comfort, which significantly influences the daily lives of people. In this study, a means of improving the thermal environment by spatial analysis of heat was implemented to ensure basic thermal comfort in future smart cities. Using Seoul as the study site, the relationship between sensible heat and land cover type was used to identify heat islands in this city. Thereafter, k-means clustering was employed to extract unfavourable and favourable thermal areas. High sensible heat indicates locations where environmental heat needs to be mitigated. Sensible heat distribution data were used for spatial typification to formulate an effective land cover factor to mitigate the UHIE. In-situ net radiation data measured at six sites were utilised to confirm the spatial typification of the thermal environment. It was found that expanding the green space by 1% reduces the sensible heat by 4.9 W/m2. Further, the building coverage ratio and green coverage influence the sensible heat in compact residential areas. The study results can be used to establish spatial planning standards to improve the thermal environments of sustainable cities.

scholarly journals The Effects of Different Space Forms in Residential Areas on Outdoor Thermal Comfort in Severe Cold Regions of China

2019 ◽  
Vol 16 (20) ◽  
pp. 3960 ◽  
Author(s):  
Zheming Liu ◽  
Yumeng Jin ◽  
Hong Jin
Keyword(s):  
Thermal Comfort ◽  
Thermal Environment ◽  
Residential Areas ◽  
The South ◽  
Cold Regions ◽  
Space Forms ◽  
Transition Season ◽  
Outdoor Spaces ◽  
Outdoor Space ◽  
Comfort Condition

In the context of global climate change and accelerated urbanization, the deterioration of the urban living environment has had a serious negative impact on the life of residents. However, studies on the effects of forms and configurations of outdoor spaces in residential areas on the outdoor thermal environment based on the particularity of climate in severe cold regions are very limited. Through field measurements of the thermal environment at the pedestrian level in the outdoor space of residential areas in three seasons (summer, the transition season and winter) in Harbin, China, this study explored the effects of forms and configurations of three typical outdoor spaces (the linear block, the enclosed block, and the square) on the thermal environment and thermal comfort using the Physiologically Equivalent Temperature (PET). The results show that the thermal environment of all outdoor space forms was relatively comfortable in the transition season but was uncomfortable in summer and winter. The full-enclosed block with a lower sky view factor (SVF) had a higher thermal comfort condition in summer and winter. The linear block with higher buildings and wider south–north spacing had a higher thermal comfort condition in summer and winter. When the buildings on the south side were lower and the south–north spacing was wider, the thermal environment of the square was more comfortable in winter.

scholarly journals Enhancement of Thermal Comfort Inside the Kitchen of Non-Airconditioned Railway Pantry Car

2021 ◽  
Vol 39 (1) ◽  
pp. 275-291
Author(s):  
Md Sarfaraz Alam ◽  
Urmi Ravindra Salve
Keyword(s):  
Thermal Comfort ◽  
Air Temperature ◽  
Thermal Environment ◽  
Thermal Sensation ◽  
Ventilation System ◽  
Comfort Level ◽  
Air Velocity ◽  
Sustainable Improvement ◽  
Study Results ◽  
Air Ventilation

There are ample literature studies available, focusing on hot-humid built environment, which have achieved an increase in thermal comfort conditions by proper installation of ventilation-systems. The present thermal comfort study has been carried out in the kitchen environment of a non-air-conditioned railway pantry car in Indian Railways. The purpose is to enhance thermal comfort level under the currently applied ventilation system inside the kitchen of pantry car by determining the standard effective temperature (SET) index. During the summer and winter seasons, a field study was carried out to obtain the value of air temperature, globe temperature, relative humidity, and air velocity inside the pantry car for estimation of the SET index. A computational fluid dynamics (CFD) analysis was used to obtain a better-modified case model of the pantry car kitchen for the improvement of thermal comfort. The design interventions for the pantry car kitchen were created, with emphasis on increasing energy efficiency based on low-power consumption air ventilation system. The study results indicated that, modified case-I model has a better ventilation design concept as compare to the existing and other models, which increased the air velocity and significantly decreased the air temperature inside the kitchen of pantry car at all cooking periods. A value of SET (28.6–30℃) was found with a comfortable thermal sensation within all cooking periods, which is better for the pantry car workers. This finding suggests a sustainable improvement in the thermal environment of the "non-air-conditioned" pantry car kitchen in the Indian Railways, which can be applied immediately.

scholarly journals Comparison of Accuracy of Surface Temperature Images from Unmanned Aerial Vehicle and Satellite for Precise Thermal Environment Monitoring of Urban Parks Using In Situ Data

2021 ◽  
Vol 13 (10) ◽  
pp. 1977
Author(s):  
Dongwoo Kim ◽  
Jaejin Yu ◽  
Jeongho Yoon ◽  
Seongwoo Jeon ◽  
Seungwoo Son
Keyword(s):  
Land Cover ◽  
Unmanned Aerial Vehicle ◽  
Thermal Environment ◽  
Green Spaces ◽  
Urban Park ◽  
Land Cover Type ◽  
Land Cover Types ◽  
Cover Type ◽  
Aerial Vehicle

Rapid urbanization has led to several severe environmental problems, including so-called heat island effects, which can be mitigated by creating more urban green spaces. However, the temperature of various surfaces differs and precise measurement and analyses are required to determine the “coolest” of these. Therefore, we evaluated the accuracy of surface temperature data based on thermal infrared (TIR) cameras mounted on unmanned aerial vehicles (UAVs), which have recently been utilized for the spatial analysis of surface temperatures. Accordingly, we investigated land surface temperatures (LSTs) in green spaces, specifically those of different land cover types in an urban park in Korea. We compared and analyzed LST data generated by a thermal infrared (TIR) camera mounted on an unmanned aerial vehicle (UAV) and LST data from the Landsat 8 satellite for seven specific periods. For comparison and evaluation, we measured in situ LSTs using contact thermometers. The UAV TIR LST showed higher accuracy (R2 0.912, root mean square error (RMSE) 3.502 °C) than Landsat TIR LST accuracy (R2 value lower than 0.3 and RMSE of 7.246 °C) in all periods. The Landsat TIR LST did not show distinct LST characteristics by period and land cover type; however, grassland, the largest land cover type in the study area, showed the highest accuracy. With regard to the accuracy of the UAV TIR LST by season, the accuracy was higher in summer and spring (R2 0.868–0.915, RMSE 2.523–3.499 °C) than in autumn and winter (R2 0.766–0.79, RMSE 3.834–5.398 °C). Some land cover types (concrete bike path, wooden deck) were overestimated, showing relatively high total RMSEs of 4.439 °C and 3.897 °C, respectively, whereas grassland, which has lower LST, was underestimated—showing a total RMSE of 3.316 °C. Our results showed that the UAV TIR LST could be measured with sufficient reliability for each season and land cover type in an urban park with complex land cover types. Accordingly, our results could contribute to decision-making for urban spaces and environmental planning in consideration of the thermal environment.

scholarly journals TEMPORAL AND SPATIAL ANALYSIS OF URBAN HEAT ISLAND USING LANDSAT SATELLITE DATA: TWO INDIAN CASE STUDIES

2018 ◽  
Vol IV-5 ◽  
pp. 71-78
Author(s):  
K. Dutta ◽  
D. Basu ◽  
S. Agrawal
Keyword(s):  
Land Cover ◽  
Rural Areas ◽  
Land Surface ◽  
Thermal Environment ◽  
Smart Cities ◽  
Vegetation Indices ◽  
Urban Heat Islands ◽  
Spatiotemporal Pattern ◽  
Heat Islands ◽  
Urban Heat

<p><strong>Abstract.</strong> Urban environment is examined through time series Landsat TM (Thematic Mapper) and OLI/TIRS (Operational Land Imager &amp;amp; Thermal Infrared Sensor) sensor images. A continuous surface of Land Surface Temperature (LST) can be extracted from Landsat thermal bands. Similarly different band combinations and ratios will give spatial pattern of land cover categories. Among these, building and vegetation indices are used to characterize the spatiotemporal pattern of elevated temperature zones in cities. This excess heat concentration creates thermal hotspots which are known as Urban Heat Islands (UHI). Parameters of land cover are then related to LST to detect the influence of urbanization on intensity and extent of heat islands, by pixel based quantitative analysis. This paper focuses on two megacities of India and their surrounding districts for identifying the critical UHI areas. The purpose of this paper is to create a database for reconstruction in old cities and planning of new smart cities. Results suggest that urban sprawl and substitution of rural areas with impervious surface plays significant role in microclimate, causing formation of new thermal hotspots. The analysis of urban thermal environment and its dynamics is to provide a scientific basis for future strategy building.</p>

SenCity – Evaluating Users’ Experiences of Intelligent Lighting for Well-Being in Smart Cities

2018 ◽  
pp. 60-67
Author(s):  
Henrika Pihlajaniemi ◽  
Anna Luusua ◽  
Eveliina Juntunen
Keyword(s):  
Case Studies ◽  
Smart City ◽  
Smart Cities ◽  
Well Being ◽  
Evaluation Methods ◽  
Data Analyses ◽  
Sense Of Security ◽  
System Data

This paper presents the evaluation of usersХ experiences in three intelligent lighting pilots in Finland. Two of the case studies are related to the use of intelligent lighting in different kinds of traffic areas, having emphasis on aspects of visibility, traffic and movement safety, and sense of security. The last case study presents a more complex view to the experience of intelligent lighting in smart city contexts. The evaluation methods, tailored to each pilot context, include questionnaires, an urban dashboard, in-situ interviews and observations, evaluation probes, and system data analyses. The applicability of the selected and tested methods is discussed reflecting the process and achieved results.

scholarly journals Evaluating the Connection between Thermal Comfort and Productivity in Buildings: A Systematic Literature Review

Buildings ◽  
2021 ◽  
Vol 11 (6) ◽  
pp. 244
Author(s):  
Ana Maria Bueno ◽  
Antonio Augusto de Paula Xavier ◽  
Evandro Eduardo Broday
Keyword(s):  
Literature Review ◽  
Thermal Comfort ◽  
Systematic Literature Review ◽  
Thermal Environment ◽  
Final Analysis ◽  
Personal Factors ◽  
Exclusion Criteria ◽  
Environment Variables ◽  
Main Factors ◽  
Combination Of Methods

The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Throughout the years, research has described the connection between thermal comfort and productivity. Mathematical models have been established in the attempt to predict changes in productivity according to thermal variations in the environment. Some of these models have failed for a number of reasons, including the understanding of the effect that several environment variables have had on performance. From this context, a systematic literature review was carried out with the aim of verifying the connection between thermal comfort and productivity and the combinations of different thermal and personal factors that can have an effect on productivity. A hundred and twenty-eight articles were found which show a connection between productivity and some thermal comfort variables. By means of specific inclusion and exclusion criteria, 60 articles were selected for a final analysis. The main conclusions found in this study were: (i) the vast majority of research uses subjective measures and/or a combination of methods to evaluate productivity; (ii) performance/productivity can be attained within an ampler temperature range; (iii) few studies present ways of calculating productivity.

scholarly journals Development of a Deep Neural Network Model for Estimating Joint Location of Occupant Indoor Activities for Providing Thermal Comfort

Energies ◽  
2021 ◽  
Vol 14 (3) ◽  
pp. 696
Author(s):  
Eun Ji Choi ◽  
Jin Woo Moon ◽  
Ji-hoon Han ◽  
Yongseok Yoo
Keyword(s):  
Neural Network ◽  
Thermal Comfort ◽  
Deep Neural Network ◽  
Mean Squared Error ◽  
Thermal Environment ◽  
The Body ◽  
Estimation Accuracy ◽  
Accurate Estimation ◽  
Body Parts ◽  
Joint Location

The type of occupant activities is a significantly important factor to determine indoor thermal comfort; thus, an accurate method to estimate occupant activity needs to be developed. The purpose of this study was to develop a deep neural network (DNN) model for estimating the joint location of diverse human activities, which will be used to provide a comfortable thermal environment. The DNN model was trained with images to estimate 14 joints of a person performing 10 common indoor activities. The DNN contained numerous shortcut connections for efficient training and had two stages of sequential and parallel layers for accurate joint localization. Estimation accuracy was quantified using the mean squared error (MSE) for the estimated joints and the percentage of correct parts (PCP) for the body parts. The results show that the joint MSEs for the head and neck were lowest, and the PCP was highest for the torso. The PCP for individual activities ranged from 0.71 to 0.92, while typing and standing in a relaxed manner were the activities with the highest PCP. Estimation accuracy was higher for relatively still activities and lower for activities involving wide-ranging arm or leg motion. This study thus highlights the potential for the accurate estimation of occupant indoor activities by proposing a novel DNN model. This approach holds significant promise for finding the actual type of occupant activities and for use in target indoor applications related to thermal comfort in buildings.

scholarly journals Evaluating Machine Learning and Geostatistical Methods for Spatial Gap-filling of Monthly ESA CCI Soil Moisture in China

2021 ◽  
Vol 13 (14) ◽  
pp. 2848
Author(s):  
Hao Sun ◽  
Qian Xu
Keyword(s):  
Machine Learning ◽  
Soil Moisture ◽  
Water Resource Management ◽  
Large Scale ◽  
Gap Filling ◽  
Spatial Continuity ◽  
Study Results ◽  
Data Gaps

Obtaining large-scale, long-term, and spatial continuous soil moisture (SM) data is crucial for climate change, hydrology, and water resource management, etc. ESA CCI SM is such a large-scale and long-term SM (longer than 40 years until now). However, there exist data gaps, especially for the area of China, due to the limitations in remote sensing of SM such as complex topography, human-induced radio frequency interference (RFI), and vegetation disturbances, etc. The data gaps make the CCI SM data cannot achieve spatial continuity, which entails the study of gap-filling methods. In order to develop suitable methods to fill the gaps of CCI SM in the whole area of China, we compared typical Machine Learning (ML) methods, including Random Forest method (RF), Feedforward Neural Network method (FNN), and Generalized Linear Model (GLM) with a geostatistical method, i.e., Ordinary Kriging (OK) in this study. More than 30 years of passive–active combined CCI SM from 1982 to 2018 and other biophysical variables such as Normalized Difference Vegetation Index (NDVI), precipitation, air temperature, Digital Elevation Model (DEM), soil type, and in situ SM from International Soil Moisture Network (ISMN) were utilized in this study. Results indicated that: 1) the data gap of CCI SM is frequent in China, which is found not only in cold seasons and areas but also in warm seasons and areas. The ratio of gap pixel numbers to the whole pixel numbers can be greater than 80%, and its average is around 40%. 2) ML methods can fill the gaps of CCI SM all up. Among the ML methods, RF had the best performance in fitting the relationship between CCI SM and biophysical variables. 3) Over simulated gap areas, RF had a comparable performance with OK, and they outperformed the FNN and GLM methods greatly. 4) Over in situ SM networks, RF achieved better performance than the OK method. 5) We also explored various strategies for gap-filling CCI SM. Results demonstrated that the strategy of constructing a monthly model with one RF for simulating monthly average SM and another RF for simulating monthly SM disturbance achieved the best performance. Such strategy combining with the ML method such as the RF is suggested in this study for filling the gaps of CCI SM in China.

scholarly journals How Can We Adapt Thermal Comfort for Disabled Patients? A Case Study of French Healthcare Buildings in Summer

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4530
Author(s):  
Youcef Bouzidi ◽  
Zoubayre El Akili ◽  
Antoine Gademer ◽  
Nacef Tazi ◽  
Adil Chahboun
Keyword(s):  
Thermal Comfort ◽  
Thermal Environment ◽  
Residential Buildings ◽  
Linear Regression Analysis ◽  
Environmental Parameters ◽  
Physical Parameters ◽  
Mean Radiant Temperature ◽  
Operative Temperature ◽  
Neutral Temperature ◽  
Radiant Temperature

This paper investigates adaptive thermal comfort during summer in medical residences that are located in the French city of Troyes and managed by the Association of Parents of Disabled Children (APEI). Thermal comfort in these buildings is evaluated using subjective measurements and objective physical parameters. The thermal sensations of respondents were determined by questionnaires, while thermal comfort was estimated using the predicted mean vote (PMV) model. Indoor environmental parameters (relative humidity, mean radiant temperature, air temperature, and air velocity) were measured using a thermal environment sensor during the summer period in July and August 2018. A good correlation was found between operative temperature, mean radiant temperature, and PMV. The neutral temperature was determined by linear regression analysis of the operative temperature and Fanger’s PMV model. The obtained neutral temperature is 23.7 °C. Based on the datasets and questionnaires, the adaptive coefficient α representing patients’ capacity to adapt to heat was found to be 1.261. A strong correlation was also observed between the sequential thermal index n(t) and the adaptive temperature. Finally, a new empirical model of adaptive temperature was developed using the data collected from a longitudinal survey in four residential buildings of APEI in summer, and the obtained adaptive temperature is 25.0 °C with upper and lower limits of 24.7 °C and 25.4 °C.

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