scholarly journals Towards a Generic Residential Building Model for Heat–Health Warning Systems

2021 ◽  
Vol 18 (24) ◽  
pp. 13050
Author(s):  
Jens Pfafferott ◽  
Sascha Rißmann ◽  
Guido Halbig ◽  
Franz Schröder ◽  
Sascha Saad
Keyword(s):  
Heat Stress ◽  
Simulation Model ◽  
Residential Buildings ◽  
Urban Climate ◽  
Weather Data ◽  
Physical Parameters ◽  
Warning Systems ◽  
Data Set ◽  
Health Warning ◽  
Building Model

A strong heat load in buildings and cities during the summer is not a new phenomenon. However, prolonged heat waves and increasing urbanization are intensifying the heat island effect in our cities; hence, the heat exposure in residential buildings. The thermophysiological load in the interior and exterior environments can be reduced in the medium and long term, through urban planning and building physics measures. In the short term, an increasingly vulnerable population must be effectively informed of an impending heat wave. Building simulation models can be favorably used to evaluate indoor heat stress. This study presents a generic simulation model, developed from monitoring data in urban multi-unit residential buildings during a summer period and using statistical methods. The model determines both the average room temperature and its deviations and, thus, consists of three sub-models: cool, average, and warm building types. The simulation model is based on the same mathematical algorithm, whereas each building type is described by a specific data set, concerning its building physical parameters and user behavior, respectively. The generic building model may be used in urban climate analyses with many individual buildings distributed across the city or in heat–health warning systems, with different building and user types distributed across a region. An urban climate analysis (with weather data from a database) may evaluate local differences in urban and indoor climate, whereas heat–health warning systems (driven by a weather forecast) obtain additional information on indoor heat stress and its expected deviations.

scholarly journals Performances of Limited Area Models for the WORKLIMATE Heat–Health Warning System to Protect Worker’s Health and Productivity in Italy

2021 ◽  
Vol 18 (18) ◽  
pp. 9940
Author(s):  
Daniele Grifoni ◽  
Alessandro Messeri ◽  
Alfonso Crisci ◽  
Michela Bonafede ◽  
Francesco Pasi ◽  
...  
Keyword(s):  
Heat Stress ◽  
Warning System ◽  
Outdoor Environment ◽  
Limited Area ◽  
Warning Systems ◽  
Stress Indicators ◽  
Climate Conditions ◽  
Health Warning ◽  
Globe Temperature ◽  
Meteorological Models

Outdoor workers are particularly exposed to climate conditions, and in particular, the increase of environmental temperature directly affects their health and productivity. For these reasons, in recent years, heat-health warning systems have been developed for workers generally using heat stress indicators obtained by the combination of meteorological parameters to describe the thermal stress induced by the outdoor environment on the human body. There are several studies on the verification of the parameters predicted by meteorological models, but very few relating to the validation of heat stress indicators. This study aims to verify the performance of two limited area models, with different spatial resolution, potentially applicable in the occupational heat health warning system developed within the WORKLIMATE project for the Italian territory. A comparison between the Wet Bulb Globe Temperature predicted by the models and that obtained by data from 28 weather stations was carried out over about three summer seasons in different daily time slots, using the most common skill of performance. The two meteorological models were overall comparable for much of the Italian explored territory, while major limits have emerged in areas with complex topography. This study demonstrated the applicability of limited area models in occupational heat health warning systems.

scholarly journals PARAMETERIZATION OF CROP SIMULATION MODEL “CERES‐MAIZE” IN NITRA‐DOLNÁ MALANTA

2007 ◽  
Vol 15 (1) ◽  
pp. 25-30 ◽  
Author(s):  
Pavel Samuhel ◽  
Bernard Šiška
Keyword(s):  
Climate Change ◽  
Simulation Model ◽  
Economic Effect ◽  
Daily Temperature ◽  
Weather Data ◽  
Data Set ◽  
Grain Yields ◽  
Crop Simulation Model ◽  
Crop Simulation ◽  
Maize Grain

Nowadays more than ever production of food depends on reasonable usage of sources. Processes like climate change, climate variability, carbon retention, long‐time food safety are becoming more and more important. Determining of reasonable crop strategy can have a significant social and economic effect. Computer‐simulative models of systems soil/plant/atmosphere can help in processes like crop growth or development. Crop simulation model CERES‐Maize program part of DSSAT v.4 was used to simulate potential maize grain yield. Field trials of Slovak Agricultural University in Nitra ‐ Dolna Malanta were used for parameterization of the model. Model inputs included TMIN‐minimal daily temperature, TMAX‐maximal daily temperature, SRAD‐sun radiation and RAIN‐daily sum of precipitation called as ‘minimum data set’ were built into weatherman program shell. These weather data are basic for the model running. Other important input data included the soil data and agrotechnological data. Outputs of the model show that measured and simulated maize grain yields have a very close relationship. Mean relative difference from all these years reached 7,76 %. Simulated grain yields are a little bit higher in all years as compared with field trial yields. This fact can be explained by the influence of a harmful disease and insects. Successful parameterization is a good base for climate change impact studies.

scholarly journals The Building Data Genome Project 2, energy meter data from the ASHRAE Great Energy Predictor III competition

2020 ◽  
Vol 7 (1) ◽  
Author(s):  
Clayton Miller ◽  
Anjukan Kathirgamanathan ◽  
Bianca Picchetti ◽  
Pandarasamy Arjunan ◽  
June Young Park ◽  
...  
Keyword(s):  
Residential Buildings ◽  
Open Data ◽  
Cooling Water ◽  
Genome Project ◽  
Electrical Heating ◽  
Weather Data ◽  
Data Set ◽  
Water Steam ◽  
Heating And Cooling ◽  
Great Energy

Abstract This paper describes an open data set of 3,053 energy meters from 1,636 non-residential buildings with a range of two full years (2016 and 2017) at an hourly frequency (17,544 measurements per meter resulting in approximately 53.6 million measurements). These meters were collected from 19 sites across North America and Europe, with one or more meters per building measuring whole building electrical, heating and cooling water, steam, and solar energy as well as water and irrigation meters. Part of these data was used in the Great Energy Predictor III (GEPIII) competition hosted by the American Society of Heating, Refrigeration, and Air-Conditioning Engineers (ASHRAE) in October-December 2019. GEPIII was a machine learning competition for long-term prediction with an application to measurement and verification. This paper describes the process of data collection, cleaning, and convergence of time-series meter data, the meta-data about the buildings, and complementary weather data. This data set can be used for further prediction benchmarking and prototyping as well as anomaly detection, energy analysis, and building type classification.

Use of CERES-Wheat model for wheat yield forecast in central Indo-Gangetic Plains of India

2004 ◽  
Vol 142 (1) ◽  
pp. 59-70 ◽  
Author(s):  
A. S. NAIN ◽  
V. K. DADHWAL ◽  
T. P. SINGH
Keyword(s):  
Simulation Model ◽  
Wheat Yield ◽  
Uttar Pradesh ◽  
Weather Data ◽  
Large Area ◽  
Data Set ◽  
Yield Forecast ◽  
Final Yield ◽  
Eastern Uttar Pradesh ◽  
Technology Trend

A methodology was developed for large area yield forecast using a crop simulation model and a discrete technology trend, and was applied to the coherent wheat yield variability zones of Eastern Uttar Pradesh, India. The approach consisted of three major steps: (a) prediction of technology trend yield using historical yield series of the region; (b) prediction of weather-induced deviation in wheat yield using CERES-Wheat simulation model and relating weather-induced deviation in simulated yield to deviation in observed yield deviations from technology trend; and (c) final yield forecast by incorporating predicted yield deviation in trend predicted yield. The regression coefficients for step (b) were generated using 10 years' data (1984/85–1994/95) and the reliability of the approach was tested on a data set of 5 years' independent data (1995/96–1999/2000). The results showed that this approach could capture year-to-year variability in large area wheat yield with reasonable accuracy. The Root Mean Square Error (RMSE) between observed and predicted yield was reported as 0·098 t/ha for the mean yield of 2·072 t/ha (4·72%). However, the RMSE was slightly higher in the forecasting period in comparison to the calibration period. The use of this methodology for issuing the pre-harvest forecast and the effect of upgrading the technology trend were also studied. The pre-harvest forecasts were made using in-season weather data up to the end of February and climatic-normal for the rest of the wheat-growing season, which showed good agreement with observed wheat yields. The forecasts of wheat yield for the season 1999/2000 were made using the technology trend up to 1994/95 and the updated technology trend up to 1998/99, which showed that the RMSE fell in the latter case, from 4·10 to 2·50%.

scholarly journals Parametric Design Structures in Low Rise Buildings in Relation to the Urban Context in UAE

2021 ◽  
Vol 13 (15) ◽  
pp. 8595
Author(s):  
Lindita Bande ◽  
Abeer Alshamsi ◽  
Anoud Alhefeiti ◽  
Sarah Alderei ◽  
Sebah Shaban ◽  
...  
Keyword(s):  
Heat Stress ◽  
Parametric Design ◽  
Residential Buildings ◽  
Cost Evaluation ◽  
Outdoor Thermal Comfort ◽  
Climate Index ◽  
Cooling Load ◽  
Design Structure ◽  
Al Ain ◽  
The City

The city of Al Ain (Abu Dhabi, UAE) has a mainly low rise residential buildings. Villas as part of a compound or separate units represent the majority of the residential areas in the city. Due to the harsh hot arid climate of Al Ain, the energy demand for the cooling load is quite high. Therefore, it is relevant finding new retrofit strategies that are efficient in reducing the cooling load of the villas. The aim of this study is to analyze one particular strategy (parametric shading structure) in terms of design, construction, cost, energy impact on the selected villa. The main data for this study is taken from the local sources. There are six steps followed in this analysis: case study analysis; climate analysis; parametric structure and PV panels; building energy consumption and outdoor thermal comfort; modelling, simulation, and validation; materials, construction, and cost evaluation. The model of the villa was validated for the full year 2020 based on the electricity bills obtained. After adding the parametric design structure, the reduction after shading is approximately 10%. Meanwhile the UTCI (Universal Thermal Climate Index) dropped from extreme heat stress to strong heat stress (average for the month of March and September). These findings are promising in the retrofit industry due to the advanced calculations used to optimize the parametric design structure.

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.

scholarly journals Protective Netting Improves Leaf-level Photosynthetic Light Use Efficiency in ‘Honeycrisp’ Apple Under Heat Stress

HortScience ◽  
2018 ◽  
Vol 53 (10) ◽  
pp. 1416-1422 ◽  
Author(s):  
Giverson Mupambi ◽  
Stefano Musacchi ◽  
Sara Serra ◽  
Lee A. Kalcsits ◽  
Desmond R. Layne ◽  
...  
Keyword(s):  
Heat Stress ◽  
Solar Radiation ◽  
Leaf Gas Exchange ◽  
Growing Season ◽  
High Light ◽  
Weather Data ◽  
Light Use Efficiency ◽  
Ambient Temperatures ◽  
Leaf Level ◽  
Use Efficiency

Globally, apple production often occurs in semiarid climates characterized by high summer temperatures and solar radiation. Heat stress events occur regularly during the growing season in these regions. For example, in the semiarid eastern half of Washington State, historic weather data show that, on average, 33% of the days during the growing season exceed 30 °C. To mediate some of the effects of heat stress, protective netting (PN) can be used to reduce the occurrence of fruit sunburn. However, the impacts of reduced solar radiation in a high light environment on light-use efficiency and photosynthesis are poorly understood. We sought to understand the ecophysiological response of apple (Malus domestica Borkh. cv. Honeycrisp) under blue photoselective PN during days with low (26.6 °C), moderate (33.7 °C), or high (38.1 °C) ambient temperatures. Two treatments were evaluated; an uncovered control and blue photoselective PN. Maximum photochemical efficiency of PSII, or photosystem II (Fv/Fm) was significantly greater at all measurement times under blue photoselective PN compared with the control on days with high ambient temperatures. Fv/Fm dropped below 0.79, which is considered the threshold for stress, at 1000 hr in the control and at 1200 hr under blue photoselective PN on a day with high ambient temperature. On days with low or moderate ambient temperatures, Fv/Fm was significantly greater under blue photoselective PN at 1400 hr, which coincided with the peak in solar radiation. ‘Honeycrisp’ apple exhibited dynamic photoinhibition as shown by the diurnal decline in Fv/Fm. Quantum photosynthetic yield of PSII (ΦPSII) was also generally greater under blue photoselective PN compared with the control for days with moderate or high ambient temperatures. Photochemical reflectance index (ΔPRI), the difference in reflectance between a stress-responsive and nonstress-responsive wavelength, was greater under PN compared with the control on the day with high ambient temperatures, with no differences observed under low or moderate ambient temperatures. Leaf gas exchange did not show noticeable improvement under blue photoselective netting when compared with the control despite the improvement in leaf-level photosynthetic light use efficiency. In conclusion, PN reduced incoming solar radiation, improved leaf-level photosynthetic light use efficiency, and reduced the symptoms of photoinhibition in a high-light, arid environment.

scholarly journals Safety in residential buildings: Evacuation from residential buildings without fire escape stairs

2021 ◽  
Vol 69 (1) ◽  
pp. 148-178
Author(s):  
Radoje Jevtić
Keyword(s):  
Simulation Model ◽  
Residential Buildings ◽  
Residential Building ◽  
Simulation Software ◽  
Modeling Method ◽  
Real Object ◽  
Object A ◽  
The Third

Introduction/purpose: Safety in high residential buildings presents a very important and always actual task. In case of some unforeseen and dangerous occurrences, their residents must be evacuated. Fire, earthquakes, and terrorism are only some of such situations. The speed of evacuation from high residential buildings depends on many different factors. A particularly difficult and complex evacuation task concerns buildings without fire escape stairs. Methods: The modeling method was used in this paper. Based on a real object - a residential building, an appropriate simulation model was realized in appropriate simulation software. Results: The results of this paper have shown that, out of four scenarios, the fastest evacuation was for the evacuation speed of 1.75 m/s. The first two scenarios did not report any jams, unlike the third and fourth scenario; in the third scenario, the occupants' speeds were 0.75 m/s and 1.25 m/s while in the fourth scenario, the simulated occupants' speeds were from 0.75 m/s to 1.75 m/s. Conclusion:The usage of appropriate simulation software enables fast, precise, safe and cheap calculation of evacuation times and it can significantly improve evacuation procedures and evacuation strategies.

scholarly journals APPLYING FUZZY QFD MCDM TO EVALUATE MUSICAL INSTRUMENTS

2020 ◽  
Author(s):  
Peter Poon Chong ◽  
Terrence Lalla
Keyword(s):  
Quality Function Deployment ◽  
Model Performance ◽  
Musical Instruments ◽  
Physical Parameters ◽  
Analytic Hierarchy ◽  
Data Set ◽  
Linguistic Data ◽  
Order Preference ◽  
Physical Features ◽  
Stakeholder Needs

This paper exhibits a method to improve the quality of musical instruments with the application of two Multi-Criteria Decision Making models, Technique of Order Preference by Similarity to Ideal Solution (TOPSIS) and Analytic Hierarchy Process (AHP) in a Quality Function Deployment (QFD) Environment. A fuzzy analysis approach was also included to accommodate qualitative data in music. The QFD was constructed with literature based on optimizing the manufacture of musical instruments. At this phase of the research, the paper focused on the physical parameters and perceived qualities of musical instruments. The proposed modified QFD was developed to identify the product features chosen by the market and aid the manufacture of musical instruments. A standard QFD recognized and scored factors to develop and manufacture musical instruments. It accommodated some core engineering variables for the musical instruments but overlooked some feature stakeholder needs. For example, the musician may not have 100% gratification while playing the instrument as the manufacturer fails to capture acoustic features to psychologically satisfy the musician’s audience. Using fuzzy logic, QFD and MCDM increased the model performance by expanding the data set. It offered the manufacturer of musical instruments a mode to capture and analyse behavioural linguistic data covering more customer requirements. Hence, the approach increased the range to correlate the physical features and psychological behaviours of musical instruments. It allowed non-technical persons to provide an improved form of reliable information. This modified QFD can also be applied to develop other products involving linguistic data.

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