با سلام خدمت کاربران در صورتی که با خطای سیستم پرداخت بانکی مواجه شدید از طریق کارت به کارت (6037997535328901 بانک ملی ناصر خنجری ) مقاله خود را دریافت کنید (تا مشکل رفع گردد).
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1 |
Projected changes to moisture loads for design and management of building exteriors over Canada
تغییرات پیش بینی شده برای بارهای رطوبت برای طراحی و مدیریت ساختمانهای خارجی در کانادا-2020 Atmospheric moisture loading affects the performance and durability of building exteriors. In a changing climate,
designing for historical moisture loads may no longer be adequate. This study investigates potential climate
change impacts on the moisture index used in the design and management of buildings over Canada. Projections
are obtained for future periods experiencing different global warming levels (þ1 �C, þ2 �C, etc.) with respect to
1986–2016 baseline using outputs from a 15-member initial condition ensemble of the CanRCM4, a Canadian
regional climate model at 50-km resolution. CanRCM4 overestimates wetting potential, underestimates drying
capacity, and hence overestimates moisture loads for the baseline period. To obtain unbiased moisture indices, a
simple linear bias adjustment scheme is applied to the baseline simulation and future projections. Significant
increases in future moisture loads are found over the western and eastern coastal regions of Canada due to increases
in rainfall amounts, driven by increases in precipitation and a warming-induced shift from snow to rain.
Conversely, CanRCM4 projects significant decreases in future moisture loads over south-central and northern
Canada due to increases in drying capacity, driven by increases in future near surface air temperature that are
accompanied by nearly unchanged relative humidity. Internal variability of the moisture load due to the natural,
chaotic variability of the climate system, is modest compared to the magnitude of the projected change signal —
signal-to-noise ratio is high. The projected changes suggest that moisture protection could be a concern for
designing and managing building exteriors over western and eastern coastal regions of Canada. Keywords: Climate change | Design building | Moisture index | Regional climate models |
مقاله انگلیسی |
2 |
Identification of the key landscape metrics indicating regional temperature at different spatial scales and vegetation transpiration
شناسایی معیارهای اصلی چشم انداز که نشانگر دمای منطقه در مقیاسهای مختلف مکانی و تعرق پوشش گیاهی است-2020 Land use changes are widely known as one of the drivers of land surface temperature variation. However, the
influence of composition of various land use types and their configuration, i.e. landscape pattern, on regional
temperature is still unknown. We test the hypotheses: (1) surface air temperature would vary according to
landscape pattern, and (2) these effects would vary, demonstrating scale- and site-dependence effects through
different vegetation transpiration rates. The relationships of 360 landscape metrics, indicating multiple dimensions
of landscape pattern, and regional temperature at five climatic zones at five spatial scales in southern
hilly China were examined through Pearson correlation analysis, stepwise regression and redundancy analysis.
The area and number of grassland, building, and dry cropland patches are positively correlated with temperature;
the area and density of forestland, shrubland, wet cropland, and water patches have cooling effects. The
optimal scale and landscape metrics are also different between different climate zones. Landscape-level vegetation
transpiration, determined by the dominant species, may partially explain why landscape pattern affects
regional temperature. These findings provide new insights for understanding land use–temperature interactions
and designing climate adaptation and mitigation strategies. Suitable landscape pattern, optimal scale, and
dominant species should be considered in landscape planning and land use management to mitigate the impacts
of the projected climatic warming. Keywords: Landscape pattern | Scale effect | Regional temperature | Vegetation transpiration |
مقاله انگلیسی |
3 |
Analyzing the Influencing Factors of Urban Thermal Field Intensity Using Big-Data-Based GIS
تجزیه و تحلیل عوامل مؤثر از شدت میدان حرارتی شهری با استفاده از GIS مبتنی بر داده های بزرگ-2020 The effects of human activities and land cover changes on urban thermal
field patterns are closely related to the land surface temperature (LST) and air
temperature. At present, the number of studies on the quantitative relationship
between these two indexes and the effect of the observational scale on their
influence is insufficient. In this study, spatial analysis methods such as geographic
modeling were combined with remote sensing images, meteorological data, and
points of insert and used to investigate the composition and scale of the factors
influencing the temperature field in Beijing. The results showed that there are
differences in the positive and negative correlations between LST and air
temperature and various influencing factors. At a spatial resolution of 90 m, LST
had a strong linear relationship with the average air temperature. Indicators
reflecting elements of human activity, such as buildings, roads, and entertainment,
were easily measured by meteorological stations at a small scale, and the natural
green space ratio could also be easily captured by satellite thermal sensors at small
scales. These results have substantial implications for environmental impact
assessments in areas experiencing an increasing urban heat island effect due to
rapid urbanization. Keywords: land-surface temperature | thermal field pattern | POI data | GIS | air temperature |
مقاله انگلیسی |
4 |
A study on temperature-setting behavior for room air conditioners based on big data
مطالعه رفتار تنظیم دما برای سیستم های تهویه مطبوع اتاق بر اساس داده های بزرگ-2020 The set-point temperature of room air conditioners (RACs) is extremely critical for cooling
energy consumption of residential buildings. However, current research on temperature-setting
behavior is limited owing to the limitations of data acquisition. This study aims to identify the
typical temperature-setting patterns for RACs and explore the association of temperature-setting
behavior with other RAC operation characteristics. The data obtained from the big data cloud
platform of an RAC manufacturer were analyzed in this study. These data consist of measured
data from 966 bedroom RACs (BRACs) and 321 living room RACs (LRACs). First, the RAC
operation characteristics, involving five parameters, namely, set-point temperature, set wind speed,
indoor temperature, operation duration, and energy consumption, were extracted from the raw data
by transforming, aggregating, and merging the bottom-level measured data. Subsequently, cluster
analysis was performed to identify various and typical temperature-setting behavior patterns. Five
typical temperature-setting patterns for BRACs and six typical patterns for LRACs were obtained.
Afterwards, data mining methods of difference analysis and association analysis were employed to explore the differences and association, respectively, of different temperature-setting patterns with
other operation characteristics of RACs (e.g., set wind speed, indoor air temperature, operation
duration, and energy consumption). The results of this study can provide researchers with
references of temperature-setting strategies in residential building energy simulation and quantify
the energy impacts of diverse temperature-setting patterns in residential buildings. Keywords: Occupant behavior | Room air conditioner | Set-point temperature | Data mining | Cluster analysis |
مقاله انگلیسی |
5 |
Predicting the climate change impacts on water-carbon coupling cycles for a loess hilly-gully watershed
پیش بینی تأثیر تغییرات آب و هوا بر چرخه اتصال آب و کربن برای یک حوزه آبخیز کوهستانی -2020 Understanding the climate change impacts on water and carbon cycles is of great importance for comprehensive
watershed management. Although many studies have been conducted on the future climate change impacts on
either water cycle or carbon cycle, the potential impacts on water-carbon coupling cycles are still poorly understood.
This study used an integrated hydro-biochemical model (SWAT-DayCent) to quantitatively investigate
the climate change impacts on water-carbon coupling cycles with a case study of typical loess hilly-gully watershed-
the Jinghe River Basin (JRB) on the Loess Plateau. We used climate scenarios data derived under the
three Representative Concentration Pathways (RCPs2.6, 4.5 and 8.5) by five downscaled Global Circulation
Models (GCMs) and set two future periods of 2020–2049 (near future, NF) and 2070–2099 (far future, FF). It was
projected that the annual precipitation would generally decrease slightly during the NF period but increase by
4–11% during the FF period, while the maximum/minimum air temperatures would increase significantly. The
average annual streamflow would decrease (with up to 20.1% under RCP8.5) and evapotranspiration (ET) would
remain almost unchanged during the NF period; however, both of them would increase during the FF period. The
net primary production (NPP) would be generally higher due to the CO2 fertilization, whereas the soil organic
carbon would decrease across all scenarios due to the warmer climate. The NPP-ET was projected to be closely
coupled across all scenarios, and this coupling was mainly controlled by the inter-annual variability (IAV) of
precipitation. Moreover, the precipitation IAV combined with NPP-ET coupling could also jointly control the
NPP variability in the JRB. These projections in water-carbon coupling cycles can be useful to make betterinformed
decisions for future water resources and ecosystem management of the loess hilly-gully regions. Keywords: Climate change | SOC | Streamflow | SWAT-DayCent | Water-carbon coupling |
مقاله انگلیسی |
6 |
Evaluation of temperature-based machine learning and empirical models for predicting daily global solar radiation
ارزیابی یادگیری ماشین مبتنی بر دما و مدلهای تجربی برای پیش بینی تابش روزانه خورشیدی جهانی-2019 Accurate global solar radiation data are fundamental information for the allocation and design of solar energy
systems. The current study compared different machine learning and empirical models for global solar radiation
prediction only using air temperature as inputs. Four machine learning models, e.g., hybrid mind evolutionary
algorithm and artificial neural network model, original artificial neural network, random forests and wavelet
neural network, as well as four empirical temperature-based models (Hargreaves-Samani model, Bristow-
Campbell model, Jahani model, and Fan model) were applied for prediction of daily global solar radiation in
temperate continental regions of China. The results indicated the hybrid mind evolutionary algorithm and artificial
neural network model provided better estimations, compared with the existing machine learning and
empirical models. Thus, the temperature-based hybrid model is highly recommended to predict global solar
radiation in temperate continental regions of China when only air temperature data are available. Combining the
hybrid model with future air temperature forecasts, we can get the accurate information of future solar radiation,
which is of great importance to management and operation of solar energy systems. Keywords: Global solar radiation | Forecast | Empirical models | Machine learning models | Temperate continental regions |
مقاله انگلیسی |
7 |
Deep-learning-based fault detection and diagnosis of air-handling units
تشخیص خطای مبتنی بر یادگیری عمیق و تشخیص واحدهای انتقال هوا-2019 This study proposed a real-time fault diagnostic model for air-handling units (AHUs); the model used deep
learning to improve the operational efficiency of AHUs and thereby reduce the energy consumption of
HVAC—heating, ventilating, and air conditioning—systems in buildings. Additionally, EnergyPlus simulation
software was employed to establish different types of fault operation behavior data to serve as references for
deep learning, thus reducing the complexity of data preprocessing, retaining data completeness, and improving
the reliability of the diagnostic model.
The proposed deep neural network fault diagnostic model can serve as a reference for this research field; the
model features five hidden layers, each comprising 200 neurons. Additionally, this study tested abnormal faults
commonly observed in AHUs, including failure to control two-way hydronic valves and variable air volume box
dampers as well as supply air temperature sensors exhibiting measurement error. After performing diagnosis
with data that had not been used in the training or verification process, the diagnostic results indicated that the
diagnostic model exhibited 95.16% accuracy. Keywords: Deep learning | Deep neural network | Fault detection and diagnosis |
مقاله انگلیسی |
8 |
بررسی اثرات استفاده از ابزار های ذخیره انرژی گرمایی(TESD) در گرم کننده هوای خورشیدی
سال انتشار: 2018 - تعداد صفحات فایل pdf انگلیسی: 11 - تعداد صفحات فایل doc فارسی: 24 در این کار تحقیقی مقدماتی چیده شده و طراحی شده است تا اسید لوریک را به عنوان یک ماده در حال تغییر فاز (PCM) در یک گرم کننده هوای خورشیدی جا دهد.این ماده در حال تغییر فاز(PCM) به دقت بر اساس کاربرد و در محدودیت های ساخت انتخاب شده است.یک ابزار ذخیره سازی انرژی خورشیدی(TESD) ساخته شده و در گرم کننده هوای خورشیدی قرار گرفته و آزمایشات این موضوع را در خود داشتند که در این آزمایش گرم کننده هوای خورشیدی همراه و بدون وسیله ذخیره انرژی گرمایی(TESD) برای ارزیابی پارامتر هایی نظیر دمای خروجی و فشار مقایسه شدند. نتایج نشان دادند که تغییر در دمای هوای بیرون کاهشی از 8.67 کلوین تا 4 کلوین را داشته است و با افزایش نرخ یا میزان جریان ماده از هوای در حال شارش از طریق گرم کننده هوای خورشیدی از 0.021 کیلوگرم بر ثانیه به 0.035 کیلوگرم بر ثانیه بوده است. همچنین با افزایشی در میزان جرم سیال کاهشی در عامل اصطحکاک از 0.0119 تا 0.00802 مشاهده شده است. در این جا یک رشد متوسط 86.47 درصدی در افزایش دما هوای خروجی با TESD وجود دارد همچنین افزایش دمای خروجی بدون TESD یک رشد متوسط 36.47 درصدی در حضور عامل اصطحکاک بررسی و مقایسه شده است. بررسی های ترکیبی نیز شکل گرفته که زیرکی و ظرافت در کار کردن گرم کننده هوای خورشیدی با ابزار ذخیره کننده انرژی گرمایی (TESD) را نشان داده و ارائه می کند. ترکیب گرم کننده هوای خورشیدی با TESD در کد FLUENT (کد نویسی سلیس و روان ) CFD با استفاده از مدل های آشوب متنوع همانند k-ω SST , k-ωاستاندارد , k-ϵ استاندارد و k-ϵ RNG بررسی شده است . این موضوع مشاهده شده است که نتایج به دست آمده از مدل آشوب k-ϵ RNG در توافق خوبی با نتایج آزمایشگاهی بوده و بنابراین برای بررسی تمام موارد مورد توجه واقع شده در این کار استفاده شده است .
کلمات کلیدی: مواد در حال تغییر فاز | انتقال دهنده گرما | ابزار ذخیره گرما | گرم کننده هوای خورشیدی | عامل اصطحکاک |
مقاله ترجمه شده |
9 |
Predictive modeling and optimization of a multi-zone HVAC system with data mining and firefly algorithms
مدل سازی پیش بینی و بهینه سازی یک سیستم HVAC چند منطقه ای با اگوریتم های کرم ب تاب و داده کاوی-2015 This research applies a data-driven approach to investigate energy savings of a multi-zone HVAC (heating, ventilating, and air conditioning) system. The predictive models of the HVAC energy con- sumption and the environment conditions of multiple zones are constructed by data mining algorithms. Two major environment conditions, the room temperature and the relative room humidity, are considered. Two variables of operating the HVAC system, the supply air temperature set point and the supply air static pressure set point, in the predictive models are optimized with respect to minimizing the HVAC energy while maintaining the predefined environment conditions of each zone. A novel heuristic search algorithm, the firefly algorithm, is utilized to solve the data-driven predictive models and derive the optimal settings of two set points under required HVAC operational constraints. The firefly algorithm is compared with the particle swarm optimization and evolutionary strategy to demonstrate its advantages in solving the proposed optimization problem. HVAC energy saving with the proposed data-driven framework is examined in the computational studies. A sensitivity analysis of the potential of energy saving based on different types of environment condition constraints is conducted.© 2015 Elsevier Ltd. All rights reserved.
Keywords: Energy conservation | Data-driven modeling | Multi-zone HVAC | Firefly algorithm | Predictive operation |
مقاله انگلیسی |
10 |
مدل سازی پیش بینی و بهینه سازی سیستم HVAC چند منطقه¬ای با داده کاوی و الگوریتم کرم شب تاب
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 32 این تحقیق، رویکردی داده محور برای بررسی صرفه جویی انرژی در سیستم چند منطقه¬ای تهویه مطبوع (HVAC) (گرمایش، تهویه و تهویه مطبوع) است. مدل¬های پیش بینی مصرف انرژی HVAC و شرایط محیطی مناطق متعدد توسط الگوریتم های داده کاوی ایجاد شد. دمای و رطوبت نسبی اتاق، شرایط محیطی غالب محسوب می شوند. نقطه تنظیم منبع دمای هوا و منبع فشار استاتیک هوا، دو متغیر عملیاتی سیستم های HVAC هستند و در مدل های پیش بینی برای کاهش انرژی HVAC با حفظ شرایط محیطی از پیش تعریف شده در هر منطقه، بهینه شدند. الگوریتم کرم شب تاب، الگوریتم جستجوی اکتشافی جدیدی است که برای حل مدل های پیش بینی داده محور و دستیابی به تنظیمات بهینه در مجموعه¬ی دو نقطه در صورت اعمال محدودیت های عملیاتی HVAC، مورد نیاز است. در صورت مقایسه بین الگوریتم کرم شب تاب با بهینه سازی ازدحام ذرات و استراتژی تکاملی، مزایای الگوریتم کرم شب تاب در حل مشکل بهینه سازی پیشنهادی اثبات می¬شود. در مطالعات محاسباتی، صرفه جویی در انرژی HVAC در چارچوب مبتنی بر داده پیشنهادی مورد بررسی قرار گرفت. آنالیز حساسیت پتانسیل صرفه جویی انرژی بر مبنای انواع مختلف محدودیت شرایط محیطی انجام شد.
کلمات کلیدی: حفاظت از انرژی | مدل سازی داده محور | HVAC چند منطقه ای | الگوریتم کرم شب تاب | عملیات پیش بینی شده |
مقاله ترجمه شده |