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 |
Dynamic occupant density models of commercial buildings for urban energy simulation
مدلهای چگالی اشغال پویا ساختمانهای تجاری برای شبیه سازی انرژی شهری-2020 The number of occupants and its changing pattern over time are key information for building and urban energy
simulation. However, the commonly used assumption and simplification of a fixed occupancy schedule does not
reflect the complicated reality, leading to significant errors in energy simulation. Therefore, dynamic occupant
density models which describe the real-world situation more accurately should be developed. This paper presents
a methodology to develop such a model for commercial buildings and expand it from the building level to urban
level. First, a total of 2275 commercial buildings in Nanjing, a major city in China, are identified and classified
into three sub-categories using Points of Interest and logistic regression. Then field measurement is conducted to
obtain the hourly occupant density for 12 sample commercial buildings. The building-level dynamic occupant
density model is developed by fitting normal distribution functions into the measured data. Finally, transportation
accessibility and population level, two urban parameters, are defined and used to expand the buildinglevel
occupant density model to the urban-level one. The dynamic urban-level occupant density model is verified
for all three sub-categories of commercial buildings and the overall results are acceptable. Keywords: Big data | Commercial buildings | Urban-level | Dynamic occupant density models |
مقاله انگلیسی |
2 |
Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing
اتصال مدل نورپردازی شبکه عصبی مصنوعی و شبیه سازی انرژی ساختمان برای لعاب خلاء فتوولتائیک-2020 Window plays an essential role in the indoor environment and building energy consumption. As an innovative
building integrated photovoltaic (BIPV) window, the vacuum PV glazing was proposed to provide excellent
thermal performance and utilize renewable energy. However, the daylighting performance of the vacuum PV
glazing and the effect on energy consumption have not been thoroughly investigated. Most whole building
energy simulation used the daylighting calculation based on Daylight Factor (DF) method, which fails to address
realistic calculation for direct sunlight through complex glazing materials. In this study, a RADIANCE model was
developed and validated to adequately represent the daylight behaviour of a vacuum cadmium telluride photovoltaic
glazing with a three-layer structure. However, RADIANCE will consume too many computational resources
for a whole year simulation. Therefore, an artificial neuron network (ANN) model was trained based on
the weather conditions and the RADIANCE simulation results to predict the interior illuminance. Subsequently, a
preprocessing coupling method is proposed to determine the lighting consumption of a typical office with the
vacuum PV glazing. The performance evaluation of the ANN model indicates that it can predict the illuminance
level with higher accuracy than the daylighting calculation methods in EnergyPlus. Therefore, the ANN model
can adequately address the complex daylighting response of the vacuum PV glazing. The proposed coupling
method showed a more reliable outcome than the simulations sole with EnergyPlus. Furthermore, the computational
cost can be reduced dramatically by the ANN daylighting prediction model in comparison with the
RADIANCE model. Compared with the lighting consumption determined by the ANN-based coupling method,
the two approaches in EnergyPlus, the split-flux method and the DElight method, tend to underestimate the
lighting consumption by 5.3% and 9.7%, respectively. Keywords: Building integrated photovoltaic (BIPV) | Vacuum glazing | Semi-transparent photovoltaic | Daylighting model | Building energy model | Artificial neuron networks (ANNs) |
مقاله انگلیسی |
3 |
Methodology to assess business models of dynamic pricing tariffs in all-electric houses
روش ارزیابی مدل های تجاری تعرفه های قیمت گذاری پویا در خانه های تمام برقی-2020 There is a need for methodologies that integrate energy simulation and cost calculation to assess grid
rent business models as incentive for demand-side management (DSM) in buildings. Despite the proliferation of energy simulation and cost calculation tools, there are no tool (e.g., software program) with
appropriate methodology that caters specifically for the assessment of business models based on aggregation of dynamic pricing tariffs. Furthermore, the majority of existing methodologies focus on evaluating
the supply-side management (SSM) of energy grids, and largely overlook the issue of influencing the customer to make good choices when it comes to DSM and/or design/renovation actions. This paper introduces energy and cost oriented methodology that provides informative support for utility companies and
electric-grid customers including households’ occupants to assess the economic incentives of different
energy and power dynamic pricing tariffs. A physical model-based building simulation tool (IDA-ICE) is
used to assess the energy performance of a representative residential benchmark including 96 all-electric
houses in Norway with and without renewable energy technology. A business model-based cost calculator
is developed and linked with the energy simulation’s outputs to assess the effectiveness of three dynamic
pricing tariffs, suggested recently by the Norwegian Water Resources and Energy Directorate (NVE). The
effectiveness of the three pricing tariffs is compared (improving building’s energy efficiency vs enhancing
grid’s demand side load shifting). Overall, results indicate that the Tiered Rate tariff is the most effective
business strategy for customers to reduce the electric-based heating load during high demand periods.
However, the methodology generated a comprehensive suite of scenarios analysis that allow customers,
utility companies and policy makers to accurately address several building renovation variations and demand side management strategies to make the right decision upfront. Keywords: Demand-side management | Grid rent | End users | Cost effectiveness | Load shifting | Energy flexibility |
مقاله انگلیسی |
4 |
Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings
مدل انعطاف پذیر فراشناختی با الهام از طبیعت برای پیش بینی مصرف انرژی در ساختمانهای مسکونی-2020 As the global economy expands, both residential and commercial buildings consume an increasing
proportion of the total energy that is used by buildings. Energy simulation and forecasting are important
in setting energy policy and making decisions in pursuit of sustainable development. This work develops
a new ensemble model, called the Evolutionary Neural Machine Inference Model (ENMIM), for estimating
energy consumption in residential buildings based on actual data. The ensemble model combines
two single supervised learning machines - least squares support vector regression (LSSVR), and the radial
basis function neural network (RBFNN) eand incorporates symbiotic organism search (SOS) to find
automatically its optimal tuning parameters. A set of real data, which were obtained from residential
buildings in Ho Chi Minh City, Viet Nam, as well as experimental data from the literature were used to
evaluate the performance of the developed model. Comparison results reveal that the ENMIM surpasses
other benchmark models with respect to predictive accuracy. This work proves that the developed
ensemble model is a promising alternative for the planning of energy management. Furthermore, the fact
that the ENMIM has greater predictive accuracy than other artificial intelligence techniques suggests that
the developed self-tuning ensemble model can be used in various disciplines. Keywords: Energy consumption | Residential buildings | Ensemble model | Artificial intelligence | Machine learning | Evolutionary optimization |
مقاله انگلیسی |
5 |
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 |
6 |
Development of occupancy-integrated archetypes: Use of data mining clustering techniques to embed occupant behaviour profiles in archetypes
توسعه الگوهای های یکپارچه اشتغال: استفاده از تکنیک های خوشه بندی داده کاوی برای جابجایی پروفایل های رفتاری سرنشینان در آرکتیپ ها-2019 Building stock modelling usually deploys representative building archetypes to obtain reliable results of annual energy heating demand and to minimise the associated computational cost. Available method- ologies define archetypes considering only the physical characteristics of buildings. Uniform occupancy schedules, which correspond to national averages, are generally used in archetype energy simulations, de- spite evidence of occupancy schedules which can vary considerably for each building. This paper presents a new methodology to define occupancy-integrated archetypes. The novel feature of these archetype models is the integration of different occupancy schedules within the archetype itself. This allows build- ing stock energy simulations of national population subgroups characterised by specific occupancy pro- files to be undertaken. The importance of including occupant-related data in residential archetypes, which is different than the national average, is demonstrated by applying the methodology to the UK national building stock. The resultant occupancy-integrated archetypes are then modelled to obtain the annual final heating energy demand. It is shown that the relative difference between the heating demand of occupancy-integrated archetypes and uniform occupancy archetypes can be up to 30%. Keywords: Residential buildings | Archetypes | Stock modelling | k-mode clustering | Occupancy profiles |
مقاله انگلیسی |
7 |
Modeling a learning organization using a molecular network framework
مدل سازی یک سازمان آموزشی با استفاده از یک چارچوب شبکه مولکولی-2018 In this paper we present a new approach for modeling a learning organization using molecular
network framework. For the purpose of this study, we have developed a new FUTURE-O-DYN
model for simulation of learning organization by combining the FUTURE-O® model, a compre
hensive model that through the seven elements leads to a fully-fledged learning organization,
with molecular dynamics simulation technique. Molecular dynamics simulation, in which the
classical equations of motion for all particles of a system are integrated over finite period of time,
provides an important insight into the structure and function of molecules. The resulting tra
jectory is used to compute the time-dependent properties of the system. Here, we apply molecular
dynamics, in particular free energy simulation, to simulate a learning organization or any other
system including the use of computer technology in educational process. All steps of modeling
process; from data preparation to development of a suitable simulation space, potential energy
function and parameters to carry out simulations of a learning organization are discussed. Major
achievement of this study is that we apply molecular dynamics technique to model a learning
organization consisting of two individuals, which is done for the first time, with the newly de
veloped FUTURE-O-DYN model. For this purpose we also developed parameters that define
potential energy function for a pair of programmers case described in the literature. In our model,
the free energy is proportional to the values of the seven elements in the FUTURE-O® model. The
simulation results indicate that the calculated free energies using FUTURE-O-DYN model are in
excellent agreement with the experimentally measured values. The approach described here is
general and applicable to any education, business or corporate based learning organization.
Keywords: Adult learning ، Cooperative/collaborative learning ، Distributed learning environments ، Interactive learning environments ، Simulations |
مقاله انگلیسی |
8 |
Occupancy schedules learning process through a data mining framework
زملنبندی رخداد فرآیند یادگیری از طریق یک چارچوب داده کاوی-2015 Article history:Received 20 August 2014 Received in revised form 19 November 2014Accepted 22 November 2014Available online 17 December 2014Keywords: Occupant behavior Data miningOccupancy schedule Behavioral pattern Office building Building simulationBuilding occupancy is a paramount factor in building energy simulations. Specifically, lighting, plug loads, HVAC equipment utilization, fresh air requirements and internal heat gain or loss greatly depends on the level of occupancy within a building. Developing the appropriate methodologies to describe and repro- duce the intricate network responsible for human-building interactions are needed. Extrapolation of patterns from big data streams is a powerful analysis technique which will allow for a better understand- ing of energy usage in buildings. A three-step data mining framework is applied to discover occupancy patterns in office spaces. First, a data set of 16 offices with 10 min interval occupancy data, over a two year period is mined through a decision tree model which predicts the occupancy presence. Then a rule induction algorithm is used to learn a pruned set of rules on the results from the decision tree model. Finally, a cluster analysis is employed in order to obtain consistent patterns of occupancy schedules. The identified occupancy rules and schedules are representative as four archetypal working profiles that can be used as input to current building energy modeling programs, such as EnergyPlus or IDA-ICE, to investigate impact of occupant presence on design, operation and energy use in office buildings.© 2014 Elsevier B.V. All rights reserved.
Keywords: Occupant behavior | Data mining | Occupancy schedule | Behavioral pattern | Office building | Building simulation |
مقاله انگلیسی |
9 |
اثر پرداخت هاى سطحى بر روى مطالبه انرژى سيستم هاى HVAC براى ساختمان هاى موجود
سال انتشار: 2015 - تعداد صفحات فایل pdf انگلیسی: 10 - تعداد صفحات فایل doc فارسی: 28 در مراکز تاريخى،ساختمان ها توسط سقف هاى شيروانى که گاهى اوقات عايق نيستند با اينرسى پايين حرارتى مشخص شده اند و در نماهاى آجرى با فاکتور انعکاسى خورشيدى پايينى پوشش داده شده اند.بنابراين،اتاق هاى زيرشيروانى هزينه هاى انرژى زيادى را براى گرمايش و سرمايش ارائه کرده اند.
اين مقاله به تحليل صرفه جويى در انرژى حاصله توسط به کار بردن پرداخت هاى سطحى نوآورانه در سطوح داخلى و خارجى قطعات پوششى مات ساختمان هاى موجود مى پردازد.اين اقدام مقاوم سازى ساده و ارزان،کاهش هزينه انرژى را براى گرمايش و سرمايش به خود اختصاص داده،بلکه در مورد آسايش حرارتى محيط داخلى و عمر مفيد ساختمان ها نيز سودمند است زيرا مشکلات چگالش و تراکم و شوک حرارتى کاهش مى يابد.علاوه براين،آن ويژگى هاى معمارى و رنگى پوشش ساختمان را حفظ مى نمايد.
تجزيه و تحليل با استفاده از يک کد شبيه سازى انرژى ساختمان با در نظر گرفتن سيستمHVACمعمول براى شهرهاى ايتاليايى و اروپايى متفاوتى انجام شده است.بررسى هاى فنى - اقتصادى و محيط زيستى نيز انجام شده است.
نيازهاى انرژى حرارتى ساختمان ها براى سرمايش در تابستان مى تواند تا60% توسط استفاده از "رنگ هاى سرد "در سطح خارجى ديوارها و سقف کاهش يابد،در حالى که پوشش هاى نشر مادون قرمز پايين داخلى مى تواند نيازهاى حرارتى را در زمستان تا 12.5%کاهش دهد.
صرفه جويى در انرژى اوليه براى سرمايش و گرمايش و کاهش قابل توجه نشر گلخانه اى(تا 60% ) مى تواند به خوبى مقدار بازپرداخت چند سال در اکثر موارد،حاصل گردد.
کلمات کليدى:اتاق هاى زير شيروانى | ساختمان هاى موجود | پرداخت هاى سطحى | بهره ورى انرژى | هزينه هاى انرژى | سيستم هاى HVAC | اثرات زيست محيطى | عوامل نشر LCA | مقاوم سازى |
مقاله ترجمه شده |
10 |
بهینه سازی طراحی سیستم HVAC برای کاهش انرژی اولیه مورد نیاز
سال انتشار: 2014 - تعداد صفحات فایل pdf انگلیسی: 7 - تعداد صفحات فایل doc فارسی: 19 هدف از این مطالعه، ارائه¬ی روش طراحی بهینه برای سیستم HVAC در آپارتمان با استفاده از الگوریتم ژنتیک و بررسی قابلیت مصرف انرژی در سیستم طراحی شده¬ی HVAC است. انرژی مورد نیاز برای سرمایش و گرمایش خانه¬ی آپارتمان با استفاده از TRNSYS تعیین شد. با استفاده از الگوریتم ژنتیک اصلاح شده که الگوریتم ژنتیک چند جزیره نام دارد، الگوی اجرایی بهینه در سیستم های HVAC به منظور کاهش مصرف انرژی تعریف گردید. روش طراحی بهینه برای سیستم های HVAC در خانه¬ی آپارتمانی با استفاده از الگوریتم ژنتیک و داده های بار مورد نیاز خنک کننده/گرمایشی توسط TRNSYS شبیه سازی شد. ذخیره¬ی انرژی مورد نیاز سیستم های تجهیزات در خانه¬ی آپارتمانی توسط طرح بهره برداری از سیستم های HVAC مورد تایید است. نتایج نشان داد که این روش پیشنهادی قابلیت بالایی در طراحی سیستم بهینه به منظور صرفه جویی انرژی در خانه و آپارتمان دارد. طراحی سیستم HVAC با در نظر گرفتن هزینه های اولیه، هزینه های اجرا و انتشار CO2، و غیره اجرا شد.
کلمات کلیدی: سیستم HVAC | شبیه سازی انرژی | مصرف انرژی اولیه | طراحی بهینه |
مقاله ترجمه شده |