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Building Simulation
Journal Prestige (SJR): 0.839
Citation Impact (citeScore): 2
Number of Followers: 2  
 
  Hybrid Journal Hybrid journal (It can contain Open Access articles)
ISSN (Print) 1996-8744 - ISSN (Online) 1996-3599
Published by Springer-Verlag Homepage  [2467 journals]
  • Best Paper Award 2021

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      PubDate: 2023-01-01
       
  • Non-uniform operative temperature distribution characteristics and
           heat-source-controlled core-area range of local heating radiators

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      Abstract: Abstract Heating the whole space, which is currently used in northern China, leads to high energy consumption and substantial pollution. A transition to local heating has the potential to help address this problem. In this paper, the effects of radiator-related parameters (position, power, and size) and room-related parameters (aspect ratio and height) on local heating were studied. Two evaluation indices, the effective coefficient of operative temperature (OTEC) and the effective coefficient of local heating (LHEC), were proposed. In addition, the heat source-control core-area (HSCCA) was proposed, and the effect range of heat sources in the space was evaluated by the attenuation of operative temperature. The findings demonstrated that the radiator position has a greater influence on local heating than size. When the position of the radiator was changed from “close to the inner wall” to “close to the outer wall”, the LHEC (the interior one-quarter of room is a local heating zone) was found to decrease by 73%. The size of the radiator, which is close to the inner wall, doubled or quadrupled, and the LHEC increased by 9% and 18%. Moreover, rooms with a larger aspect ratio or small room height were found to be the most optimal for local heating applications. The area of the HSCCA decreased as the position of the radiator approached the outer wall. The findings of this study can be used as a design reference for the radiator when the heating mode changes from “full-space heating” to “local heating”.
      PubDate: 2023-01-01
       
  • Evaluation of the thermal performance of radiant floor heating system with
           the influence of unevenly distributed solar radiation based on the theory
           of discretization

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      Abstract: Abstract In the building with many transparent envelopes, solar radiation can irradiate on the local surface of floor and cause overheating. The local thermal comfort in the room will be dissatisfactory and the thermal performance of radiant floor will be strongly affected. However, in many current calculation models, solar radiation on the floor surface is assumed to be uniformly distributed, resulting in the inaccurate evaluation of the thermal performance of the radiant floor. In this paper, a calculation model based on the theory of discretization and the RC thermal network is proposed to calculate the dynamic thermal performance of radiant floor with the consideration of unevenly distributed solar radiation. Then, the discretization model is experimentally validated and is used to simulate a radiant floor heating system of an office room in Lhasa. It is found that with the unevenly distributed solar radiation, the maximum surface temperature near the south exterior window can reach up to 35.6 °C, which exceeds the comfort temperature limit and is nearly 8.5 °C higher than that in the north zone. Meanwhile, the heating capacity of the radiant floor in the irradiated zone can reach up to 171 W/m2, while that in the shaded zone is only 79 W/m2. The model with the assumption of uniformly distributed solar radiation ignores the differences between the south and north zones and fails to describe local overheating in the irradiated zones. By contrast, the discretization model can more accurately evaluate the thermal performance of radiant floor with the influence of real solar radiation. Based on this discretization model, novel design and control schemes of radiant floor heating system can be proposed to alleviate local overheating and reduce heating capacity in the irradiated zone.
      PubDate: 2023-01-01
       
  • Numerical analysis on phase change progress and thermal performance of
           different roofs integrated with phase change material (PCM) in Moroccan
           semi-arid and Mediterranean climates

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      Abstract: Abstract Phase change material (PCM) applied to roofs can weak external heat entering the room to reduce air-conditioning energy consumption. In this study, three forms of macro-encapsulated PCM roofs with different PCMs (RT27, RT31, RT35HC, PT37) are proposed. The effects of PCM thickness, the encapsulation forms, and different PCMs on the thermal performance of the roof are discussed in Moroccan semi-arid and Mediterranean climates. The results show that as the PCM thickness increases, the peak temperature attenuation of the roof inner surface decreases. In two climates, the pure PCM layer among the three encapsulation forms (i.e. pure PCM layer, PCM in aluminum tubes, PCM in triangular aluminum) is the easiest to appear the phenomenon of insufficient heat storage and release, while the reduction of the peak inner surface temperature and time lag is the most satisfying. For the PCM in the aluminum tube, phase change time is the shortest and the latent heat utilization ratio is the highest, while thermal regulation performance is the least satisfying. The PCM in triangular aluminum can improve the latent heat utilization ratio significantly, and its thermal regulation performance is in the middle. In semi-arid climate, the time lag increases with phase change temperature increasing. The time lag could reach up to 6 h with 37 °C phase transition temperature. In Mediterranean climate, the longest time lag with RT31 is 5 h, while the lowest peak inner surface temperature appears with RT27. The obtained conclusions could provide guidance for the application of PCM roofs in these two climates.
      PubDate: 2023-01-01
       
  • Uncertainty and parameter ranking analysis on summer thermal
           characteristics of the hydronic thermal barrier for low-energy buildings

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      Abstract: Abstract The hydronic thermal barrier (HTB) makes the building envelope gradually regarded as a multi-functional element, which is an opportunity to transform thermal insulation solutions from high to zero-carbon attributes. However, inappropriate design, construction, and operation control may lead to issues like low efficiency and high investment, and even the opposite technical effects. In this paper, a comprehensive uncertainty and variable ranking analysis is numerically conducted to explore the influence mechanism of twelve risk variables on three types and five thermal performance indexes under summer conditions. The uncertainty analysis results showed that the correct application of HTB could significantly reduce the heat gain that needs to be handled by the traditional air-conditioning system and even have the technical effect of auxiliary cooling if the variables are appropriately selected. The comprehensive influences of water temperature, room temperature, charging duration, and thermal conductivity of the HTB layer were in the first 1/3 range. Among them, the first two variables were identified as the two most influential variables, and they had a significant mutual restriction relationship in all other four indexes except for the exterior surface cold loss. The recommended charging duration was not less than eight hours in practical application, and the HTB layer with a higher thermal conductivity value but less than 3.3 W/(m·°C) was suggested. Besides, the climate zone was no longer the most influential variable affecting the mean radiant temperature of the interior surface due to the combined effects of HTB and static thermal insulation measures. In addition, pipe spacing should preferably be selected between 100 and 250 mm to help form a continuous thermal buffer zone inside the building envelope.
      PubDate: 2023-01-01
       
  • Development of a Bayesian inference model for assessing ventilation
           condition based on CO2 meters in primary schools

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      Abstract: Abstract Outdoor fresh air ventilation plays a significant role in reducing airborne transmission of diseases in indoor spaces. School classrooms are considerably challenged during the COVID-19 pandemic because of the increasing need for in-person education, untimely and incompleted vaccinations, high occupancy density, and uncertain ventilation conditions. Many schools started to use CO2 meters to indicate air quality, but how to interpret the data remains unclear. Many uncertainties are also involved, including manual readings, student numbers and schedules, uncertain CO2 generation rates, and variable indoor and ambient conditions. This study proposed a Bayesian inference approach with sensitivity analysis to understand CO2 readings in four primary schools by identifying uncertainties and calibrating key parameters. The outdoor ventilation rate, CO2 generation rate, and occupancy level were identified as the top sensitive parameters for indoor CO2 levels. The occupancy schedule becomes critical when the CO2 data are limited, whereas a 15-min measurement interval could capture dynamic CO2 profiles well even without the occupancy information. Hourly CO2 recording should be avoided because it failed to capture peak values and overestimated the ventilation rates. For the four primary school rooms, the calibrated ventilation rate with a 95% confidence level for fall condition is 1.96±0.31 ACH for Room #1 (165 m3 and 20 occupancies) with mechanical ventilation, and for the rest of the naturally ventilated rooms, it is 0.40±0.08 ACH for Room #2 (236 m3 and 21 occupancies), 0.30±0.04 or 0.79±0.06 ACH depending on occupancy schedules for Room #3 (236 m3 and 19 occupancies), 0.40±0.32,0.48±0.37,0.72±0.39 ACH for Room #4 (231 m3 and 8–9 occupancies) for three consecutive days.
      PubDate: 2023-01-01
       
  • Association between the infection probability of COVID-19 and ventilation
           rates: An update for SARS-CoV-2 variants

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      Abstract: Abstract Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), the cause of the current coronavirus disease 2019 (COVID-19) pandemic, is evolving. Thus, the risk of airborne transmission in confined spaces may be higher, and corresponding precautions should be re-appraised. Here, we obtained the quantum generation rate (q) value of three SARS-CoV-2 variants (Alpha, Delta, and Omicron) for the Wells-Riley equation with a reproductive number-based fitted approach and estimated the association between the infection probability and ventilation rates. The q value was 89–165 h−1 for Alpha variant, 312–935 h−1 for Delta variant, and 725–2,345 h−1 for Omicron variant. The ventilation rates increased to ensure an infection probability of less than 1%, and were 8,000–14,000 m3 h−1, 26,000–80,000 m3 h−1, and 64,000–250,000 m3 h−1 per infector for the Alpha, Delta, and Omicron variants, respectively. If the infector and susceptible person wore N95 masks, the required ventilation rates decreased to about 1/100 of the values required without masks, which can be achieved in most typical scenarios. An air purifier was ineffective for reducing transmission when used in scenarios without masks. Preventing prolonged exposure time in confined spaces remains critical in reducing the risk of airborne transmission for highly contagious SARS-CoV-2 variants.
      PubDate: 2023-01-01
       
  • An effective method to determine bedding system insulation based on
           measured data

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      Abstract: Abstract The thermal environment is an essential factor that affects sleep quality. In many circumstances, the bed microenvironment is more important than the ambient environment because of the large covered area of the human body and the close contact between the bedding system and the human body. The main objective of this research is to establish an effective method to determine bedding system insulation. A thermal manikin was used in the measurement of bedding system insulation. Three different types of quilts, which were filled with cotton, polyester and duvet respectively, were chosen to be tested. In total ten different quilts with different materials and weights were involved in the test. Four regular arrangements of covers were chosen with coverage rates of 94.1%, 85.9%, 70.6%, and 54.4% to test. A total of 64 bedding systems were tested to build an effective method to determine the bedding system insulation. On the basis of test data, the change of bedding system insulation with coverage was found to be nonlinear. Exponential fitting was applied to establish an insulation evaluation method for bedding system insulation. In addition, the effects of quilt cover and sleepwear on bedding system insulation were discussed and thermal insulation increment caused by quilt cover and sleepwear were estimated. The relationships between neutral indoor temperature and weight per unit area of the quilt for different coverage rates have been quantified based on existing subject experiments. This research provides an effective method to determine bedding system insulation, which can be widely used in thermal comfort research and HVAC system design.
      PubDate: 2023-01-01
       
  • Multi-objective optimization of equipment capacity and heating network
           design for a centralized solar district heating system

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      Abstract: Abstract Northwest China has abundant solar energy resources and a large demand for winter heating. Using solar energy for centralized heating is a clean and effective way to solve local heating problems. While present studies usually decoupled solar heating stations and the heating network in the optimization design of centralized solar heating systems, this study developed a joint multi-objective optimization model for the equipment capacity and the diameters of the heating network pipes of a centralized solar district heating system, using minimum total life cycle cost and CO2 emission of the system as the optimization objectives. Three typical cities in northwest China with different solar resource conditions (Lhasa, Xining, and Xi’an) were selected as cases for analysis. According to the results, the solar heating system designed using the method proposed in this study presents lower economic cost and higher environmental protection in comparison to separately optimizing the design of the solar heating station and the heating network. Furthermore, the solar fraction of the optimal systems are 90%, 70%, and 31% for Lhasa, Xining, and Xi’an, and the minimum water supply temperatures are 55 °C, 50 °C, and 65 °C for an optimal economy and 55 °C, 45 °C, and 45 °C for optimal environmental protection, respectively. It was also established that the solar collector price has a greater impact on the equipment capacity of the solar heating station than the gas boiler price.
      PubDate: 2023-01-01
       
  • Residential building performance analysis at near extreme weather
           conditions in Hong Kong through a thermal-comfort-based strategy

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      Abstract: Abstract The precise building performance assessment of residential housings in subtropical regions is usually more difficult than that for the commercial premises due to the much more complicated behavior of the occupants with regard to the change in indoor temperature. The conventional use of a fixed schedule for window opening, clothing insulation and cooling equipment operation cannot reflect the real situation when the occupants respond to the change in thermal comfort, thus affecting the appropriateness of the assessment results. To rectify the situation, a new modeling strategy in which the modification of the various operation schedules was based on the calculated thermal comfort (TC), was developed in this study. With this new TC-based strategy, the realistic building performances under different cooling provision scenarios applied to a high-rise residential building under the near extreme weather conditions were investigated and compared. It was found that sole provision of ventilation fans could not meet the zone thermal comfort by over 68% of the time, and air-conditioning was essential. The optimal use of ventilation fans for cooling could only help reduce the total cooling energy demand by less than 12% at best which could only be realistically evaluated by adopting the present strategy. Parametric studies were conducted which revealed that some design factors could offer opportunities for reducing the total cooling energy under the near extreme weather conditions.
      PubDate: 2023-01-01
       
  • ROBOD, room-level occupancy and building operation dataset

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      Abstract: Abstract The availability of the building’s operation data and occupancy information has been crucial to support the evaluation of existing models and development of new data-driven approaches. This paper describes a comprehensive dataset consisting of indoor environmental conditions, Wi-Fi connected devices, energy consumption of end uses (i.e., HVAC, lighting, plug loads and fans), HVAC operations, and outdoor weather conditions collected through various heterogeneous sensors together with the ground truth occupant presence and count information for five rooms located in a university environment. The five rooms include two different-sized lecture rooms, an office space for administrative staff, an office space for researchers, and a library space accessible to all students. A total of 181 days of data was collected from all five rooms at a sampling resolution of 5 minutes. This dataset can be used for benchmarking and supporting data-driven approaches in the field of occupancy prediction and occupant behaviour modelling, building simulation and control, energy forecasting and various building analytics.
      PubDate: 2022-12-01
       
  • Vapor intrusion in buildings: Development of semi-empirical models
           including lateral separation between the building and the pollution source
           

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      Abstract: Abstract Future constructions in the context of the industrial wastelands reuse may be exposed to Vapor Intrusion (VI). VI can be evaluated by combining in-situ measures and analytical models to evaluate exposure risk in future indoor environments. However, the assumptions in the existing models may reduce their accuracy when they do not meet the characteristics of real situations. Wrong estimations of indoor concentration levels may lead to inappropriate solutions against VI. In this context, new semi-empirical models (SEM) are proposed in order to better specify pollution scenarios and thus increase the accuracy of VI estimations. This development is based on a parametric study (numerical CFD) and a dimensionless analysis combined to existing VI models that consider a continuous source distribution in the soil. These expressions allow to better take into account the source position in the soil (i.e. depth and lateral source/building separation), soil properties (air permeability, diffusion coefficient of the pollutant, …) and building features (building foundation, indoor pressure, air exchange rate, …) in the estimation of indoor concentration levels. The obtained results with the proposed SEM were compared with a numerical CFD model and available experimental data, showing good accuracy in the estimation of VI. Given the advantages of these new models, they can provide better precision in the health risk assessments associated with VI. Furthermore, these expressions can be easily integrated into building ventilation codes allowing to consider air exchange rate and indoor pressure variations over time.
      PubDate: 2022-12-01
       
  • Impact of climate change on outdoor design conditions and implications to
           peak loads

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      Abstract: Abstract This study exams the impact of climate change on outdoor design conditions and peak loads of five Chinese cities over the five major climate zones for the winter and summer conditions. The design dry-bulb temperature (DDBT) and the coincident wet-bulb temperature (CWBT) for two 30-year periods; 1971–2000 and 1984–2013 were analysed. It was found that the DDBT of the period 1984–2013 was higher than that of the period 1971–2000, whereas the CWBT and the corresponding outdoor enthalpy of the period 1984–2013 was lower than that of 1971–2000 at the various cumulative frequencies. This trend implies that the increment in conductive heat gain through the building envelope due to the rising temperature can be lower than the reduction in fresh air load due to the lower outdoor air enthalpy. In this case, the peak cooling loads may reduce in all five cities under study, and this is different from the widely held view that global warming will lead to more stringent outdoor design conditions, higher peak cooling loads and larger heating, ventilation and air conditioning (HVAC) plants than the current or historical status. The implications to the “free-cooling” of HVAC systems with enthalpy control are discussed as well.
      PubDate: 2022-12-01
       
  • Cooling load characteristics of indoor spaces conditioned by decoupled
           radiant cooling unit with low radiant temperature

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      Abstract: Abstract Decoupled radiant cooling units (DRCUs) are capable of increasing the cooling capacity without increasing condensation risks even using a much lower cooling temperature than conventional radiant cooling units (CRCUs). However, it is unclear whether DRCUs using low radiant cooling temperature will increase the cooling load of the conditioned indoor spaces. In this study, the cooling load characteristics of a thermal chamber conditioned by a DRCU was investigated through developing a steady-state analysis model suitable for both DRCUs and CRCUs. The total/radiative heat flux, as well as the heat exchange with a thermal manikin and walls were analysed under different surface temperatures of DRCUs. The effect of the emissivity of the thermal chamber’ external wall on the cooling load was also investigated. Results indicated that the cooling load under the DRCU was slightly smaller than that under the CRCU when the same operative environment was created. Decreasing the infrared emissivity of the exterior wall’s inner surface could lead to a significant decrease in the cooling load for both the DRCU and CRCU. By decreasing the wall emissivity from 0.9 to 0.1, the total cooling load of the DRCU can be decreased by 8.4% and the heat gain of the exterior wall decreased by 21.6%. This study serves as a reference for developing the analysis model and understanding the load characteristics when DRCUs are used to create the thermal environment for indoor spaces.
      PubDate: 2022-12-01
       
  • Development of data-driven thermal sensation prediction model using
           quality-controlled databases

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      Abstract: Abstract Predicting the thermal sensations of building occupants is challenging, but useful for indoor environment conditioning. In this study, a data-driven thermal sensation prediction model was developed using three quality-controlled thermal comfort databases. Different machine-learning algorithms were compared in terms of prediction accuracy and rationality. The model was further improved by adding categorical inputs, and building submodels and general models for different contexts. A comprehensive data-driven thermal sensation prediction model was established. The results indicate that the multilayer perceptron (MLP) algorithm achieves higher prediction accuracy and more rational results than the other four algorithms in this specific case. Labeling AC and NV scenarios, climate zones, and cooling and heating seasons can improve model performance. Establishing submodels for specific scenarios can result in better thermal sensation vote (TSV) predictions than using general models with or without labels. With 11 submodels corresponding to 11 scenarios, and three general models without labels, the final TSV prediction model achieved higher prediction accuracy, with 64.7%–90.7% fewer prediction errors (reducing SSE by 3.2–4.9) than the predicted mean vote (PMV). Possible applications of the new model are discussed. The findings of this study can help in development of simple, accurate, and rational thermal sensation prediction tools.
      PubDate: 2022-12-01
       
  • Airborne transmission during short-term events: Direct route over indirect
           route

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      Abstract: Abstract Numerous short-term exposure events in public spaces were reported during the COVID-19 pandemic, especially during the spread of Delta and Omicron. However, the currently used exposure risk assessment models and mitigation measures are mostly based on the assumption of steady-state and complete-mixing conditions. The present study investigates the dynamics of airborne transmission in short-term events when a steady state is not reached before the end of the events. Large-eddy simulation (LES) is performed to predict the airborne transmission in short-term events, and three representative physical distances between two occupants are examined. Both time-averaged and phase-averaged exposure indices are used to evaluate the exposure risk. The results present that the exposure index in the short-term events constantly varies over time, especially within the first 1/ACH (air changes per hour) hour of exposure between occupants in close proximity, posing high uncertainty to the spatial and temporal evolutions of the risk of cross-infection. The decoupling analysis of the direct and indirect airborne transmission routes indicates that the direct airborne transmission is the predominated route in short-term events. It suggests also that the general dilution ventilation has a relatively limited efficiency in mitigating the risk of direct airborne transmission, but determines largely the occurrence time of the indirect one. Given the randomness, discreteness, localization, and high-risk characteristics of direct airborne transmission, a localized method that has a direct interference on the respiratory flows would be better than dilution ventilation for short-term events, in terms of both efficiency and cost.
      PubDate: 2022-12-01
       
  • The effect of classroom wall color on learning performance: A virtual
           reality experiment

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      Abstract: Abstract The wall color has the certain impact on the learning performance of college students in the classroom. To find the suitable wall color to improve the learning performance, this study used the virtual reality (VR) technology to build five virtual classrooms with yellow, red, white, blue, and green walls, while the subjective evaluation and objective physiological indicators of college students were collected. The subjective survey showed the cold-colored walls such as blue and green had the highest levels of relaxation and pleasure, while the warm-colored walls such as yellow and red had the better attention and learning performance. And the white-walled classroom had the lowest subjective evaluation and the worst learning performance, but the white wall was widely used in the present class room. Physiological test results showed the yellow wall had the higher HRV-nLF/nHF and low-β & high-β frequencies in the FP2 channel in the frontal, but the white wall had the lowest scores. Moreover, the correlation analysis had confirmed electroencephalography (EEG) and electrocardiogram (ECG) indicators could be employed to evaluate the learning performance. These findings provide an effective reference for the spatial design of university classrooms and a basis for the study of physiological indicators.
      PubDate: 2022-12-01
       
  • The impacts of evaluation indices and normalization methods on E-TOPSIS
           optimization of return vent height for an impinging jet ventilation system
           

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      Abstract: Abstract Stratified air distribution (STRAD) systems have been intensively investigated in recent decades for their energy-saving potential and good indoor air quality performance. However, the evaluation indices used to optimize STRAD systems and the normalization methods for weight calculation vary from one research to another. This study aims to investigate the impacts of evaluation indices on the optimal return vent height of a room cooled by an impinging jet ventilation system (one type of STRAD system). The effects of several widely used normalization methods (i.e., vector normalization, sum normalization, min-max normalization, and no normalization) on indices weights are investigated. The evaluation indices are cooling coil load (Qcoil), energy-saving potential (ΔQcoil), mean age of air (MAA), CO2 mass fraction, temperature difference between the head and ankles (ΔT0.1–1.1), predicted mean vote (PMV), predicted percentage of dissatisfied (PPD), and draft rate (DR). The multi-criteria optimization method is the entropy-based technique for order preference by similarity to ideal solution (E-TOPSIS). As a result, the min-max normalization method evens the weight of each index and results in unreasonable relative weights. Consequently, the raw matrix (i.e., the normalization is omitted) is suggested for weight calculation. Among these indices, ΔT0.1–1.1 and PPD play critical roles. Without ΔT0.1–1.1, the optimal return vent height changes from mid-level to near-floor, while without PPD, it changes to near-ceiling. Another important result is that the Qcoil plays the most trivial role, followed by MAA and DR. Therefore, the optimal return vent height is not determined by energy-saving performance but by performances of thermal comfort.
      PubDate: 2022-12-01
       
  • DeepRadiation: An intelligent augmented reality platform for predicting
           urban energy performance just through 360 panoramic streetscape images
           utilizing various deep learning models

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      Abstract: Abstract Urban energy simulation is critical for understanding and managing energy performance in cities. In this research, we design a novel framework called DeepRadiation, to enable automatic urban environmental performance prediction. By incorporating deep learning strategies, DeepRadiation predicts solar radiation on an urban scale using just panoramic streetscape images without any 3D modeling and simulation. New York City was chosen as the case study for this research. DeepRadiation is comprised of three different deep learning models organized into two stages. The first stage, named DeepRadiation modeling, serves as the framework’s brain. At this stage, solar radiation analysis was performed using a Pix2Pix model, a type of conditional generative adversarial networks (GANs). After extracting GIS data and performing energy simulation analysis to prepare the dataset, the Pix2Pix model was trained on 10000 paired panoramic depth images of streetscapes with only building blocks and related panoramic images of streetscapes with only solar radiation analysis. Two GAN generator evaluation measures named qualitative evaluation and quantitative evaluation were used to validate the trained Pix2Pix model. Both demonstrated high levels of accuracy (qualitative evaluation: 93%, quantitative evaluation: 89%). DeepRadiation application as the DeepRadiation’s sescond stage is the framework’s eyes. At this stage, two convolutional neural network (CNN) models (DeepLabv3 and MiDaS) were used to perform computer vision tasks on panoramic streetscape images, such as semantic segmentation and depth estimation. The DeepRadiation application stage allows urban designers, architects, and urban policymakers to use the DeepRadiation framework and experience the final output via augmented reality.
      PubDate: 2022-11-21
       
  • Model predictive control of indoor thermal environment conditioned by a
           direct expansion air conditioning system

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      Abstract: Abstract Temperature and humidity are two important factors that influence both indoor thermal comfort and air quality. Through varying compressor and supply fan speeds of a direct expansion (DX) air conditioning (A/C) unit, the air temperature and humidity in the conditioned space can be regulated simultaneously. However, most existing controllers are designed to minimize the tracking errors between the system outputs with their corresponding settings as quickly as possible. The energy consumption, which is directly influenced by the compressor and supply fan speeds, is not considered in the relevant controller formulations, and thus the system may not operate with the highest possible energy efficiency. To effectively control temperature and humidity while minimizing the system energy consumption, a model predictive control (MPC) strategy was developed for a DX A/C system, and the development results are presented in this paper. A physically-based dynamic model for the DX A/C system with both sensible and latent heat transfers being considered was established and validated by experiments. To facilitate the design of MPC, the physical model was further linearized. The MPC scheme was then developed by formulating the objective function which sought to minimize the tracking errors of temperature and moisture content while saving energy consumption. Based on the results of command following and disturbance rejection tests, the proposed MPC scheme was capable of controlling temperature and humidity with adequate control accuracy and sensitivity. In comparison to linear-quadratic-Gaussian (LQG) controller, better control accuracy and lower energy consumption could be realized when using the proposed MPC strategy to simultaneously control temperature and humidity.
      PubDate: 2022-11-19
       
 
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