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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]
  • Physics-informed machine learning for metamodeling thermal comfort in
           non-air-conditioned buildings

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      Abstract: Abstract There is a growing need for accurate and interpretable machine learning models of thermal comfort in buildings. Physics-informed machine learning could address this need by adding physical consistency to such models. This paper presents metamodeling of thermal comfort in non-air-conditioned buildings using physics-informed machine learning. The studied metamodel incorporated knowledge of both quasi-steady-state heat transfer and dynamic simulation results. Adaptive thermal comfort in an office located in cold and hot European climates was studied with the number of overheating hours as index. A one-at-a-time method was used to gain knowledge from dynamic simulation with TRNSYS software. This knowledge was used to filter the training data and to choose probability distributions for metamodel forms alternative to polynomial. The response of the dynamic model was positively skewed; and thus, the symmetric logistic and hyperbolic secant distributions were inappropriate and outperformed by positively skewed distributions. Incorporating physical knowledge into the metamodel was much more effective than doubling the size of the training sample. The highly flexible Kumaraswamy distribution provided the best performance with R2 equal to 0.9994 for the cold climate and 0.9975 for the hot climate. Physics-informed machine learning could combine the strength of both physics and machine learning models, and could therefore support building design with flexible, accurate and interpretable metamodels.
      PubDate: 2023-02-01
       
  • Validation of virtual sensor-assisted Bayesian inference-based in-situ
           sensor calibration strategy for building HVAC systems

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      Abstract: Abstract For building heating, ventilation and air-conditioning systems (HVACs), sensor faults significantly affect the operation and control. Sensors with accurate and reliable measurements are critical for ensuring the precise indoor thermal demand. Owing to its high calibration accuracy and in-situ effectiveness, a virtual sensor (VS)-assisted Bayesian inference (VS-BI) sensor calibration strategy has been applied for HVACs. However, the application feasibility of this strategy for wider ranges of different sensor types (within-control-loop and out-of-control-loop) with various sensor bias fault amplitudes, and influencing factors that affect the practical in-situ calibration performance are still remained to be explored. Hence, to further validate its in-situ calibration performance and analyze the influencing factors, this study applied the VS-BI strategy in a HVAC system including a chiller plant with air handle unit (AHU) terminal. Three target sensors including air supply (SAT), chilled water supply (CHS) and cooling water return (CWR) temperatures are investigated using introduced sensor bias faults with eight different amplitudes of [−2 °C, +2 °C] with a 0.5 °C interval. Calibration performance is evaluated by considering three influencing factors: (1) performance of different data-driven VSs, (2) the influence of prior standard deviations σ on in-situ sensor calibration and (3) the influence of data quality on in-situ sensor calibration from the perspective of energy conservation and data volumes. After comparison, a long short term memory (LSTM) is adopted for VS construction with determination coefficient R-squared of 0.984. Results indicate that σ has almost no impact on calibration accuracy of CHS but scanty impact on that of SAT and CWR. The potential of using a prior standard deviation σ to improve the calibration accuracy is limited, only 8.61% on average. For system within-control-loop sensors like SAT and CHS, VS-BI obtains relatively high in-situ sensor calibration accuracy if the data quality is relatively high.
      PubDate: 2023-02-01
       
  • A modelling method for large-scale open spaces orientated toward
           coordinated control of multiple air-terminal units

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      Abstract: Abstract The temperature distribution is always assumed to be homogeneous in a traditional single-input-single-output (SISO) air conditioning control strategy. However, the airflow inside is more complicated and unpredictable. This study proposes a zonal temperature control strategy with a thermal coupling effect integrated for air-conditioned large-scale open spaces. The target space was split into several subzones based on the minimum controllable air terminal units in the proposed method, and each zone can be controlled to its own set-point while considering the thermal coupling effect from its adjacent zones. A numerical method resorting to computational fluid dynamics was presented to obtain the heat transfer coefficients (HTCs) under different air supply scenarios. The relationship between heat transfer coefficient and zonal temperature difference was linearized. Thus, currently available zonal models in popular software can be used to simulate the dynamic response of temperatures in large-scale indoor open spaces. Case studies showed that the introduction of HTCs across the adjacent zones was capable of enhancing the precision of temperature control of large-scale open spaces. It could satisfy the temperature requirements of different zones, improve thermal comfort and at least 11% of energy saving can be achieved by comparing with the conventional control strategy.
      PubDate: 2023-02-01
       
  • Timetabling optimization of classrooms and self-study rooms in university
           teaching buildings based on the building controls virtual test bed
           platform considering energy efficiency

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      Abstract: Abstract The energy consumption of a teaching building can be effectively reduced by timetable optimization. However, in most studies that explore methods to reduce building energy consumption by course timetable optimization, self-study activities are not considered. In this study, an MATLAB-EnergyPlus joint simulation model was constructed based on the Building Controls Virtual Test Bed platform to reduce building energy consumption by optimizing the course schedule and opening strategy of self-study rooms in a holistic way. The following results were obtained by taking a university in Xi’an as an example: (1) The energy saving percentages obtained by timetabling optimization during the heating season examination week, heating season non-examination week, cooling season examination week, and cooling season non-examination week are 35%, 29.4%, 13.4%, and 13.4%, respectively. (2) Regarding the temporal arrangement, most courses are scheduled in the morning during the cooling season and afternoon during the heating season. Regarding the spatial arrangement, most courses are arranged in the central section of the middle floors of the building. (3) During the heating season, the additional building energy consumption incurred by the opening of self-study rooms decreases when duty heating temperature increases.
      PubDate: 2023-02-01
       
  • A demand side management approach to increase self-consumption in
           buildings

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      Abstract: Abstract There is a growing interest in increasing the presence of renewable energy in the electric network. Photovoltaic production from grid-connected systems is leading this growth in terms of households. Alongside this development, concern about network security has emerged, because excesses of intermittent renewable energy on the grid could exceed voltage limits. Self-consumption, understood as the capacity of the producer to consume his or her own production, can partially solve these problems. Thermostatic controllable loads, such as heating and cooling, represent 50% of the total amount of energy consumed by buildings; the proper allocation of these loads could be a driving force for self-consumption. In this study, a demand side management strategy is proposed based on a building energy model equipped with an inverter heat pump coupled with a photovoltaic plant. The goal is to maximize the use of local energy from the photovoltaic plant (self-consumption), reducing the export and import of energy to and from the grid. This goal is achieved by optimizing the set-points in each room. An array of optimal set-points over six years is presented. The results show the capacity of the methodology to match similar values of self-consumption (70% in winter and 50% in summer) obtained by strategies based on chemical batteries. The findings are shown in an energy matching chart at different levels of detail (yearly and monthly). Color bubbles are added to the matching chart to help visualize the unmatched energy of the system graphically. In comparison with actual model predictive control technologies, this study’s strategy offers great simplicity and a large saving in computational time.
      PubDate: 2023-02-01
       
  • Nationwide evaluation of energy and indoor air quality predictive control
           and impact on infection risk for cooling season

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      Abstract: Abstract Since the coronavirus disease 2019, the extended time indoors makes people more concerned about indoor air quality, while the increased ventilation in seeks of reducing infection probability has increased the energy usage from heating, ventilation, and air-conditioning systems. In this study, to represent the dynamics of indoor temperature and air quality, a coupled grey-box model is developed. The model is identified and validated using a data-driven approach and real-time measured data of a campus office. To manage building energy usage and indoor air quality, a model predictive control strategy is proposed and developed. The simulation study demonstrated 18.92% energy saving while maintaining good indoor air quality at the testing site. Two nationwide simulation studies assessed the overall energy saving potential and the impact on the infection probability of the proposed strategy in different climate zones. The results showed 20%–40% energy saving in general while maintaining a predetermined indoor air quality setpoint. Although the infection risk is increased due to the reduced ventilation rate, it is still less than the suggested threshold (2%) in general.
      PubDate: 2023-02-01
       
  • Numerical evaluation of the use of vegetation as a shelterbelt for
           enhancing the wind and thermal comfort in peripheral and lateral-type
           skygardens in highrise buildings

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      Abstract: Abstract Skygardens or skycourts are a unique architectural intervention in the built environment, enhancing the social, economic, and environmental values of the building. It allows occupants to connect and experience outdoor freshness within a semi-enclosed environment. However, skygardens located on a highrise building may generate intense wind gusts, endangering the safety of occupants. Using a validated computational fluid dynamics model, this study investigates the potential of various vegetative barriers or shelterbelts in attenuating the high wind speeds encountered in such spaces and the impact on wind and thermal comfort. Three skygarden configurations were investigated with and without vegetative barriers, simplified and modelled as porous zones, and their effect was studied on the velocity and temperature profile at the occupants’ level. The results indicate that while hedges and trees can offer resistance to airflow, trees provide higher temperature reduction. However, a combination of vegetative and geometrical barriers provides the most optimal condition in the skygarden. The study has identified the importance of assessing wind attenuation characteristics of tree plantations on highrise skygarden, and the results can be used in designing intervention strategies. Moreover, vegetation can attenuate pollutants and mitigate poor air quality by surface deposition, and future studies should investigate in that direction.
      PubDate: 2023-02-01
       
  • Modelling occupant behaviour for urban scale simulation: Review of
           available approaches and tools

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      Abstract: Abstract Urban building energy modelling (UBEM) is considered one of the high-performance computational tools that enable analyzing energy use and the corresponding emission of different building sectors at large scales. However, the efficiency of these models relies on their capability to estimate more realistic building performance indicators at different temporal and spatial scales. The uncertainty of modelling occupants’ behaviours (OB) aspects is one of the main reasons for the discrepancy between the UBEM predicted results and the building’s actual performance. As a result, research efforts focused on improving the approaches to model OB at an urban scale considering different diversity factors. On the other hand, the impact of occupants in the current practice is still considered through fixed schedules and behaviours pattern. To bridge the gap between academic efforts and practice, the applicability of OB models to be integrated into the available UBEM tools needs to be analyzed. To this end, this paper aims to investigate the flexibility and extensibility of existing UBEM tools to model OB with different approaches by (1) reviewing UBEM’s current workflow and the main characteristics of its inputs, (2) reviewing the existing OB models and identifying their main characteristics and level of details that can contribute to UBEM accuracy, (3) providing a breakdown of the occupant-related features in the commonly used tools. The results of this investigation are relevant to researchers and tool developers to identify areas for improvements, as well as urban energy modellers to understand the different approaches to model OB in available tools.
      PubDate: 2023-02-01
       
  • Monitoring the green evolution of vernacular buildings based on deep
           learning and multi-temporal remote sensing images

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      Abstract: Abstract The increasingly mature computer vision (CV) technology represented by convolutional neural networks (CNN) and available high-resolution remote sensing images (HR-RSIs) provide opportunities to accurately measure the evolution of natural and artificial environments on Earth at a large scale. Based on the advanced CNN method high-resolution net (HRNet) and multi-temporal HR-RSIs, a framework is proposed for monitoring a green evolution of courtyard buildings characterized by their courtyards being roofed (CBR). The proposed framework consists of an expert module focusing on scenes analysis, a CV module for automatic detection, an evaluation module containing thresholds, and an output module for data analysis. Based on this, the changes in the adoption of different CBR technologies (CBRTs), including light-translucent CBRTs (LT-CBRTs) and non-light-translucent CBRTs (NLT-CBRTs), in 24 villages in southern Hebei were identified from 2007 to 2021. The evolution of CBRTs was featured as an inverse S-curve, and differences were found in their evolution stage, adoption ratio, and development speed for different villages. LT-CBRTs are the dominant type but are being replaced and surpassed by NLT-CBRTs in some villages, characterizing different preferences for the technology type of villages. The proposed research framework provides a reference for the evolution monitoring of vernacular buildings, and the identified evolution laws enable to trace and predict the adoption of different CBRTs in a particular village. This work lays a foundation for future exploration of the occurrence and development mechanism of the CBR phenomenon and provides an important reference for the optimization and promotion of CBRTs.
      PubDate: 2023-02-01
       
  • Framework on low-carbon retrofit of rural residential buildings in arid
           areas of northwest China: A case study of Turpan residential buildings

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      Abstract: Abstract At present, buildings in arid and hot regions are facing severe challenges of indoor comfort improvement and carbon emission reduction, especially in rural areas. Multi-objective optimization could be an effective tool for tackling the aforementioned challenges. Therefore, this paper proposes a life-cycle optimization framework considering thermal comfort, which is beneficial to promoting residents’ motivation for low-carbon retrofit in arid climate regions. First, in response to the above problems, three objective functions are specified in the framework, which are global warming potential (GWP), life cycle cost (LCC), and thermal discomfort hours (TDH). To improve the optimization efficiency, this research uses Deep Neural Networks (DNN) combined with NSGA-II to construct a high-precision prediction model (meta-model for optimization) based on the energy consumption simulation database formed by the orthogonal multi-dimensional design parameters. The accuracy index of the modified model is R2 > 0.99, cv(RMSE) ≤ 1%, and NMBE ≤ 0.2%, which gets rid of the dilemma of low prediction accuracy of traditional machine learning models. In the scheme comparison and selection stage, the TOPSIS based on two empowerment methods is applied to meet different design tendencies, where the entropy-based method can avoid the interference of subjective preference and significantly improve the objectivity and scientific nature of decision analysis. Additionally, sensitivity analysis is conducted on the variables, which supports guidance for practitioners to carry out the low-carbon design. Finally, the multi-objective optimization analysis for a farmhouse in Turpan is taken as a case study to evaluate the performance of the framework. The results show that the framework could significantly improve the building performance, with 60.8%, 52.5%, and 14.2% reduction in GWP, LCC, and TDH, respectively.
      PubDate: 2023-02-01
       
  • 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
       
 
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