Thermal comfort is determined by the combined effect of the six thermal comfort parameters: temperature, air moisture content, thermal radiation, air relative velocity, personal activity and clothing level as formulated by Fanger through his double heat balance equations. In conventional air conditioning systems, air temperature is the parameter that is normally controlled whilst others are assumed to have values within the specified ranges at the design stage. In Fanger’s double heat balance equation, thermal radiation factor appears as the mean radiant temperature (MRT), however, its impact on thermal comfort is often ignored. This paper discusses the impacts of the thermal radiation field which takes the forms of mean radiant temperature and radiation asymmetry on thermal comfort, building energy consumption and air-conditioning control. Several conditions and applications in which the effects of mean radiant temperature and radiation asymmetry cannot be ignored are discussed. Several misinterpretations that arise from the formula relating mean radiant temperature and the operative temperature are highlighted, coupled with a discussion on the lack of reliable and affordable devices that measure this parameter. The usefulness of the concept of the operative temperature as a measure of combined effect of mean radiant and air temperatures on occupant’s thermal comfort is critically questioned, especially in relation to the control strategy based on this derived parameter. Examples of systems which deliver comfort using thermal radiation are presented. Finally, the paper presents various options that need to be considered in the efforts to mitigate the impacts of the thermal radiant field on the occupants’ thermal comfort and building energy consumption.
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Thermal comfort is determined by the combined effect of the six thermal comfort parameters: temperature, air moisture content, thermal radiation, air relative velocity, personal activity and clothing level as formulated by Fanger through his double heat balance equations. In conventional air conditioning systems, air temperature is the parameter that is normally controlled whilst others are assumed to have values within the specified ranges at the design stage. In Fanger’s double heat balance equation, thermal radiation factor appears as the mean radiant temperature (MRT), however, its impact on thermal comfort is often ignored. This paper discusses the impacts of the thermal radiation field which takes the forms of mean radiant temperature and radiation asymmetry on thermal comfort, building energy consumption and air-conditioning control. Several conditions and applications in which the effects of mean radiant temperature and radiation asymmetry cannot be ignored are discussed. Several misinterpretations that arise from the formula relating mean radiant temperature and the operative temperature are highlighted, coupled with a discussion on the lack of reliable and affordable devices that measure this parameter. The usefulness of the concept of the operative temperature as a measure of combined effect of mean radiant and air temperatures on occupant’s thermal comfort is critically questioned, especially in relation to the control strategy based on this derived parameter. Examples of systems which deliver comfort using thermal radiation are presented. Finally, the paper presents various options that need to be considered in the efforts to mitigate the impacts of the thermal radiant field on the occupants’ thermal comfort and building energy consumption.
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To understand how transition across different thermal zones in a building impacts the thermal perception of occupants, the current work examines occupant feedback in two work environments — nursing staff in hospital wards and the workers in an office. Both studies used a mix of subjective surveys and objective measurements. A total of 96 responses were collected from the hospital wards while 142 were collected from the office. The thermal environment in the hospital wards was perceived as slightly warm on the ASHRAE thermal sensation scale (mean TSV = 1.2), while the office workers rated their environment on the cool side (mean TSV = 0.15). The results also show that when the transitions were across temperature differences within 2 °C, the thermal perception was not impacted by the magnitude of the temperature difference — as reflected in occupant thermal sensation and thermal comfort/thermal acceptability vote. This would imply that the effect of temperature steps on thermal perception, if any, within these boundaries, was extremely short lived. These findings go towards establishing the feasibility of heterogeneous indoor thermal environments and thermal zoning of workspaces for human comfort.
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Older people are often over-represented in morbidity and mortality statistics associated with hot and cold weather, despite remaining mostly indoors. The study “Improving thermal environment of housing for older Australians” focused on assessing the relationships between the indoor environment, building characteristics, thermal comfort and perceived health/wellbeing of older South Australians over a study period that included the warmest summer on record. Our findings showed that indoor temperatures in some of the houses reached above 35 °C. With concerns about energy costs, occupants often use adaptive behaviours to achieve thermal comfort instead of using cooling (or heating), although feeling less satisfied with the thermal environment and perceiving health/wellbeing to worsen at above 28 °C (and below 15 °C). Symptoms experienced during hot weather included tiredness, shortness of breath, sleeplessness and dizziness, with coughs and colds, painful joints, shortness of breath and influenza experienced during cold weather. To express the influence of temperature and humidity on perceived health/wellbeing, a Temperature Humidity Health Index (THHI) was developed for this cohort. A health/wellbeing perception of “very good” is achieved between an 18.4 °C and 24.3 °C indoor operative temperature and a 55% relative humidity. The evidence from this research is used to inform guidelines about maintaining home environments to be conducive to the health/wellbeing of older people. Original publication at MDPI: https://doi.org/10.3390/atmos13010096 © 2022 by the authors. Licensee MDPI.
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Thermal comfort -the state of mind, which expresses satisfaction with the thermal environment- is an important aspect of the building design process as modern man spends most of the day indoors. This paper reviews the developments in indoor thermal comfort research and practice since the second half of the 1990s, and groups these developments around two main themes; (i) thermal comfort models and standards, and (ii) advances in computerization. Within the first theme, the PMV-model (Predicted Mean Vote), created by Fanger in the late 1960s is discussed in the light of the emergence of models of adaptive thermal comfort. The adaptive models are based on adaptive opportunities of occupants and are related to options of personal control of the indoor climate and psychology and performance. Both models have been considered in the latest round of thermal comfort standard revisions. The second theme focuses on the ever increasing role played by computerization in thermal comfort research and practice, including sophisticated multi-segmental modeling and building performance simulation, transient thermal conditions and interactions, thermal manikins.
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As people age, physiological changes affect their thermal perception, sensitivity and regulation. The ability to respond effectively to temperature fluctuations is compromised with physiological ageing, upsetting the homeostatic balance of health in some. As a result, older people can become vulnerable at extremes of thermal conditions in their environment. With population ageing worldwide, it is an imperative that there is a better understanding of older people’s thermal needs and preferences so that their comfort and wellbeing in their living environment can be optimised and healthy ageing achieved. However, the complex changes affecting the physiological layers of the individual during the ageing process, although largely inevitable, cannot be considered linear. They can happen in different stages, speeds and intensities throughout the ageing process, resulting in an older population with a great level of heterogeneity and risk. Therefore, predicting older people’s thermal requirements in an accurate way requires an in-depth investigation of their individual intrinsic differences. This paper discusses an exploratory study that collected data from 71 participants, aged 65 or above, from 57 households in South Australia, over a period of 9 months in 2019. The paper includes a preliminary evaluation of the effects of individual intrinsic characteristics such as sex, body composition, frailty and other factors, on thermal comfort. It is expected that understanding older people’s thermal comfort from the lens of these diversity-causing parameters could lead to the development of individualised thermal comfort models that fully capture the heterogeneity observed and respond directly to older people’s needs in an effective way. (article starts at page 13)
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This study explores if multiple alterations of the classrooms' indoor environmental conditions, which lead to environmental conditions meeting quality class A of Dutch guidelines, result in a positive effect on students' perceptions and performance. A field study, with a between-group experimental design, was conducted during the academic course in 2020–2021. First, the reverberation time (RT) was lowered in the intervention condition to 0.4 s (control condition 0.6 s). Next, the horizontal illuminance (HI) level was raised in the intervention condition to 750 lx (control condition 500 lx). Finally, the indoor air quality (IAQ) in both conditions was improved by increasing the ventilation rate, resulting in a reduction of carbon dioxide concentrations, as a proxy for IAQ, from ~1100 to <800 ppm. During seven campaigns, students' perceptions of indoor environmental quality, health, emotional status, cognitive performance, and quality of learning were measured at the end of each lecture using questionnaires. Furthermore, students' objective cognitive responses were measured with psychometric tests of neurobehavioural functions. Students' short-term academic performance was evaluated with a content-related test. From 201 students, 527 responses were collected. The results showed that the reduction of the RT positively influenced students' perceived cognitive performance. A reduced RT in combination with raised HI improved students' perceptions of the lighting environment, internal responses, and quality of learning. However, this experimental condition negatively influenced students' ability to solve problems, while students' content-related test scores were not influenced. This shows that although quality class A conditions for RT and HI improved students' perceptions, it did not influence their short-term academic performance. Furthermore, the benefits of reduced RT in combination with raised HI were not observed in improved IAQ conditions. Whether the sequential order of the experimental conditions is relevant in inducing these effects and/or whether improving two parameters is already beneficial, is unknown.
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In 2004 the first adaptive thermal comfort guideline was introduced in the Netherlands. Recently a new, upgraded version of this ISSO 74 (ATG) guideline has been developed. The new requirements are hybrid in nature as the 2014 version of the guideline combines elements of traditional non-adaptive comfort standards with elements of adaptive standards. This paper describes the new guideline and explains the rationale behind it. Also changes in comparison with the original 2004 version and issues related to performance verification are discussed. The information presented in this paper can be used by others (other countries) as inspiration material for other new adaptive comfort guidelines and standards.
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Thermal comfort in operating theatres is a less addressed research component of the in-door environment in operating theatres. The air quality naturally gets most attention when considering the risk of surgical site infections. However, the importance of thermal comfort must not be underestimated. In this research, the current thermal comfort situation of staff members is investigated. Results show that the thermal comfort for the members of a surgical team is perceived as not optimal. Application of the PMV and DR models needs further attention when applied for operating theatres. For the investigated ventilation systems, the differences in thermal comfort outcomes are small.
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The built environment requires energy-flexible buildings to reduce energy peak loads and to maximize the use of (decentralized) renewable energy sources. The challenge is to arrive at smart control strategies that respond to the increasing variations in both the energy demand as well as the variable energy supply. This enables grid integration in existing energy networks with limited capacity and maximises use of decentralized sustainable generation. Buildings can play a key role in the optimization of the grid capacity by applying demand-side management control. To adjust the grid energy demand profile of a building without compromising the user requirements, the building should acquire some energy flexibility capacity. The main ambition of the Brains for Buildings Work Package 2 is to develop smart control strategies that use the operational flexibility of non-residential buildings to minimize energy costs, reduce emissions and avoid spikes in power network load, without compromising comfort levels. To realise this ambition the following key components will be developed within the B4B WP2: (A) Development of open-source HVAC and electric services models, (B) development of energy demand prediction models and (C) development of flexibility management control models. This report describes the developed first two key components, (A) and (B). This report presents different prediction models covering various building components. The models are from three different types: white box models, grey-box models, and black-box models. Each model developed is presented in a different chapter. The chapters start with the goal of the prediction model, followed by the description of the model and the results obtained when applied to a case study. The models developed are two approaches based on white box models (1) White box models based on Modelica libraries for energy prediction of a building and its components and (2) Hybrid predictive digital twin based on white box building models to predict the dynamic energy response of the building and its components. (3) Using CO₂ monitoring data to derive either ventilation flow rate or occupancy. (4) Prediction of the heating demand of a building. (5) Feedforward neural network model to predict the building energy usage and its uncertainty. (6) Prediction of PV solar production. The first model aims to predict the energy use and energy production pattern of different building configurations with open-source software, OpenModelica, and open-source libraries, IBPSA libraries. The white-box model simulation results are used to produce design and control advice for increasing the building energy flexibility. The use of the libraries for making a model has first been tested in a simple residential unit, and now is being tested in a non-residential unit, the Haagse Hogeschool building. The lessons learned show that it is possible to model a building by making use of a combination of libraries, however the development of the model is very time consuming. The test also highlighted the need for defining standard scenarios to test the energy flexibility and the need for a practical visualization if the simulation results are to be used to give advice about potential increase of the energy flexibility. The goal of the hybrid model, which is based on a white based model for the building and systems and a data driven model for user behaviour, is to predict the energy demand and energy supply of a building. The model's application focuses on the use case of the TNO building at Stieltjesweg in Delft during a summer period, with a specific emphasis on cooling demand. Preliminary analysis shows that the monitoring results of the building behaviour is in line with the simulation results. Currently, development is in progress to improve the model predictions by including the solar shading from surrounding buildings, models of automatic shading devices, and model calibration including the energy use of the chiller. The goal of the third model is to derive recent and current ventilation flow rate over time based on monitoring data on CO₂ concentration and occupancy, as well as deriving recent and current occupancy over time, based on monitoring data on CO₂ concentration and ventilation flow rate. The grey-box model used is based on the GEKKO python tool. The model was tested with the data of 6 Windesheim University of Applied Sciences office rooms. The model had low precision deriving the ventilation flow rate, especially at low CO2 concentration rates. The model had a good precision deriving occupancy from CO₂ concentration and ventilation flow rate. Further research is needed to determine if these findings apply in different situations, such as meeting spaces and classrooms. The goal of the fourth chapter is to compare the working of a simplified white box model and black-box model to predict the heating energy use of a building. The aim is to integrate these prediction models in the energy management system of SME buildings. The two models have been tested with data from a residential unit since at the time of the analysis the data of a SME building was not available. The prediction models developed have a low accuracy and in their current form cannot be integrated in an energy management system. In general, black-box model prediction obtained a higher accuracy than the white box model. The goal of the fifth model is to predict the energy use in a building using a black-box model and measure the uncertainty in the prediction. The black-box model is based on a feed-forward neural network. The model has been tested with the data of two buildings: educational and commercial buildings. The strength of the model is in the ensemble prediction and the realization that uncertainty is intrinsically present in the data as an absolute deviation. Using a rolling window technique, the model can predict energy use and uncertainty, incorporating possible building-use changes. The testing in two different cases demonstrates the applicability of the model for different types of buildings. The goal of the sixth and last model developed is to predict the energy production of PV panels in a building with the use of a black-box model. The choice for developing the model of the PV panels is based on the analysis of the main contributors of the peak energy demand and peak energy delivery in the case of the DWA office building. On a fault free test set, the model meets the requirements for a calibrated model according to the FEMP and ASHRAE criteria for the error metrics. According to the IPMVP criteria the model should be improved further. The results of the performance metrics agree in range with values as found in literature. For accurate peak prediction a year of training data is recommended in the given approach without lagged variables. This report presents the results and lessons learned from implementing white-box, grey-box and black-box models to predict energy use and energy production of buildings or of variables directly related to them. Each of the models has its advantages and disadvantages. Further research in this line is needed to develop the potential of this approach.
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