Indoor Occupants Research Articles

Beyond academia: Investigating indoor environmental quality and its impact on postgraduate student satisfaction

PurposeUniversities need to provide well-planned physical environments to foster creativity, since the indoor setting plays a crucial role in academic success in the 21st century, influencing their research efforts and future outcomes. This study set out to gain a better understanding of the relationship between Indoor Environmental Quality (IEQ) measures and occupants' fulfilment in their environments, emphasizing the significance of IEQ in improving comfort and well-being. To this end, first the indoor environmental quality (IEQ) in university open-plan research offices (UOROs) in Brunel University London, UK is assessed by measuring various environmental factors through low-cost sensors. The data is then linked to a “Right Now” survey carried out to investigate to what extent do the measured values of the IEQ factors in postgraduate rooms differ from occupant satisfaction. The research showed a significant connection between measured IEQ factors and evaluations made by occupants. Subjective assessments may not always correspond with objective measures. The findings could guide design elements for postgraduate students' use of structures, potentially enhancing productivity. The system combines sensor technologies and Arduino-based monitoring to collect real-time data, improving the comprehension of IEQ and facilitating future study and practical applications in optimizing academic environments for increased productivity and well-being.

Impact of natural ventilation and outdoor environment on indoor air quality and occupant health in low-income tropical housing

Ventilation is pivotal in mitigating indoor pollutants and ensuring comfortable Indoor Air Quality (IAQ) levels globally. The outdoor environment and ventilation mechanisms significantly impact indoor air quality and occupant health. This research investigated the impact of natural ventilation (NV) and outdoor environment (OE) on indoor air quality (AQ) and occupant health (HI) in low-income housing in Kampala City, Uganda. The study followed a mixed methodology approach by employing self-administered questionnaires and statistical modeling using IBM® SPSS® Amos V24 to analyze the relationships between Natural Ventilation (NV), indoor Air Quality (AQ), Outdoor Environment (OE), and occupant health (HI). The research reveals a strong correlation (0.76) between NV and AQ. In contrast, correlations between AQ and OE (0.16) and NV and OE (0.08) are weak. Model comparative fit indices (CFI: 0.984, SRMR: 0.029, RMSEA: 0.053) indicate an excellent fit. Reliability is high with Cronbach's alpha (NV: 0.800, AQ: 0.862, OE: 0.782) and AVE values (NV: 0.832, AQ: 0.869, OE: 0.786). Significant positive relationships were found between NV and AQ and AQ and HI, highlighting the importance of natural ventilation in improving indoor air quality and occupant health. The study supports SDGs 3, 11, and 13, promoting sustainable building practices and promoting health, enhanced living conditions, and lower greenhouse gas emissions.

Research on The local clothing thermal insulation prediction model with different dress state of indoor occupants

In a dynamic and non-uniform thermal environment, adjusting personal dress states is an effective method for achieving thermal comfort. During this process, clothing thermal insulation, as a key parameter in human thermoregulation models, is not uniformly distributed across the body. However, current thermal comfort standards do not provide a direct method for obtaining local clothing thermal insulation. Based on field surveys, this study summarized typical year-round clothing ensembles and common dress states, and tested the local thermal insulation of 40 clothing ensembles and 60 individual garments using a thermal manikin. Subsequently, a prediction model for the local thermal insulation of clothing ensembles was developed based on the overall thermal insulation of individual garments. Meanwhile, it was found that changes in dress state significantly alter the thermal insulation of body parts such as the arms and chest. Simulations conducted using the JOS-3 and UCB models revealed that the lower the ambient temperature, the greater the impact of changes in local clothing thermal insulation on human skin temperature and thermal sensation. Finally, a correction model for different dress states was developed, and comparison with existing models showed the lower root mean square error (RMSE) between the predicted and measured values. This study provides a more accurate and convenient method for determining local thermal insulation, offering a data foundation for improving indoor environmental control and enhancing energy efficiency.

The Impact of Green Building Design on Indoor Air Quality and Occupant Experience

This study examines the impact of green building design on indoor air quality (IAQ) and occupant experience, using Heritage Place in Ikoyi, Lagos, as a case study. Green buildings are known for incorporating sustainable practices such as the use of eco-friendly materials, advanced ventilation systems, and biophilic design elements, all aimed at enhancing environmental sustainability and occupant well-being. A descriptive survey methodology was employed, with data gathered through a questionnaire administered to 50 occupants of the building to assess their perceptions of IAQ, health, comfort, and overall satisfaction. The findings indicate that the majority of respondents reported high levels of satisfaction with IAQ, attributing improvements in their health, productivity, and comfort to the building’s green design features. Specifically, low-emission materials and enhanced ventilation systems were highly praised for reducing pollutants and maintaining a healthy indoor environment. However, a few respondents expressed concerns about inconsistent thermal comfort in certain areas. These results underscore the importance of sustainable design in creating healthier, more productive indoor environments. Recommendations for future green building projects include fine-tuning HVAC systems to improve thermal comfort and enhancing occupant education on the benefits of sustainable features.

A study on the enhancement of energy efficiency and indoor environment through the integration of solar photovoltaic modules in daylighting louver systems

The global energy demand is predicted to increase significantly, with a strong link between energy consumption and CO2 emissions. Lighting energy accounts for a substantial proportion of total energy consumption in buildings, with the potential to be reduced using daylighting louvers. Despite some drawbacks, integrating solar photovoltaic modules with louvers could enhance energy efficiency while addressing the increasing demand for renewable energy systems in new buildings. Therefore, this study explores the potential benefits of integrating solar photovoltaic (PV) modules into a daylighting louver system to simultaneously reduce lighting, cooling, and heating loads and generate solar power. The performance of the proposed louver system was experimentally compared to a conventional building-integrated photovoltaic (BIPV) system installed on a window. The daylighting louver system demonstrated significant improvements in indoor illumination and occupants’ experience compared to the BIPV system. Furthermore, the indoor air temperature in the space with the louver system increased by about 5 degrees compared to the BIPV system, indicating its contribution to heating load reduction. The power generation performance of the louver system reached up to 65 % of the BIPV system, with a total cumulative electricity generation of 64 %. The results suggest that the daylighting louver system can be expanded to enhance its efficiency and address the increasing demand for renewable energy system installations in new buildings. Future studies should investigate the potential of 2-axis or 3-axis louvers to further improve the system’s efficiency.

A comprehensive study on the impacts of thermal comfort on occupants’ thermophysiology and cognitive performances in a radiant cooling environment using physiological measurements

Radiant cooling (RC) systems are increasingly implemented in public buildings to enhance human thermal comfort and reduce building energy consumption. The quality of the indoor thermal environment significantly impacts occupants’ learning and work performance. This investigation explores indoor occupants’ dynamic physiological regulation, thermal comfort, and cognitive performance under radiant whole-wall cooling (RWWC) systems. Twenty-eight participants underwent a summer exposure test task at varying ambient temperatures, yielding four collected data types: indoor physical parameters, physiological parameters (including skin temperature (ST), heart rate (HR)/heart rate variability (HRV), and electroencephalography (EEG)), performance index (PI), and thermal perception. Results indicate that the RWWC system notably improves thermal perception and comfort, with 93 % of participants reporting comfort at a target temperature of 24℃. Air temperature (AT) exerted the most significant effect on changes in ST on the right upper arm (p < 0.01) and left hand back (p < 0.01) at 30℃ compared to the 22℃. The difference in whole-body mean skin temperature (MST) is 2.68℃. The HR decline was more pronounced in higher temperatures, and the HRV representativeness index approached ‘1′ at 24℃. PI was affected by temperature and task type, displaying a non-linear correlation with AT (R2 = 0.6056) and an inverted “U” shaped curve with TSV (R2 = 0.9523). Thermal perception correlates with MST, HRV, and EEG. The study carries implications for the reasonable design of comfortable and healthy RC environments.

Factors Affecting the Indoor Air Quality and Occupants’ Thermal Comfort in Urban Agglomeration Regions in the Hot and Humid Climate of Pakistan

The World Air Quality Index indicates that Pakistan ranks as the third most polluted country, regarding the average (Particulate Matter) PM2.5 concentration, which is 14.2 times higher than the World Health Organization’s annual air quality guideline. It is crucial to implement a program aimed at reducing PM2.5 levels in Pakistan’s urban areas. This review paper highlights the importance of indoor air pollution in urban regions such as Lahore, Faisalabad, Gujranwala, Rawalpindi, and Karachi, while also considering the effects of outdoor air temperature on occupants’ thermal comfort. The study aims to evaluate past methodological approaches to enhance indoor air quality in buildings. The main research question is to address whether there are statistical correlations between the PM2.5 and the operative air temperature and whether other indoor climatic variables have an impact on the thermal comfort assessment in densely built urban agglomeration regions in Pakistan. A systematic review analysis method was employed to investigate the effects of particulate matter (PM2.5), carbon oxides (COx), nitrogen oxides (NOx), sulfur oxides (SOx), and volatile organic compounds (VOCs) on residents’ health. The Preferred Reporting Items for Systematic Reviews and Meta Analyses (PRISMA) protocol guided the identification of key terms and the extraction of cited studies. The literature review incorporated a combination of descriptive research methods to inform the research context regarding both ambient and indoor air quality, providing a theoretical and methodological framework for understanding air pollution and its mitigation in various global contexts. The study found a marginally significant relationship between the PM2.5 operative air temperature and occupants’ overall temperature satisfaction, Ordinal Regression (OR) = 0.958 (95%—Confidence Interval (CI) [0.918, 1.000]), p = 0.050, Nagelkerke − Regression (R2) = 0.042. The study contributes to research on the development of an evidence-based thermal comfort assessment benchmark criteria for the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) Global Thermal Comfort Database version 2.1.

Observational study of close contact behaviors in a multinational graduate student office

Understanding human contact behavior plays a crucial role in improving the design of indoor environments, for example, by enhancing thermal comfort, workplace performance and productivity, and controlling infection transmission. Although baseline information for indoor environmental design is available, data on person-to-person contact behavior are limited, especially in populations of diverse nationalities, genders, and age ranges. In this study, we used high-resolution video cameras to collect close contact data for five consecutive school days in a multinational graduate school student office in Japan with a reserved cultural background. We characterized human close-contact behavior using stochastic interactions in terms of interpersonal distance, assortativity, posture, duration, and frequency of contact in a multinational population. Our results showed a remarkable difference compared to the study results from person-to-person contact behavior in an ethnically homogenous population. The interpersonal distance averaged 0.75 m, and the close contact frequency was notably lower, but the close contact duration was significantly longer. Nationality showed stronger homophily. We considered the degree centrality measures of the nodes in the observed networks. The observed contact network links were dense with high reciprocity. High contact heterogeneity was observed in twenty-five percent of the nodes. These data can be used to improve targeted epidemiological analyses for indoor occupant health decision-making, and infection spread prevention strategies.

Evaluating annual thermal discomfort time ratio of indoor occupants caused by solar radiation using a novel model

Thermal comfort of indoor occupants exposed to solar radiation is receiving widespread attention. Researchers have proposed many models to predict solar radiation and related indexes are used to evaluate thermal comfort. However, there are some limitations in the existing solar radiation models and evaluation indexes, such as only applying to sunny weather and requiring intensive modeling work. This study adopts a mathematical model called the improved HNU Solar Model and proposes a new evaluation index called the annual thermal discomfort time ratio by solar radiation (ratiotd, solar) to evaluate thermal comfort of indoor occupants exposed to solar radiation. The effects of different window parameters, i.e. window direction, window transmittance (Tsol) and window-to-wall ratio (WWR) on ratiotd, solar were also analyzed. The results show that the indoor area less than 2.0 m away from the window is easy to have solar discomfort. And for every 0.1 reduction in WWR, the average values of the four directions are reduced by 3% to 4%; and for every 0.1 reduction in Tsol, the ratiotd, solar values of four window directions are reduced by 4% to 6%. This study provides references for evaluating and optimizing the window design to create thermally comfortable environments for indoor occupants.

Impact of ventilation system retrofitting on indoor air quality in a single-family building

Due to prolonged human occupancy within indoor environments, the quality of indoor air holds significant importance. This study evaluates the impact of retrofitting a ventilation system from natural to mechanical on indoor air quality (IAQ) in a bedroom during heating seasons in 2017/18 and 2020/21. Measurements were conducted before and after modernization to assess CO2 concentrations, temperature, humidity, particulate matter (PM) and bacterial and fungal aerosols levels indoors and outdoors. Results show a significant decrease in CO2 concentration (from 1000 ± 240 ppm to 806 ± 165 ppm) and relative humidity (from 53.0 ± 3.6 % to 28.2 ± 4.1 %) post-modernization. Notable reduction in CO2 concentrations averaging 20 % was recorded, thereby elevating air quality category from IDAIII to IDAII. Additionally, the study determined the infiltration coefficient for PM2.5, which increased from 0.48 under natural ventilation to 0.63 after retrofitting. PM2.5 concentrations increased indoors (from 23.49 ± 11.21 μg/m3 to 56.68 ± 13.47 μg/m3) due to higher infiltration rates with mechanical ventilation. Bioaerosol measurements indicate fluctuating concentrations, with bacterial aerosols peaking in the evening. The use of mechanical ventilation led to increased transport and dispersion of bacterial bioaerosols indoors. In contrast, this was not observed for fungal aerosols. Additionally, the study highlights limitations in ventilation filtration efficiency and recommends the installation of PM2.5 sensors to regulate airflow during periods of high outdoor PM2.5 concentrations. Overall, while mechanical ventilation improved some IAQ parameters, it posed challenges in controlling particulate matter infiltration, emphasizing the need for comprehensive ventilation strategies to ensure indoor air quality and occupants' well-being.

Comparative Assessment of Insulation Materials for Improving Indoor Air Quality in Building Retrofit

This paper evaluates the impacts of different insulation materials on Indoor Air Quality (IAQ) and occupant health with a focus on the Volatile Organic Compound (VOC) emissions. The main aim is to identify options that minimize exposure rates while improving IAQ and energy in retrofitted buildings. A comprehensive literature review was conducted synthesizing scholarly articles, guidelines from international organizations, and information on pollutants, IAQ standards, and retrofit strategies. The findings show high emission rates for some insulation materials that could negatively affect health. Hemp insulation in contrast was identified as a promising solution exhibiting low VOC emissions compared to other insulation materials. As sustainable construction practices advance, hemp insulation emerges as a viable retrofit strategy for social housing by synergistically addressing performance gaps related to energy conservation, air quality, and thermal comfort. The synthesis of evidence from this paper suggests that, from environmental and public health perspectives, certain insulation materials are preferable for improving IAQ and reducing the risk of exposure to indoor air pollutants in retrofitted buildings.

Numerical simulation and safety assessment of fires in historic timber structures based on fire load investigation

Historic timber structures face substantial fire loads and complex fire risks. Subsequent renovations and utilization may influence their fire safety performance. Therefore, accurately predicting indoor fire development in historic buildings and assessing their fire safety performance is crucial. Numerical fire simulation is currently at the forefront of analyzing and assessing fire risks in historic buildings. However, there is a shortage of globally accessible historic building fire data. This paper proposes a method to determine fire scenarios, peak heat release rates, and development curves of indoor fires in wooden historic buildings through a fire load investigation. Using the Guangzhou ancestral hall as an example, PyroSim fire dynamics simulation software is employed to calculate fire development and assess the available safe evacuation time. The simulation results are subsequently input into the Pathfinder evacuation simulation software to ascertain the required safe evacuation time for indoor occupants. A comparative assessment is conducted to evaluate the fire safety performance before and after the renovation of historic buildings. The research findings indicate that installing closed glass curtain walls in the courtyards of ancestral hall buildings in Guangzhou accelerates the infiltration of smoke during fires, leading to rapid fire spread and long-distance ignition, significantly reducing the time available for safe evacuation. Therefore, when renovating and utilizing the ancestral hall buildings in Guangzhou, the installation of ventilation and smoke extraction systems should be prioritized to slow down fire development. Additionally, controlling the number of indoor occupants is an effective management measure to mitigate fire damage in historic buildings.

From perception to prediction: A comparative study of thermal comfort assessment techniques in school facilities

Indoor environmental quality (IEQ) significantly affects learning, health, and productivity in educational buildings, particularly classrooms. Thermal comfort is crucial to IEQ in classrooms, and various approaches have been developed to assess it. This study compares subjective evaluation, fieldwork measurement, and simulation methods in naturally ventilated secondary school classroom environments in Abuja, Nigeria. It evaluates their reliability, validity, advantages, and limitations. In achieving this objective, fieldwork measurements, questionnaire surveys, and building simulations were conducted in 83 classrooms with 2758 respondents. The data were subjected to descriptive and inferential statistical analysis using the analytical software programs SPSS version 23 and Microsoft Excel. The study found that the Actual Mean Vote (AMV) comfort operative temperature for classrooms was 28.7 °C. Findings from the comparative analysis of the methods indicate that the PMV generally overestimates students’ thermal sensation and that the fieldwork measurement Predicted Mean Votes (PMVf) undervalues the neutral temperature by 4.2 °C. In contrast, the computer-simulated Predicted Mean Votes (PMVs) undervalues it by 3.9 °C. The study found that all three methods are valid and reliable, with some advantages and limitations, depending on the assessment’s specific context and goals. The findings provide valuable insights into improving indoor environmental quality and occupant satisfaction in educational buildings, contributing to the discourse on assessing thermal comfort in classrooms and informing the development of more effective assessment methods. The systematic comparison of these methods addresses a notable gap in the literature, providing practical insights to optimize thermal conditions in learning environments.

The parameter of the Sick Building Syndrome: A systematic literature review

Sick Building Syndrome (SBS) is a collection of symptoms assumed to be related to spending time in a certain building, most typically a workplace, but no specific cause has been identified. The need to measure and assess various types of parameters of SBS is crucial and it is important to explore what parameter has been used in the previous studies of SBS. Therefore, this study aims to systematically review the parameter that has been used to monitor the SBS. This study was conducted using the PRISMA Statement and the search was conducted using two scientific databases which were Scopus and Web of Science. After a thorough and tight process, a total of 55 articles have been finalized and selected for thematic analysis. Two themes have been identified which were a) Indoor Environmental Quality (IEQ) and b) Occupant. This study also found that the spatial distribution pattern revealed that the Sick Building Syndrome research was spread over 26 nations, with the majority of articles originating from the United States and China. In terms of context, the majority of the selected publications employed the survey approach to investigate SBS parameters. Aside from that, the most researched form of building is the business building. This study has found that it would be more impactful for the SBS study if researchers could incorporate both indoor environmental quality and occupant factors into a study, resulting in more holistic conclusions.

A novel rule mining method for knowledge discovery of relationships among indoor air quality, HVAC operation and occupants’ activities

This paper presents a novel data mining method to discover the occurrence time, co-occurrence and sequential association rules among indoor air quality (IAQ), Heating, Ventilation and Air Conditioning (HVAC) operation and occupants' activities. The novelty of this work includes a peak detection method to discover the pollutants' peaks and a rule mining method with extended rule selection criteria that considers both the time lags between events and relationships among multiple events. The peak detection method aimed to use moving windows and criteria to find the candidate peaks and the non-maximum suppression to filter the overlapped candidate peaks. The proposed rule mining method is based on the Apriori and Apriori-All with the rule selection criteria extended by labeling the occurrence time to events to find the rules among various occurrence times and multiple events and by limiting the time lags among the occurrences of the events to discover the sequential rules. Two datasets, one from 70 houses and one from a single commercial building, were used to evaluate the performance of this proposed data-driven method. The results showed that 93.19 % of the peak detection results matched the peaks detected from the data records. The CO2 peaked frequently at 12:00, 17:00 and from 19:00 to 24:00. The occurrence of occupants' activities could lead to pollutants’ concentrations and pollutants peaks within 2 hours in the residential buildings. The occurrence of a high Wifi connection number was frequently followed by a CO2 peak within 2 hours in the commercial building. These findings can support the development of control strategies for building HVAC systems to achieve better indoor air quality control.

Multimodal Framework for Smart Building Occupancy Detection

Over the years, building appliances have become the major energy consumers to improve indoor air quality and occupants’ lifestyles. The primary energy usage in building sectors, particularly lighting, Heating, Ventilation, and Air conditioning (HVAC) equipment, is expected to double in the upcoming years due to inappropriate control operation activities. Recently, several researchers have provided an automated solution to turn HVAC and lighting on when the space is being occupied and off when the space becomes vacant. Previous studies indicate a lack of publicly accessible datasets for environmental sensing and suggest developing holistic models that detect buildings’ occupancy. Additionally, the reliability of their solutions tends to decrease as the occupancy grows in a building. Therefore, this study proposed a machine learning-based framework for smart building occupancy detection that considered the lighting parameter in addition to the HVAC parameter used in the existing studies. We employed a parametric classifier to ensure a strong correlation between the predicting parameters and the occupancy prediction model. This study uses a machine learning model that combines direct and environmental sensing techniques to obtain high-quality training data. The analysis of the experimental results shows high accuracy, precision, recall, and F1-score of the applied RF model (0.86, 0.99, 1.0, and 0.88 respectively) for occupancy prediction and substantial energy saving.

Energy-efficiency oriented occupancy space optimization in buildings: A data-driven approach based on multi-sensor fusion considering behavior-environment integration

Buildings contribute significantly to global energy consumption. Optimizing internal building space layout is an essential approach for reducing energy consumption. However, proactively improving energy efficiency by building space design is still challenging requiring comprehensive consideration of complex interactions between indoor environment and occupant behavior, which is less studied previously. Considering behavior-environment integration, this study proposes a data-driven approach based on multi-sensor fusion for energy-efficiency oriented occupancy space optimization in buildings. Firstly, time series data including indoor environment and occupant behavior were collected based on multi-sensor fusion. Then, a data-integrated Convolutional Neural Network (CNN) model was developed for occupancy state classification. Based on obtained occupant schedules, space occupancy patterns of users were extracted using hierarchical clustering, and space optimization was further conducted for energy efficiency improvement. Finally, energy consumption was predicted with random forest regression after space optimization, and the impact of occupancy space optimization on energy efficiency can be evaluated. The proposed method was successfully applied in an academic office building on a campus in Wuhan, China, which helped achieve energy consumption reduction by 23.5 %. This study presents a promising path towards sustainable energy goals in building design, which serves as advanced guidance in the management of building energy performance.

Energy consumption and thermal comfort assessment using CFD in a naturally ventilated indoor environment under different ventilations

The rising energy demands of modern buildings necessitate the proper balance between occupant comfort and energy efficiency. This study numerically evaluates the effect of different inlet and exhaust vent profiles on indoor IEQ and energy consumption in well-validated, occupied office buildings for a cold climate conditions. Using ANSYS products, the research evaluates the impact of different ventilation configurations on occupant comfort using different comfort parameters such as, PMV, PPD, air temperature, temperature gradient, fluid flow velocity. The study also analyze how different ventilation configurations affect the thermal load on the radiator for indoor thermal comfort. The findings reveal a significant effect of ventilation profiles on both IEQ and energy consumption. Inadequate ventilation configuration selection can lead to a significant increase in radiator heat load, potentially increase up to 117.3%. Conversely, the adoption of an optimized ventilation strategy can simultaneously reduce energy consumption and improve indoor occupant thermal comfort. Indoor occupant comfort with minimal radiator heat load can be achieved using vertically oriented, rectangular inlet and exhaust vents with a perimeter ratio of 0.611. This research contributes to the development of sustainable buildings and describes indoor comfort design for cold climate conditions.

Assessing the post-COVID interaction between indoor environmental quality and occupants within educational buildings: A structural equation modeling approach

It is well-acknowledged that indoor environmental quality (IEQ) affects different aspects of occupants' lives, such as satisfaction, performance, and well-being. This acknowledgement is a result of extensive studies investigating IEQ interaction with occupants within different spaces, such as offices, commercial buildings, and homes. What is still missing are two aspects that IEQ studies should consider: (a) the post-COVID-19 era and the resulting anti-measures (COVID measures) and (b) the interaction of IEQ with occupants in academic libraries. This study aims to analyze the effects of COVID measures on the relationship between IEQ and occupants' satisfaction (learning performance), as well as their visiting habits at one of the largest academic libraries in Asia. To this end, two scenarios are developed to compare the effects of IEQ on occupants with and without COVID measures. This paper aims to address how COVID measures will shape the relationship between the IEQ of an academic library and its users’ satisfaction and visiting habits. This relationship is investigated using structural equation modeling based on 509 face-to-face surveys completed over the course of 4 months. Results suggest that COVID measures will produce a significant relationship between IEQ and satisfaction; however, higher satisfaction is reported when COVID measures are not applied. Both models show no significant relationship between IEQ and visiting habits. The proposed model will help in defining construction guidelines for libraries and educational buildings that promote IEQ conditions in the post-COVID era.

A novel method for calculating solar radiation on indoor human body under different weather conditions

Indoor occupants may receive large solar heat that affect their health, productivity, and comfort. Most of previous studies focus on indoor occupants with strong direct solar radiation and weak diffuse radiation. Considering the non-uniform solar radiation distribution in the sky, this study proposes the improved HNU Solar Model, a novel method for calculating solar radiation on indoor human body under different weather conditions. The improved HNU Solar Model proposes a new calculation algorithm for calculating non-uniform radiation distributed on indoor human body under different weather conditions, via adding the human body points, windows points, and clearness index to improve the calculation accuracy; then it converts the human-body-received solar radiation into the increase in the mean radiant temperature (Delta MRT). The calculated Delta MRT values by the improved HNU Solar Model were compared with those calculated by Radiance. The results indicate that, for Delta MRT by total solar radiation on typical days with different weather conditions, the absolute error (AE) and the relative error (RE) of the improved HNU Solar Model are usually less than 1.5 °C and 15%, respectively. For the whole-year hourly Delta MRT values, the corresponding AE and RE are usually less than 1 °C and 15%, respectively. Moreover, according to the proposed model, for large-window buildings in medium-latitude regions, indoor occupants are more exposed to direct solar radiation (63%) than diffuse solar radiation (37%); while diffuse radiation can be the dominant solar heat gain of indoor occupants (up to 55%) in buildings in low-latitude regions.