Indoor Environmental Conditions Research Articles

Occupancy Prediction in IoT-Enabled Smart Buildings: Technologies, Methods, and Future Directions

In today’s world, a significant amount of global energy is used in buildings. Unfortunately, a lot of this energy is wasted, because electrical appliances are not used properly or efficiently. One way to reduce this waste is by detecting, learning, and predicting when people are present in buildings. To do this, buildings need to become “smart” and “cognitive” and use modern technologies to sense when and how people are occupying the buildings. By leveraging this information, buildings can make smart decisions based on recently developed methods. In this paper, we provide a comprehensive overview of recent advancements in Internet of Things (IoT) technologies that have been designed and used for the monitoring of indoor environmental conditions within buildings. Using these technologies is crucial to gathering data about the indoor environment and determining the number and presence of occupants. Furthermore, this paper critically examines both the strengths and limitations of each technology in predicting occupant behavior. In addition, it explores different methods for processing these data and making future occupancy predictions. Moreover, we highlight some challenges, such as determining the optimal number and location of sensors and radars, and provide a detailed explanation and insights into these challenges. Furthermore, the paper explores possible future directions, including the security of occupants’ data and the promotion of energy-efficient practices such as localizing occupants and monitoring their activities within a building. With respect to other survey works on similar topics, our work aims to both cover recent sensory approaches and review methods used in the literature for estimating occupancy.

A living lab to develop smart home services for the residential welfare of older adults

Owing to the decline in the physical and social functions of older adults, the residential environment substantially impacts the psychological and emotional stability and life satisfaction for older adults. Recently, there has been a growing interest in providing residential welfare services related to the health and independent living of the older adults. The primary objective of this research was to identify the needs and perceptions of older adults regarding smart home services and associated sensors and evaluate the indoor environment conditions of older adults through a living lab. Survey results indicate that older adults felt the burden of electricity bills in summer and winter and had relatively high safety-related smart home service needs. Focus group interviews with the participants in the living lab confirmed that older adults positively perceived the benefits and privacy of the living-lab sensors. In addition, the analysis of indoor environment data over one year from a case-study household showed that approximately 66.4% during the winter season and 13.1% during the summer season have fallen within the neutral thermal comfort range according to the proposed approach (i.e., predicted mean vote (PMV) calculated using heart rate). This study adopted the proposed PMV method (hereafter PMV-P) that takes into account personal factors (e.g., metabolic rate of older adults that can be calculated using heart rate) and environmental factors (e.g., temperature, humidity) by using the real-time data collected from IoT-based sensors. Furthermore, this study proposed a new home energy management system (EMS) algorithm on the basis of model predictive control for reducing heating energy consumption while satisfying the thermal comfort. The new EMS was demonstrated to achieve 5 – 12% heating energy savings depending on the thermal comfort preferences of occupants. Overall, the results of multidisciplinary research not only provided the preliminary evidence regarding the needs and perceptions of older adults toward IoT-based sensors but also showcased the development of IoT-based smart home service system through the living lab.

In situ physiological and behavioural responses of vulnerable populations during an extreme heat event in Montreal, Quebec 2023: a pilot study

Introduction: Extreme heat events (EHE) continue to increase in frequency, intensity, and duration, posing a significant health threat to vulnerable populations. Despite the development of heat-health action plans, heat-related injuries during EHE persist with the majority occurring at-home. Although epidemiological evidence has identified vulnerable populations, limited evidence exists to confirm their physiological and behavioural responses within the natural environment. Objective: The objective was to undertake a pilot study to monitor heat-vulnerable populations before and during an EHE and determine whether differences in physiological strain, subjective sleep quality, thermal perception, and thirst, would be detected during the EHE. Methods: Five participants who identified as a member of at least one heat-vulnerable group (67±5 y, 84.3±18.0 kg) participated in this study which ran throughout the summer months in Montreal, Quebec 2023. Using HeatSuite, a multipoint remote data acquisition platform that captures indoor environmental conditions, participants continuously wore a fitness tracker, completed a daily sleep quality assessment and a short questionnaire 4 times a day to quantify their thermal sensation, thermal comfort, and thirst, and recorded their oral temperature, heart rate, and blood pressure at rest 3 times a day (morning, midday, and evening). An EHE, as defined by Environment Canada, occurred Sept. 5th – 7th (max ambient temperature: 30.6±0.6°C; max humidex: 40.7±0.6), and thus the physiological and behavioural data collected are compared to the 3 preceding days (Sept. 2nd – 4th, PRE; max ambient temperature 27.2±2.3°C; max humidex: 34.0±4.6). Results: While all participants had air-conditioning, mean indoor ambient temperature was slightly higher during the EHE (22.5±0.5°C) than PRE (21.4±0.6°C, P=0.03). Total step count increased during the EHE relative to PRE in 4 of 5 participants (+47±83%). Mean oral temperature was higher during the EHE (37.3±1.0°C) compared to PRE (36.7±0.7°C, p<0.05). Mean heart rate was not different during the EHE (82±8 BPM) relative to PRE (81±7 BPM, P=0.32). Mean blood pressure was not different during PRE compared to during the EHE (P>0.38). Subjective sleep quality was lower during the EHE compared to PRE (p<0.05). Participants reported no difference in thermal discomfort (P=0.12) and sensation (P=0.38), however they reported an increased thirst sensation (P=0.03) during the EHE. Conclusion: This pilot study was successful at monitoring physiological and behavioural responses in situ of heat-vulnerable individuals during an EHE. The results suggest that the EHE reduced sleep quality, increased thirst and thermal sensations, resulted in a higher oral temperature, and that participants were more active during the EHE relative to the 3 preceding days. This research is supported by Dr. Ravanelli’s New Frontiers in Research Fund (NFRFR-2021-00247). This is the full abstract presented at the American Physiology Summit 2024 meeting and is only available in HTML format. There are no additional versions or additional content available for this abstract. Physiology was not involved in the peer review process.

Indoor Parameters of Museum Buildings for Guaranteeing Artworks Preservation and People’s Comfort: Compatibilities, Constraints, and Suggestions

Since people tend to spend more and more time visiting museums, more accurate requirements are needed for the indoor environmental conditions of these confined spaces where two primary requisites coincide in defining their optimal indoor microclimate: the need for the appropriate artwork preservation and suitable levels of indoor comfort conditions for people visiting the exhibition buildings and/or working there. Regrettably, people and artwork requirements are sometimes characterized by different reference limits of the environmental parameters that, not rarely, could potentially conflict. Another important point to consider is that museums hosted by heritage buildings (particularly in Mediterranean climates, as is often the case in Italy) are often not equipped with climatization systems because of difficulty in installing generally bulky equipment such as HVAC systems. This circumstance represents another important limit for achieving suitable conditions for the two requisites. In addition, the recent pandemic-related occurrences are pushing technicians and designers to rethink the criteria for controlling the microclimate of public buildings, and museums among them. In this paper, this issue is addressed by reviewing current regulations, standards, and handbooks (and by means of a real case example related to the Italian context) in order to ascertain whether such documentation could facilitate the development of effective rules/guidelines for proper management of indoor parameters in museums.

Indoor environmental quality in schools: NOTECH solution vs. standard solution.

In many Danish schools, the indoor environmental quality (IEQ) is challenged and studies document a poor IEQ in a majority of existing schools. Municipalities cannot afford comprehensive renovations and expensive mechanical ventilation solutions, hence public schools often suffer from poor indoor environment conditions. This study tests a new façade based, demand-controlled ventilation solution called NOTECH in the renovation of school. The study tests NOTECH vs. existing mechanical ventilation solution, comparing performance of both solutions at Skovbrynet Skole in Denmark. The project investigates the effect of the NOTECH solution in a primary school classroom, comparing it to a similar classroom with conventional, mechanical ventilation. Methodically, indoor environmental quality and energy performance is monitored in the two identical classrooms during one school year 2018 - 2019. The results show that both systems keep the conditions within acceptable limits and CO2 levels below 1000 ppm, which is the requirement according to the Danish Building Regulations. In terms of costs, the NOTECH system has a lower overall cost than the mechanical ventilation system, with total estimated costs for installation, heating, electricity and maintenance amounting to approximately 35% of the mechanical system's costs. Finally, the results show that the NOTECH solution has a smaller embedded CO2 footprint for building materials, reducing the estimated carbon load by 95% compared to the mechanical ventilation solution. While the performance of the both systems complies to the Danish Building Regulations, the indoor environmental quality between systems differs significantly. Results showing a higher air-temperature and lower relative air-humidity in the classroom with mechanical ventilation during winter and lower CO 2 levels in the mechanically ventilated classroom during winter and summer. Costs for implementation, energy consumption for heating and CO 2 footprint for building materials are significantly lower for the NOTECH solution, compared to the mechanical solution.

State-of-the-Art Low-Cost Air Quality Sensors, Assemblies, Calibration and Evaluation for Respiration-Associated Diseases: A Systematic Review

Indoor air quality and respiratory health have always been an area of prime interest across the globe. The significance of low-cost air quality sensing and indoor public health practices spiked during the pandemic when indoor air pollution became a threat to living beings, especially human beings. Problem Definition: Indoor respiration-associated diseases are hard to diagnose if they are due to indoor environmental conditions. A major challenge was observed in establishing a baseline between indoor air quality sensors and associated respiratory diseases. Methods: In this work, 10,000+ articles from top literature databases were reviewed using six bibliometric analysis methods (Lorenz Curve of Citations, Hirch’s H-Index, Kosmulski’s H2-Index, Harzing’s Hl-Norm-Index, Sidoropolous’s HC-Index, and Schrieber’s HM-index) to formulate indoor air quality sensor and disease correlation publication rubrics to critically review 482 articles. Results: A set of 152 articles was found based on systematic review parameters in six bibliometric indices for publications that used WHO, NIH, US EPA, CDC, and FDA-defined principles. Five major respiratory diseases were found to be causing major death toll (up to 32%) due to five key pollutants, measured by 30+ low-cost sensors and further optimized by seven calibration systems for seven practical parameters tailored to respiratory disease baselines evaluated through 10 cost parameters. Impact: This review was conducted to assist end-users, public health facilities, state agencies, researchers, scientists, and air quality protection agencies.

A comprehensive review of Building Energy Management Systems (BEMS) for Improved Efficiency

Building Energy Management Systems (BEMS) play a crucial role in enhancing energy efficiency and sustainability in buildings. This abstract provides a comprehensive review of BEMS, focusing on its components, benefits, challenges, and future trends. BEMS is a centralized system that monitors and controls building services, such as heating, ventilation, air conditioning, lighting, and other systems, to improve energy efficiency and occupant comfort. The key components of BEMS include sensors, controllers, communication networks, and user interfaces. These components work together to collect data on building performance, analyze energy consumption patterns, and optimize system operation. One of the primary benefits of BEMS is its ability to reduce energy consumption and costs. By monitoring and controlling building systems based on occupancy schedules and environmental conditions, BEMS can significantly reduce energy waste. Additionally, BEMS can improve occupant comfort and productivity by maintaining optimal indoor environmental conditions. Despite its benefits, BEMS faces several challenges, including high upfront costs, complexity of installation, and integration issues with existing building systems. However, advancements in technology, such as the Internet of Things (IoT) and cloud computing, are addressing these challenges and making BEMS more accessible and cost-effective. Looking ahead, the future of BEMS is promising, with continued advancements in technology driving its adoption and integration into smart building systems. The integration of artificial intelligence and machine learning algorithms into BEMS is expected to further improve its energy-saving capabilities and enhance building performance. In conclusion, BEMS is a key technology for improving energy efficiency and sustainability in buildings. By leveraging its components and capabilities, BEMS can help reduce energy consumption, lower costs, and create healthier and more comfortable indoor environments.

Exploring the influence of indoor environment and spatial layout on changed behaviours of people with dementia in a nursing home

People with dementia sometimes show changed behaviours such as agitation, hallucination, and wandering during the moderate and severe dementia stages. In addition to individual health factors, contextual factors, such as indoor environment conditions, spatial layout, and human activities, may trigger or influence these behaviours, but there is a lack of solid evidence. We used mixed methods to collect data, including the fly-on-the-wall method to observe the residents' daily lives and deploying environmental sensors to monitor the indoor environments of two central living rooms and ten bedrooms in a nursing home in the Netherlands. A data collection campaign from August to September 2022 focused on the indoor environmental parameters, ventilation control of monitored rooms, the observation of ten participants' locations, activities, clothing levels, and changed behaviours. The data were analysed using Fisher's exact tests and heatmap analysis. The results show that even though the nursing home was well maintained according to existing indoor environmental quality standards, the room conditions of temperature, TVOC, and HCHO levels and contextual factors (main activity and numbers of people in the space) were significantly correlated with locations of changed behaviours. By analysing observation data with spatial layout, participants had larger activity ranges on the days that exhibited changed behaviours than those without. Most of these behaviours were observed at the edge of common spaces, where caregivers need to pay more attention.

Seasonal field study on thermal comfort in university classrooms in Mediterranean climate

The assessment of indoor environmental conditions of educational buildings is not only essential to ensure the correct performance of heating, cooling and ventilation systems but it is also fundamental to guarantee a suitable environment. This study aims to analyse the thermal comfort of students in teaching–learning spaces in university buildings with natural ventilation. A field measurement campaign and a questionnaire survey were carried out from September 2021 to June 2022 in educational buildings in southern Spain. The collected data were analysed and the neutral temperature in each season was obtained, based on the thermal sensation votes of 1966. The neutral temperatures found in this study were 23.5°C, 23.1°C, 23.3°C and 23.9°C, for autumn, winter, spring and summer, respectively. The ranges obtained for 90% acceptability in the winter season (21.1°C–25.1°C) provided lower temperature limits than the ranges obtained in the summer months (22.6°C–25.3°C). The highest values for clothing insulation were found in the autumn (0.90 clo) and winter (0.75 clo) seasons, compared to the spring (0.5 clo) and summer (0.4 clo) seasons. An adaptive thermal comfort model was applied. These findings could be used to improve thermal comfort and to optimise energy consumption according to the students’ actual thermal perception.

Perceived Air Quality, Thermal Comfort and Health: A Survey of Social Housing Residents in Kazakhstan

Kazakhstan is in Central Asia and is the ninth-largest country in the world. Some socially vulnerable segments of the Kazakh population residing in subsidised social housing have experienced a range of problems due to the low quality of housing construction and its planning. Poor indoor environmental conditions in social housing contribute to occupants’ comfort, health, and general well-being. This study assessed social housing residents’ health and quality of life, focusing on their perceived indoor air quality and thermal comfort satisfaction. A cross-sectional survey in Kazakhstan was conducted to test the effects of environmental factors on social housing residents’ health and satisfaction. Four hundred thirty-one responses were analysed, and the SF12v2 questionnaire was used to measure the health-related quality of life. Multiple regression analysis showed that air quality negatively predicted the respondents’ physical (PCS) and mental (MCS) health. In addition, age, smoking, and employment status had a significantly negative effect on PCS, while education level had a predictive positive effect. Thermal conditions negatively predicted only MCS, as well as alcohol consumption. Next, the air-conditioning control factor had a negative effect. In contrast, low air circulation, low humidity, high solar gain, temperature imbalance, duration of the residence and alcohol consumption had a significantly positive effect on overall satisfaction with the temperature. The odour sources from tobacco, furniture and external sources were predictors of respondents’ overall air quality satisfaction, along with the duration of the residence, alcohol consumption and smoking status.

Evaluation of the Effects of Window Films on the Indoor Environment and Air-Conditioning Electricity Consumption of Buildings

The objective of this study was to evaluate the effects of window films on indoor environmental conditions and electricity consumption of air conditioning. The research focused on the performance of different window films (HAG, RG), taking into account variations from different building orientations. The findings of this research indicated that building orientation could significantly influence the duration of direct sunlight entering the interior, with the areas closer to the glass being more susceptible to the effects of outdoor temperature and solar radiation. The clear glass with heat-absorbing film (HAG) and reflective film (RG) both reduced the indoor temperature and indoor illuminance while increasing indoor comfort. The RG could accumulate less heat on the glass surface compared with the HAG. The glass temperature of the RG will be lower than the HAG. The electricity-saving ratios of the HAG were 1.4%, 1.9%, 1.4%, and 1.2%, respectively, when facing the east, south, west, and northwest orientations compared with the clear glass (OG). The electricity-saving ratios of the RG were 3%, 4.2%, 4.2%, and 10.3%, respectively.

Design and Evaluation of Wireless DYU Air Box for Environment-Monitoring IoT System on Da-Yeh University Campus

This paper presents an original wireless DYU Air Box of an environment-monitoring IoT (EMIoT) system on a campus to offer information on environmental conditions through the public ThingSpeak IoT platform for stakeholders including all the students and employees on the Da-Yeh University (DYU) campus in Taiwan. Firstly, the proposed wireless heterogeneous multi-sensor module aggregates BME680, SCD30, PMS7003, and BH1750 sensors with a TTGO ESP32 Wi-Fi device based on the I2C and UART interface standards of series communication. Through the DYU-802.1X Wi-Fi network with the WPA2 Enterprise security directly, the wireless multi-sensor monitoring module further forwards the observation data of environmental conditions on campus via the DYU-802.1X Wi-Fi network to the public ThingSpeak IoT platform, which is a cloud service platform to aggregate, visualize, and analyze live sensing data of air quality index (AQI), concentrations of PM1.0/2.5 and CO2, brightness, ambient temperature, and relative humidity (RH). The results illustrate the proposed DYU Air Box for monitoring the indoor environmental conditions on campus and validate them with sufficient accuracy and confidence with commercialized measurement instruments. In this work, the wireless smart environment-monitoring IoT system features monitoring and automatic alarm functions for monitoring AQI, CO2, and PM concentrations, as well as ambient illumination, temperature, and RH parameters and collaboration and interoperability through the Enterprise Intranet. All the organizational stakeholders interested in the environmental conditions of the DYU campus can openly access the information according to their interests. In the upcoming future, the information of the environmental conditions in the DYU campus will be developed to be simultaneously accessed by all the stakeholders through both the public ThingSpeak IoT platform and the private EMIoT system.

Environmental causality calibration: Advancing WLAN RF fingerprinting for precise indoor localization.

In recent years, considerable and valuable research progress has been made in indoor positioning technologies based on WLAN Radio Frequency (RF) fingerprinting, identifying it as one of the most promising positioning technologies with substantial potential for wider adoption. However, indoor environmental factors significantly influence the propagation of wireless RF signals, resulting in a considerable decrease in positioning accuracy as the indoor environmental conditions vary. Thus, effectively mitigating the impact of indoor environmental factors on WLAN RF fingerprinting-based positioning systems has become a crucial research problem. Currently, there is a dearth of comprehensive research on the influence of indoor climatic factors, particularly the variations in relative humidity, on the propagation of WLAN RF signals within indoor spaces and its consequential impact on positioning accuracy. To address the aforementioned issues, this paper proposes an Adaptive expansion fingerprint database (AeFd) model based on a regression learning algorithm. The AeFd, through the design of a relationship model describing the interaction between fingerprint databases under varying relative humidity, allows the fingerprint database expanded by AeFd to dynamically adapt to the changes in indoor relative humidity. Our experiments show that using the AeFd model with the KNN algorithm, a 5% performance improvement was observed over 10 days and an 8% improvement over 10 months. According to experimental test results, the fingerprint database expansion model AeFd proposed in this paper can effectively expand the fingerprint database under different relative humidity levels, thereby significantly enhancing the positioning performance of the system and improving its stability.

Indoor pollution control based on surrogate model for residential buildings

Individuals typically spend most of their lives indoors, predominantly in spaces like offices and residences. Consequently, prolonged indoor exposure underscores the critical significance of maintaining optimal indoor air quality (IAQ) to safeguard one's health. The primary impediment to attaining efficient regulation of Indoor Air Quality (IAQ) is the challenge of monitoring the IAQ parameters, particularly within the immediate vicinity of an individual's breathing space. Current heating, ventilation, and air conditioning systems lack the ability to rapidly predict and optimize the quality of indoor air. The objective of this study is to acquire the distribution features of indoor pollutants and precise indoor environment data in order to efficiently forecast and enhance the IAQ. To achieve this objective, a proposed surrogate model was developed using computational fluid dynamics (CFD). Notably, the Kriging surrogate model can rapidly predict IAQ while using a limited number of CFD runs. CFD are widely used as numerical simulation methods to obtain the accurate information. Surrogate models can rapidly forecast indoor environmental conditions using CFD simulation data simultaneously. Optimization algorithms can efficiently achieve desirable indoor ambient conditions, offering highly effective and intelligent control techniques for indoor atmospheric ventilation.

A BIM-enabled digital twin framework for real-time indoor environment monitoring and visualization by integrating autonomous robotics, LiDAR-based 3D mobile mapping, IoT sensing, and indoor positioning technologies

Existing indoor environment monitoring methods often fall short due to the lack of using standardized evaluation methods, missing spatial information, or the absence of user-friendly visualization interfaces. To address these limitations, this paper proposes a novel digital twin framework for real-time indoor environment monitoring and visualization by integrating Building Information Modeling (BIM), Internet of Things, autonomous robot-based mobile mapping and sensing, and indoor positioning technologies. The goal is to autonomously monitor indoor environmental conditions and provide real-time visualization using a standardized assessment index with estimated spatial information in BIM software. The proposed framework was demonstrated and validated in two indoor scenarios for air quality monitoring and visualization-related applications. Implementation results revealed that: the framework effectively captured spatial characteristics of air quality in small and medium-sized indoor spaces and intuitively visualized it using a BIM digital twin model; and, the ultra-wideband-based indoor positioning system achieved a nearly centimeter-level positioning precision. Practical interpretations of the results demonstrated that the proposed framework can not only detect and localize the potential sources of air pollutants but can also identify the possible causes of low indoor air quality. This study ultimately contributes to the body of knowledge by offering a novel and generic digital twin framework that seamlessly bridges various popular yet previously isolated technologies for better informing building facility management-related decisions. This paper is the first research work that integrates several emerging technologies (i.e., BIM, digital twins, IoT sensing, indoor localization/positioning/tracking, and robotics) for advancing real-time monitoring and visualization of indoor environmental conditions. While the proposed framework was applied for indoor air quality monitoring, it could be extended and used for various indoor environmental assessment and visualization-related applications.

Investigation of Thermal Comfort, Productivity and Lighting Conditions in Higher Education Buildings

Thermal comfort and lighting conditions are essential aspects of indoor environmental quality. They are considered to influence the productivity of room users. The paper presents the experimental test results of research conducted in the university educational building of Kielce University of Technology (Central Poland) using anonymous questionnaires and physical measurements of indoor air parameters with a high-precision microclimate meter. It covers the analysis of the subjective assessment of thermal sensations, acceptability and preference, as well as productivity, lighting and air quality in eleven rooms (both lecture rooms and classrooms). The study analyses the impact of the indoor environment (mostly air temperature and illuminance) on the subjective sensations of the respondents they expressed in the questionnaires. The experiments have enabled us to provide valuable insights into developing the proper indoor environmental conditions to maximise room users' comfort and productivity.

What are the impacts of architectural design on occupants’ well-being, and how can architects optimize spaces to enhance productivity?

In the context of rising urbanization and the growing enhancement of green architecture, architectural design is a topic of increasing importance. This study examines the detailed relationship between architectural design and the human experience, emphasizing the significant effects of design decisions on people's well-being. The research delves into the psychological effects of architectural design and explores how architects can optimize spaces to increase occupants' productivity and well-being while taking environmental sustainability and cost-efficiency into account. Numerous studies have demonstrated the critical relationship between indoor environmental conditions and occupant well-being. This study examines the relationships between architecture and the effects that various elements have on inhabitants' moods, behavior, and general well-being, including ventilation, lighting, color, layout, and biophilic design. Further, it discusses possible solutions that could be implemented, such as eco-friendly architectural design, adaptable floor plans, biophilic design principles, and a variety of color and aesthetic decisions to create environments that foster occupant comfort, creativity, and productivity. The study does, however, identify a few limitations including the possible financial effects of incorporating sustainable design elements. Architects can play a critical role in creating environments that improve the quality of life and productivity of their occupants while respecting the environment and budgetary constraints by taking into account the psychological effects of design choices and advocating for innovative, sustainable, and occupant-centric design approaches.

Expressions of Arab Influence on the Brazilian Architecture: The Case of Solar Control Elements

Over the centuries, architects have distanced themselves from the accumulated architectural knowledge, which often provided constructive solutions highly connected to the climatic context and cultural characteristics. With utmost expression from the 20th century on, building designers have assumed a somewhat negligent attitude towards the architectural project, essentially relying on active mechanical systems, to achieve indoor environmental comfort conditions. This paper overviews the current knowledge of solar control elements adopted and developed by Brazilian architecture, tracing its origin to the influence of Arab and Moorish architecture, with the objective of valuing passive solutions. Arab influence in Brazilian architecture began in the 17th century when Portuguese settlers felt the need to adapt the colonial buildings to the climatic constraints, particularly with regard to excessive solar radiation, leading to indoor thermal discomfort and excess natural light. Arab influence in Brazilian architecture remains present until the current day with the same objectives; however, it has been reinterpreted and appropriated by the Brazilian architectural school. Among the most used construction elements during the colonial period were the muxarabis, rótulas, and gelosias. Throughout the 20th century, these elements were culturally appropriated and served as inspiration for the development of the cobogó and the pioneering use of brise-soleis in Brazilian architecture. These elements have spread throughout global architecture, accompanied by the adoption of computer-controlled dynamic solar protection systems. Therefore, it is important to promote traditional solutions and encourage new architects to adopt passive approaches, aiming for energy efficiency and reducing environmental impacts.

Development of a crop growth model for the energy analysis of controlled agriculture environment spaces

In controlled environment agriculture spaces, the conditions fluctuate between photoperiod and dark period, with crops growing continuously. As crops grow, their impact on the energy demand and energy use, often estimated using a building performance simulation tool, becomes more prominent. In this paper, a dynamic crop model integrated into a building performance simulation tool is proposed to estimate the yield and heat gain/loss from crops by combining a growth model and an energy balance model of the crops. The developed growth model is an adjusted version of a greenhouse lettuce growth model modified for high-density controlled environment agriculture applications by calibrating the sensitive parameters for several indoor environment conditions (temperature, lighting, etc.) using an experimental growth dataset. The yield, the energy demand and the energy use were assessed for a case study modelled in TRNSYS. The results obtained using the greenhouse and developed growth models were compared to those generated with the experimental growth dataset. Depending on the indoor environment conditions, the difference in specific energy use estimated using the experimental growth dataset and the developed model varied between 0.1% and 3.5%, indicating that the model led to an acceptable level of accuracy. The dynamic crop model estimates yield and heat gain/loss from crops for various indoor environment conditions, which are essential for carrying out energy, financial, and environmental analyses.

High-density controlled environment agriculture (CEA-HD) air distribution optimization using computational fluid dynamics (CFD)

In this paper, the indoor environment of a small-scale high-density controlled environment agriculture (CEA-HD) space was simulated using computational fluid dynamics. Spatial modelling of the indoor environment considering the influential phenomena (e.g. transpiration and photosynthesis) over the indoor temperature, relative humidity, carbon dioxide (CO2) concentration, and airflow velocity is still challenging. These indoor environment conditions were computed for a 3D model of a CEA-HD experimental space while simultaneously modelling crop airflow impingement, transpiration and photosynthesis. The crops being grown were represented in the model as porous media zones and their exchanges with the indoor air were modelled using user defined functions. The air distribution parameters and configuration were optimized using a simplified 2D model to overcome the steep computational time, and associated cost, of 3D simulation. The objective function of the optimization problem relied on a correlation analysis of the simulation output. The optimization of the 2D model yielded an airfoil configuration that reduced the mean airflow speed and relative humidity variations between the cultivation tiers while achieving higher mean velocities (≈1.9 m·s−1) at a lower inlet speed (8 m·s−1). The proposed modelling and optimization approach is a small step forward towards model-based design and operation of CEA-HD production spaces.