Indoor Air Temperature Research Articles

Analysis of cooling load calculations using AHU in swimming pools (indoor pools)

AC or Air Conditioner is one of the most widely used mechanical devices in Indonesia. A machine device that functions as an air conditioner so that the indoor air temperature is comfortable, healthy, and has the humidity required by the occupants. Air freshening systems or ACs are increasingly becoming a necessity for urban communities, whether in homes, office buildings, apartments, hotels, swimming pools, hospitals, and even in lecture rooms. The indoor swimming pool measuring 40 x 11 x 3.5 m is located in the apartment So it is uncomfortable for swimmers. Thus, it is necessary to design using an AC system so that the temperature in the swimming pool is cool and comfortable for swimmers. The planning methodology starts by determining data on the indoor swimming pool in apartment AC used. From the analysis results, the cooling load was obtained at 411.5 kW, including a sensible load of 397.66 kW and a Latin load of 13.85 kW, the refrigerant used by the engine was R134a, the compressor power was 81 HP, while the COP was 6.8. So that the indoor air is evenly distributed comfortably, the indoor cold air distribution system uses an AHU (Air Handling Unit) system, capacity of 1900 m3/hour. 2 AHU Units, Blower Power 3 kW

Real-Time Indoor Environmental Quality (IEQ) Monitoring Using an IoT-Based Wireless Sensing Network

In recent years, our time spent indoors has risen to around 90% and to maintain an occupant’s comfort and well-being, Indoor Environmental Quality (IEQ) is monitored. Concerned with inhabitant’s satisfaction and health, the adoption of smart solutions for IEQ monitoring and improvement has expanded. The solution this study explores is an occupant-centric approach involving the implementation of an Internet of Things (IoT) IEQ sensing network in a prominent office skyscraper in Hong Kong. Over the course of 15 months, real-time IEQ data were collected from 12 locations within the building. The data were collected at 1-min time intervals and consisted of readings of indoor air temperature, radiant temperature, relative humidity, air velocity, carbon dioxide (CO2), particulate matter (PM10 and PM2.5), horizontal illuminance levels, and sound pressure levels, which served as the basis of the assessment made about the qualities of thermal comfort, indoor air quality (IAQ), aural comfort, and visual comfort. Compared to traditional periodic surveys, this IoT-based sensing network captured instantaneous environmental variations, providing valuable insights into the indoor environment’s spatial characterization and temporal dynamics. This smart solution also assisted facility management in terms of identifying sources of discomfort and developing effective mitigation strategies accordingly. This study presents an occupant-centric approach to improve occupant comfort and energy efficiency within office buildings. By customizing the built environment to enhance occupants’ well-being, comfort, and productivity, an emphasis is placed on a more personalized and occupant-focused design strategy. This approach integrates technical design with human experience, highlighting the importance of real-time physical and subjective surveys for achieving optimal results.

CFD Simulation of Dynamic Temperature Variations Induced by Tunnel Ventilation in a Broiler House.

Maintaining the optimal microclimate in broiler houses is crucial for bird productivity, yet enabling efficient temperature control remains a significant challenge. This study developed and validated a computational fluid dynamics (CFD) model to predict temporal changes in indoor air temperature in response to variable ventilation operations in a commercial broiler house. The model accurately simulated air velocity and airflow distribution for different numbers of tunnel fans in operation, with air-velocity errors ranging from -0.22 to 0.32 m s-1. The predicted airflow rates through inlets and cooling pads showed good agreement with measured values with an accuracy of up to 108.1%. Additionally, the CFD model effectively predicted temperature dynamics, accounting for chicken heat production and ventilation effect. The model successfully predicted the longitudinal temperature gradients and their variations during ventilation cycles, validating its reliability through comparison with experimental data. This study also explored different variable inlet configurations to mitigate the temperature gradient. The variable inlet adjustment showed the potential to relieve the high temperatures but may reduce overall ventilation efficiency or intensify temperature gradients, which confirms the importance of optimising ventilation strategies. This CFD model provides a valuable tool for evaluating and improving ventilation systems and contributes to enhanced indoor microclimates and productivity in poultry houses.

Container buildings used for residential and business purposes in Johannesburg, South Africa and potential heat-related health risks

Background Outdoor and indoor air temperature affects human health and wellbeing. Climate change projections suggest that global temperatures will continue to increase, and this poses a threat to health. Buildings (for housing and business purposes) that can protect humans from the adverse effects of temperature are essential, especially in the context of climate change. Method In this cross-sectional study, we measured the indoor temperature inside shipping containers comprising a seven-storey block of apartments and businesses in Johannesburg, South Africa for 14 days. We assessed indoor temperature and relative humidity; evaluated measured temperatures in relation to thresholds known to be associated with adverse health risks; and sought to understand heat-health perceptions and symptoms of people living and working in shipping container units. Results Median indoor apparent temperature (AT) (a combination of temperature and relative humidity) was 16°C with values ranging from 6°C (observed at 8:00) to 42°C (observed at 17:00). Insulated units had temperatures between 2°C and 9°C cooler than the uninsulated unit. Heat-health risks from AT exposure were likely in all units, although there was variation in the number of occurrences that AT measurements exceeded the four symptom bands of caution, extreme caution, danger and extreme danger. Indoor AT was found to be 7°C higher on average when compared to outdoor AT. Some participants believed that their units were hot during hot weather and most people opened windows or did nothing during hot weather. Few participants reported experiencing adverse heat-health impacts, except for experiencing headaches (58%) and feeling tired or weak (40%). Conclusion Residents, tenants, or business owners using shipping containers should consider insulation installation and adequate windows/air conditioning for ventilation, especially in hot climates. Further research and awareness regarding heat-health risks of living or working in these spaces is needed.

Experimental study of performance of building heating system based on HPS and equipped with thermal potential recovery system and heat load prediction

The results of experimental studies of the heating system performance of a building located in the area of temperate continental climate with temperatures in the heating period down to −30 °C are discussed. The studied heating system comprises an air–liquid heat pump, a liquid–liquid heat pump with a ground circuit, and an electric boiler for peak demands.The paper presents the results of the influence of the ground thermal potential recovery system on the liquid–liquid heat pump performance. Also the influence of the used method of heat load prediction on the system efficiency and indoor air temperature stability is shown.Implementation of the thermal potential recovery system allows to increase the average value of the transformation coefficient for the liquid–liquid heat pump from 2.7 to 2.9 during three years of operation.Heat load prediction leads to stable indoor air temperature in the range of 22 ± 1 °C. Also it allows to level out the dependence of indoor temperature fluctuations on outdoor air temperature fluctuations.

Coupling adaptive filters with acoustic travel time tomography for indoor climate measurements in occupied room conditions

Acoustic travel time TOMography (ATOM) as a remote sensing technique allows for the measurement of indoor climate parameters, such as air temperature and airflow velocity distributions. The performance of ATOM in monitoring indoor air temperature in empty room conditions was evaluated recently, utilizing a combined analysis approach of room acoustics and tomography. This study shifts focus to the assessment of ATOM in an occupied room condition where fixed objects are introduced to the test area. The presence of objects in a room can result in notable shifts in the travel times of individual reflections. In some cases, these shifts can cause reflections to disappear completely or be replaced by new ones. These partial disruptions in the reflection pattern present challenges in obtaining accurate information about the room's temperature. To ensure the accurate identification of undisturbed reflection paths, an algorithm was developed that can convert distorted signals into a pure reference signal using a suitable adaptive filter. The measurement results confirm the validity of the approach for a temperature interval of 4°C where the system was trained for two temperatures of 20°C and 24°C in a climate chamber.

Evaporation temperature prediction of the refrigerant-direct convective-radiant cooling system based on LSTM neural network

The existing radiant cooling systems are effective at handling indoor sensible heat load. However, the indoor latent load needs to be treated with an additional dehumidification system, which will increase the complexity and initial investment of the system. To settle this dilemma, a novel aluminum column-wing type refrigerant-direct convective-radiant cooling (ACT-RCC) system was proposed, which adopted refrigerant as direct medium and provided cooling and independent dehumidification simultaneously. Experiments were conducted in an enthalpy difference laboratory with the indoor air temperature of 22.0 °C − 30.0 °C and relative humidity of 60.0%. A numerical model of the ACT-RCC terminal was established and validated with the experimental data. To meet the indoor heat and latent load of residential buildings, a heuristic approach based on Long Short-Term Memory (LSTM) neural network for predicting the evaporation temperature of this system was proposed. Results showed that the predicted values were accurate enough by measured data and the maximum deviation of the evaporation temperature was 0.7 °C. The relationship between the evaporation temperature, indoor air temperature and relative humidity, and heat moisture ratio was derived. Besides, the indoor thermal comfort of the selected bedroom was also evaluated. Results showed that when the indoor air temperature and relative humidity remained 26.0 °C and 40.0% − 70.0%, the thermal comfort of this bedroom can achieve Level Ⅰ by adjusting the evaporation temperature of the ACT-RCC system of 2.0 °C − 19.8 °C.

Effect of Cool Roof Paint Application on Metal Roof Surface Temperature and Indoor Air Temperature in Tropical Climates

Abstract Increasingly hot environmental temperatures and less comfortable indoor temperatures are the impacts of inappropriate choices of building roof materials. An effort to mitigate this problem is by using a cool roof. This study was conducted through a field experiment by applying cool roof paint to the metal roof of a building in Malang, Indonesia. Field measurement results show that the cool roof paint application can reduce the maximum surface temperature above the metal roof by up to 6.6ºC and under the metal roof by up to 6.3ºC, as well as the maximum indoor air temperature by up to 0.8ºC at a height of 120 cm above the floor and up to 1.2º C at a height of 50 cm below the metal roof.

Thermal performance of thermochromic smart windows in different indoor environments

Thermochromic (TC) smart windows can passively respond to the exterior and interior temperature, which further adjust their indoor thermal environments due to the varied photo-thermal parameters. This study investigated the thermal performances of practical TC windows in a room with/without air-conditioning or with stratified temperature distribution through a validated 3D CFD model. The application of TC windows could decrease the window, indoor ambient and floor temperatures, and also improve their temperature uniformity. The upper interior window could be 3.13 °C higher than the bottom in a room without air-conditioning, which could be reduced to 2.48 °C if the TC window was utilized. The uniformity improvement could be more significant for indoor environments with apparent air temperature differences, especially for the air-conditioning case. It is suggested that the indoor air temperature and its uniformity may affect the temperature and uniformity of the TC window surface in turn, specifically its phase transition time.

A novel dataset of indoor environmental conditions in work-from-home settings

During the last week of March 2020, millions of workers around the world transitioned to working from home due to the COVID-19 pandemic. While much research has explored the behavioural and psychological aspects of work-from-home (WFH), few studies have provided data-driven evaluations of indoor environmental quality (IEQ). This paper presents findings from a summer 2022 field study of IEQ conditions in WFH settings, involving 95 participants in the North American Pacific Northwest. Sensors installed on participants' work desks continuously measured total volatile organic compounds (tVOC), particulate matter (PM2.5), carbon dioxide (CO2) air temperature, humidity, and sound pressure levels (SPLs). Participants also completed surveys on home and workspace characteristics, as well as their subjective assessments of IEQ, well-being, and productivity. The study found that mean indoor concentrations of tVOC, PM2.5, and CO2 were 262 ppb, 5 µg/m3, and 712 ppm, respectively. Indoor air temperature ranged between 14.7–32.3°C, with a mean of 22.8°C, while relative humidity and SPL averaged at 52.5% and 53.7 dBA. Statistically-significant associations were observed between IEQ variables and factors such as residence type, cooking habits, workspace type, and window availability. A future paper will assess the surveyed behavioural and psychological characteristics in greater detail.

A Multivariate Model and Correlation Study on the Impact of Typical Residential Spatial Forms in the Middle Reaches of the Hanjiang River on the Thermal Environment and Thermal Comfort

Different spatial forms affect the indoor thermal environment and human thermal comfort. A good living environment largely depends on the flexibility of spatial forms, and spatial scale and proportion are the key factors affecting these forms. We selected typical residential houses in the middle reaches of the Hanjiang River in the hot summer and cold winter climate area as an example. Through on-site measurements and questionnaire surveys, we studied the impact of residential form indicators on the thermal environment and thermal comfort. We also established a multivariate model to explore the correlation among various parameters. The results showed that the spatial-real ratio of the residential spatial form index in the middle reaches of Hanjiang River was 5–58%. The height from the ground was 2.23–6.92 m. The open-space ratio was 0.04–4.55. The explanatory power of the spatial form index to indoor air temperature was 57.5%, with a strong correlation (R2 = 0.675). The explanatory power for humidity was 38.2%, with a weak correlation (R2 = 0.525). The explanatory power of SET was 30.6–50.1%, with a weak correlation (R2 = 0.466). The explanatory power of PMV was 6.5–31.7%, and PMV1.0 was weakly correlated (R2 = 0.474). The explanatory power for PPD was 15.5%, where PPD 1.0 was close to a weak correlation (R2 = 0.508). The results of this study provide reference values for the design methods of and decision-making process for green and energy-saving regional buildings.

Energy Flexibility Strategies for Buildings in Hot Climates: A Case Study for Dubai

This paper presents a case study of energy flexibility strategies for a building located in the hot climate of Dubai, a type of climate in which energy flexibility has been under-researched. Energy flexibility is changing the routine-consumption profile and deviating from the normal operation of the building by the users to manage the variability in the load profile and cost of electricity. The three flexibility strategies being considered are based on the modulation of the indoor air temperature setpoint profile while considering different marginal costs for electricity. The main objective is to quantify the energy storage flexibility of each strategy and evaluate its impact on the system ramping and load factor. The study was carried out utilizing a grey-box, resistance–capacitance model of the building, which was validated against experimental measurements. This study is the first to use the following five indicators simultaneously: load factor, system ramping, storage capacity, peak-period demand reduction, and cost savings. Combining these indicators helps building facility managers and distribution system operators (DSOs) better understand the implications of implementing a specific flexibility strategy in a building or a group of buildings. When comparing the indicators of each strategy with each other, it was observed that depending on the amplitude of the change in the electricity cost signal during the peak period, a significant cost reduction of more than 25% could be achieved through the implementation of specific flexibility strategies compared with the normal baseline operating condition.

The effectiveness of duty cycling technique on air conditioning system during break period for comfort, energy and cost savings: A real case study in health center

This paper investigates the potential of duty cycling techniques for air conditioning (AC) systems in producing energy and cost savings and the impact on the indoor thermal environment, air quality, and environmental sustainability. The real case study concentrated on energy consumption practice during the break period between 13:00 to 14:00 in a lobby area of a Health Center. Site data measurements of electrical input power, energy, indoor air temperature and humidity, and indoor carbon dioxide (CO2) concentration were conducted. Two zero-cost strategies were studied and results were compared to baseline conditions. In general, the results show that all strategies can meet the indoor thermal environment and air quality requirements, except room temperature for Strategy 2 which ranges on average between 26.71 to 26.82C. In addition to the instant payback period (0 years), the results also indicate that as compared to baseline condition, Strategies 1 and 2 led to annual energy and cost savings of around 759.2 kWh (USD 64.40) and 1250.6 kWh (USD 106.08), respectively. In terms of environmental sustainability, Strategies 1 and 2 can avoid environmental CO2 emissions by 444.13 and 731.60 kg of CO2 per year, respectively. Due to these reasons and if the sensitivity of occupant comfort to room temperature during the break period can be tolerated, it is proposed that Strategy 2 is the best option for the AC system operation during the break period. Considering other buildings also adopted the same strategy, this small action can lead to a high impact, not only on energy and cost savings but also on environmental sustainability in the future.

Predictive heating load management and energy flexibility analysis in residential sector using an archetype gray-box modeling approach: Application to an experimental house in Québec

This paper presents a methodology to develop archetype gray-box models and use them in an economic model-based predictive control algorithm to simulate optimal heating load management in response to a newly-introduced static time-of-use tariff for Québec’s residential sector, rate Flex-D. The methodology is evaluated through a case study, wherein in situ measurements from a two-storey unoccupied research house of Hydro-Québec are used to develop an 11R6C network with a heuristic zoning-by-floor approach and compute the sequence of optimal electric heating input for the next control horizon. Properly-tuned economic model-based predictive control under rate Flex-D shows potential for an approximately 30% reduction in daily heating cost compared to the reference operation, with a minimal average deviation of indoor air temperature from the reference setpoint. Also, the analysis of the response’s sensitivity to weather forecast uncertainties indicates that the most influential uncontrolled input directing the performance of economic model-based predictive control is the structure price signal, rendering the impact of uncertainty in the weather forecast negligible.

Theoretical modelling and experimental evaluation of thermal performance of a combined earth-to-air heat exchanger and return air hybrid system

To both alleviate thermal saturation in surrounding soil and avoid indoor air pollution caused by closed-loop earth-to-air heat exchanger (EAHE) systems, this study proposes a hybrid mode that combines EAHE and return air (RA), i.e., the EAHE-RA system. A theoretical model was proposed to predict the thermal performance of EAHE-RA systems considering both sensible and latent heat transfer inside the EAHE. Full-scale experiments on both open-loop EAHE system and EAHE-RA system were carried out. The results indicated that the average cooling capacity of EAHE in EAHE-RA system was decreased by 1202.8 W (43.82 %) in summer compared to open-loop EAHE system, while the average cooling capacity recovery of return air was 645.4 W. In winter, the average heating capacity of the EAHE of EAHE-RA system was decreased by 394.1 W (48.91 %), while the average heating capacity recovery of the return air was 1021.7 W. Theoretical predictions of EAHE outlet and indoor air temperature agree well with the measured ones in both summer and winter. Theoretical results indicated that the EAHE-RA system has lower/higher pipe wall temperatures in summer/winter than the open-loop EAHE does, indicating a lower thermal interference on surrounding soils and thus better durability.

Optimization for the temperature range of radiator for preventing draught risk of cold window

High surface temperature of radiator can provide thermal plume with enough momentum intensity and thus counteracts the downdraft caused by window. Meanwhile, it is necessary to avoid causing indoor temperature to exceed upper limit of thermal comfort zone. In this paper, influence of heat transfer coefficients of window and exterior wall, outdoor temperature, and average surface temperature of radiator on operative temperature (Top) and temperature difference between indoor air and window (Td) was investigated. Based on response surface method, single factor and interaction effects for the four influencing factors on Top and Td were analyzed. A framework optimizing average surface temperature of radiator for preventing draught risk of window was established. When the heat transfer coefficient of window was around 1.4 W/m2·°C and satisfied related energy-conservation standard, cold draught could be eliminated without causing indoor environment overheating. Based on the optimized temperature range, the supply water temperature was controlled, and seasonal performance using the variable temperature strategy was investigated based on TRNSYS simulation. With the variable water temperature control strategy, the output hourly indoor air temperature was within the comfortable range. In the whole heating season, hours that did not meet cold draught requirement occupied 77.22 %, as the heat transfer coefficients of window dis-satisfied the energy-saving design standard. As meeting the design standard, the proportion decreased to 12.97 %. Utilizing the optimized temperature interval can prevent discomfort caused by cold draught to the greatest extent. This study is conducive to improving thermal environment with the convective–heating system and may lead to the reduction of energy consumption for heating.

Real‐Time Model Predictive Control of Air‐Conditioners Through IoT—Results From an Experimental Setup in a Tropical Climate

ABSTRACTThere is an increasing demand for air‐conditioners (ACs) to maintain a comfortable indoor environment for all types and sizes of buildings including commercial, industrial, and office spaces. Such ACs are mostly operated by traditional ON–OFF controllers to maintain a temperature setpoint. Extensive control engineering knowledge resulting from experiments on actual buildings is needed before the wide application of an advanced control methods, such as model predictive control (MPC), which are more effective and energy‐efficient than the traditional controllers. Simulation studies on the application of control may provide promising results, but the corresponding experimental validations may prove otherwise. User‐friendly experimental setups to investigate the performance of real‐time advanced control on an actual building and its HVAC system is scarce. This paper details the design, implementation and testing of a user‐friendly, remote and autonomous test bed to acquire measured data through an IoT platform, and to control ACs in real time through MATLAB Thingspeak. Measurement and data acquisition equipment are installed in a two‐zone concrete building in Mauritius. MPC of the indoor air temperature achieved an AC temperature setpoint tracking RMSE which was 0.7°C lower than that achieved by the built‐in ON/OFF AC control. The test bed also revealed that portable air‐conditioners are not very efficient, given that the maximum cooling efficiency achieved in this work was only 60%. It also provided valuable insights based on the experiments carried out, in terms of improvements to sensing and data acquisition. Control engineers can implement such a test bed for the development and application of advanced controllers as per their needs and applications.

Analysis of Indoor Air Quality and Fresh Air Energy Consumption Based on Students’ Learning Efficiency under Different Ventilation Methods by Modelica

This paper aimed to explore a suitable ventilation method at a lower cost of energy to pursue a high learning efficiency based on the characteristics of a Chinese student group and campus building. Firstly, the model was established by Modelica and a good agreement between the numerical simulation and the results by CONTAM 3.4.0.3 was obtained. Secondly, the effects of the fixed window-opening ratio method (FWM), switch control window-opening ratio method (SCM), and automatic control window-opening ratio method (ACM) on CO2 concentration, indoor air temperature, and the heating capacity of air conditioning were investigated. The results showed that, when the FWM with 0% opening or 20% opening was adopted, the indoor CO2 concentration (ICC) was higher than the limit value of the classroom air quality standard, which was 1000 ppm. When the fixed window-opening ratio was greater than 40%, the indoor air temperature could not be controlled at the set value of 18 °C, which presented bad indoor thermal comfort. Meanwhile, when the ACM was adopted, the duration to meet good indoor thermal comfort was 57.17% higher than that of the SCM. However, both of them could maintain the average ICC below the set value in the class. Lastly, the fresh air energy consumption under different ventilation methods was compared. When the design temperature was 13.5 °C, it could be revealed that the fresh air energy consumption under the ACM, SCM, and FWM with 40% opening was 46.58%, 48.38%, and 51.26% lower than those at 18 °C. In summary, it was recommended to set the design temperature of the classroom at 13.5 °C, and the ACM was suggested as a suitable ventilation method to provide fresh air for the classroom.

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 novel model to detect abnormal thermal energy consumption in existing buildings with limited metering infrastructure

In EU, excessive energy waste occurs in existing buildings due to multiple reasons, such as inadequate building design, frequent operation of the technical building systems far from the design and reference conditions, lack of proper maintenance, lack of building automation and control systems, unexpected or exceptional weather conditions and inappropriate end-user behaviours. In this context, the detection of “abnormal” energy consumption is crucial both to increase end-users’ awareness and to help energy managers and technicians in the energy diagnosis. In this paper, the authors present a novel model to detect abnormal thermal energy consumption in existing buildings equipped with limited metering infrastructure. Four heating seasons of daily thermal energy consumption and indoor environmental data of a case study building located in Central Italy were analysed to develop, train and test the model for data processing, benchmarking, abnormal energy consumption identification and diagnosis. The results of the test phase show the ability of the model to detect behavioural faults, such as the low consumption and indoor air temperature in two dwellings and the high ones in one other. Also, the poor thermal insulation properties of the building were clearly highlighted by the proposed model.