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The paper introduces the assessment model of effectiveness of surrogate model for predicting indoor air temperature in buildings using a range of machine learning techniques and optimized data sampling algorithms, with the primary objectives of enhancing prediction accuracy. The main aim is to improve the forecasting accuracy. Among the evaluated models, the SVM demonstrates the highest error rates, as evidenced by its higher mean square error (MSE), root mean square error (RMSE), and mean absolute error (MAE) values. A negative R² value also indicates that the SVM model is performing poorly, unable to capture the underlying patterns in the data effectively. In contrast, linear regression performs significantly better, with lower error rates and an R² value of 0.871, explaining 87.1% of the variance in the data and indicating a strong linear relationship. Gradient Boosting demonstrates excellent performance, with low error rates and a high R² value of 0.992, which almost completely covers the variance of the data and provides highly accurate predictions. AdaBoost surpasses all other models by achieving the lowest errors. With an R² value of 0.998, it accounts for 99.8% of the data's variance, making it the most precise model for this dataset. Overall, AdaBoost exhibits the highest accuracy with the smallest error rates and the highest R² value, with Gradient Boosting coming in second. [ABSTRACT FROM AUTHOR]

Objective. An emergency shutdown of the heating system of a civil building will inevitably lead to a violation of the thermal regime in the heated rooms. Subsequently, this incident will have a negative impact on human thermal comfort and working conditions. Thus, the issue of predicting the thermal regime of a room in the event of a termination of the supply of thermal energy, along with the search for energy-saving technologies in the construction sector, is an urgent task.Method. Software calculations are based on the classical theory of thermal stability of a room in the case of intermittent heat supply and the law of regular thermal regime of the first kind (for a heating system), the essence of which is as follows: a decrease in temperature at all points of the body during its cooling occurs in the same way, obeying an exponential law.Result. For a room in a residential building, it is established that the indoor air temperature will reach the dew point (12.5 ℃) for a period of 8 hours 42 minutes after an emergency shutdown of the heating system, and the zero value after 23 hours 42 minutes. The average value of the rate of temperature drop over a period of 50 hours is 0.72 ℃⁄h, the total loss of thermal energy was 138.2 MJ. Conclusion. Thermal accumulation of enclosing structures plays a primary role for civil buildings in maintaining the required thermal regime of the room in the event of an emergency shutdown of the heating system. With a relatively low heat capacity and high heat transfer associated with the high temperature pressure of the heating elements, the cooling of the heating system occurs quite quickly (has a high cooling rate).

The rise in energy-efficient building strategies, driven by the intensifying energy crisis, has encouraged the development of the passive house (PH) approach. However, existing research highlights a potential downside, the perception of the overheating risk in hot periods, particularly when design and construction methods fail to incorporate adequate mitigation strategies. This study examines the pressing necessity of addressing overheating risks in PHs through a systematic review. The aim is to identify key factors reported as contributing to overheating, to evaluate recommended solutions across diverse global regions, and to identify methods to reduce the risk. This review indicates that PHs are considered at risk of overheating in the hot periods of the year across many climatic regions, exacerbated by the impacts of climate change. Architectural features, climate conditions, inhabitants' behaviors, and perceptions of the quality of indoor spaces are important factors affecting PH overheating and should be considered at the design stage. It is concluded that the urban context, building envelope characteristics, and their impacts require greater attention. Based on the knowledge gaps identified, green walls are proposed as a nature-based solution with good potential for mitigating overheating in PHs. More integrated consideration of all factors and solutions can minimize current and future risks. [ABSTRACT FROM AUTHOR]

During the pandemic and periods of martial law, educational institutions in Ukraine implemented various forms of organising the educational process, combining in-person and remote classes. As a result, there has been a decrease in the level of use of building premises. The analysis of the characteristics of energy consumption in these conditions requires additional attention. In this study, a dynamic energy model of the building was created using the educational building of Igor Sikorsky Kyiv Polytechnic Institute as an example. Energy consumption for heating needs was determined for normal operating conditions and quarantine conditions in Ukraine (only a specific part of the premises is being operated, ensuring that the standard temperature value is maintained while people are present). Based on the results of the study, the features of the energy consumption of the building during quarantine/martial law restrictions, subject to the partial use of the building, were analysed, and the main disadvantages of such a mode were identified. For the educational building of the university, the total energy consumption for heating needs depends on the number of rooms that are actively used, and the modes of regulation of heating and ventilation; the consumption can decrease depending on the chosen scenario by 61 %, 56 %, and 34 % in quarantine mode. The findings indicate that the efficiency of regulation modes can decrease by more than four times when compared to normal mode due to internal heat exchange with unoccupied rooms. [ABSTRACT FROM AUTHOR]

The rise in energy-efficient building strategies, driven by the intensifying energy crisis, has encouraged the development of the passive house (PH) approach. However, existing research highlights a potential downside, the perception of the overheating risk in hot periods, particularly when design and construction methods fail to incorporate adequate mitigation strategies. This study examines the pressing necessity of addressing overheating risks in PHs through a systematic review. The aim is to identify key factors reported as contributing to overheating, to evaluate recommended solutions across diverse global regions, and to identify methods to reduce the risk. This review indicates that PHs are considered at risk of overheating in the hot periods of the year across many climatic regions, exacerbated by the impacts of climate change. Architectural features, climate conditions, inhabitants’ behaviors, and perceptions of the quality of indoor spaces are important factors affecting PH overheating and should be considered at the design stage. It is concluded that the urban context, building envelope characteristics, and their impacts require greater attention. Based on the knowledge gaps identified, green walls are proposed as a nature-based solution with good potential for mitigating overheating in PHs. More integrated consideration of all factors and solutions can minimize current and future risks.

There are differences in thermal perceptions between preschool children and adults, scientific guidance for adults on dressing children is thus essential. This study aimed to find the preferred thermal properties, i.e., thickness and thermal conductivity, of sleeping bag for children to achieve sleeping thermal comfort based on analysis of heat transfer. A validated 3D heat transfer model was established for the analysis. The results showed that skin temperatures (Tsk) at central body segments are higher than those at distal segments. Tsk is positively (p

Several alternatives have been introduced in recent years to enhance the thermal comfort levels within buildings. Thermally Activated Building Systems (TABS), one of the above alternatives, have gained interest because of the huge benefits this technology offers the building sector. This type of system consists of encapsulated pipes within the building structure to control the surface temperature. This study explores the thermal behavior of the cooling surface and fluctuations in indoor air temperature (IAT) of TABS under various cooling scenarios. Only limited number of investigations has been carried out to study the heat transfer behavior of TABS. Hence, the building indoor thermal properties such as air temperature, surface temperature and rate of heat transfer between the indoor air and inner surface of the TABS has been evaluated experimentally by enhancing the cooling surface area. Moreover the results were compared with the conventional building (no cooling provides). The thermal energy stored in the TABS is significantly removed by the increase in cooling surface area, resulting in a 2°C decrease in the average indoor air temperature. The average heat gain of all wall surfaces in the case of no cooling (WOC) ranges from −3 to 13 W/m2. The amount of heat gain on the walls was not significantly affected by only roof and floor cooling (R+F) activities. Moreover, it ranged from −2 to 24 W/m2 in all surface cooling (ASC) scenarios. As a result, there was additional surface cooling, which increased surface heat gain and indoor cooling capacity. [ABSTRACT FROM AUTHOR]

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions. [ABSTRACT FROM AUTHOR]

Thermal comfort is linked to our health, well-being, and productivity. The thermal environment is one of the main factors that influence thermal comfort and, consequently, the productivity of occupants inside buildings. Meanwhile, behavioural adaptation is well known to be the most critical contributor to the adaptive thermal comfort model. This systematic review aims to provide evidence regarding indoor thermal comfort temperature and related behavioural adaptation. Studies published between 2010 and 2022 examining indoor thermal comfort temperature and behavioural adaptations were considered. In this review, the indoor thermal comfort temperature ranges from 15.0 to 33.8 °C. The thermal comfort temperature range varied depending on several factors, such as climatic features, ventilation mode, type of buildings, and age of the study population. Elderly and younger children have distinctive thermal acceptability. Clothing adjustment, fan usage, AC usage, and open window were the most common adaptive behaviour performed. Evidence shows that behavioural adaptations were also influenced by climatic features, ventilation mode, type of buildings, and age of the study population. Building designs should incorporate all factors that affect the thermal comfort of the occupants. Awareness of practical behavioural adaptations is crucial to ensure occupants' optimal thermal comfort. [ABSTRACT FROM AUTHOR]

Thermally Activated Building System (TABS), is one of the alternative cooling methodology, due to the numerous advantages it provides the building industry. This system consists of encapsulated pipes within the building structure to control the surface temperature, which helps to remove the sensible heat from the indoor. The objective of this investigation is to experimentally evaluate the influence of cooling surface area and the effect of cooling water inlet flow velocities on the TABS performance in the warm and humid zone under natural ventilation, and the experimental results were compared with the conventional building (CB), which does not have any cooling arrangements. An increase in water inlet velocity from 0.35 to 1.5 m/s substantially removes the thermal energy stored in the TABS, and reduces the average indoor temperature by 3 °C. In a CB, the average heat gain of all surfaces varies from −3 W/m2 to 13 W/m2. Moreover, in all surface cooling (ASC), it varied from −2 W/m2 to 24 W/m2. This resulted in increased surface cooling, which increased the surface heat gain and indoor cooling capacity. There are no significant thermal behaviour and heat transfer variations at 1 m/s and 1.5 m/s of inlet water flow velocities.

In thermally modernised buildings, sharing of ventilation heat loss becomes more significant. In the case of the application of ventilation with heat recovery, especially during transitional periods, there arises a question of whether an air system makes it possible to maintain the required indoor air temperature without the necessity of using a basic hydronic heating system. This paper presents the application of a simple thermal network model of a building zone to simulate indoor air temperature in a single room of a multi-storey building with a mechanical ventilation system with heat recovery. Ventilation air was supposed to be the only heat source and its ability to maintain the required indoor air temperature was checked in simulations and then compared with measurements. The 5R1C thermal network model of a building zone was used for simulations. Comparison with measurements showed the Mean Absolute Error (MAE) and Root Mean Square Error (RMSE) of indoor air calculation to be 2.37 °C and 2.45 °C, respectively. When including heat flux from the bottom storey through the floor, MAE = 1.28 °C and RMSE = 1.38 °C were obtained. [ABSTRACT FROM AUTHOR]

• Multiple machine learning methods for indoor air temperature prediction were tested. • Optimized decision-tree based methods produced best overall performance. • Simple methods lost to best methods, but should be used as reference. • Optimization methods affected primarily calculation time, but not end result. • Prediction methods should be compared using multiple datasets and time periods. Indoor air temperature is one of the key variables for indoor air quality, building energy consumption and moisture safety. Measurements are required to have accurate information on how well indoor air temperature during operation matches the target values set in the design phase. However, besides the information acquired during the measurements, we would also like to have a more comprehensive understanding on how the temperature conditions behave outside the measurement campaign, in different years and in future climatic conditions. The purpose of this paper is to compare machine learning (ML) methods for long-term prediction of hourly indoor air temperature, where the predictions are made based on outdoor climatic conditions only. According to results, the prediction accuracy (mean absolute error) was between 0.78 °C and 1.71 °C for the baseline method (arithmetic mean of training data) and between 0.5 °C and 0.8 °C for the best methods. Prediction methods should be evaluated using multiple datasets and with sufficiently long measurement periods. The most influential factor for prediction accuracy was the selection of the prediction method, whereas optimisation method, number of cross-validation splits and number of lagged values of the climatic variables were of secondary importance. The best combination of prediction accuracy, calculation time and robustness towards variation in measured data was found with decision-tree based methods, such as RandomForest, XGBoost, LightGBM and ExtraTreesRegressor. In the future common datasets and a benchmarking system should be defined for a better comparison of different ML methods for indoor air temperature prediction. [ABSTRACT FROM AUTHOR]

Ensuring the required microclimate parameters of a cultivation structure, including indoor air temperature, relative humidity, and soil surface temperature, plays a crucial role in obtaining a rich harvest of vegetables and fruits in the winter season. Creating favourable conditions for growing plants in the protected ground is possible only by using modern, high-tech heating systems that will compensate for heat energy losses and maintain a set temperature regime in the room. Gas-fired radiant heating is one such system. This heating method, using ceiling gas infrared emitters as heat sources, directs the required heat flux directly to the soil surface. At the same time, direct or indirect emissions of harmful substances into the environment are minimal, and due to the absence of heat losses during heat production and heat carrier transportation, this type of heating is effective from both energy-saving and economic points of view. The article investigates the influence of meteorological, aerodynamic, heat engineering, and other factors on the air temperature in a cultivation structure under gas-fired radiant heating conditions using a developed software calculation method. An analysis is performed, and explanations are given for the nature of the change in indoor air temperature depending on the changing environmental conditions—the example of the industrial greenhouse “Farmer 7.5” (Russian Federation).

Maintaining favorable microclimatic conditions in a residential dwelling is directly related to the stable operation of the heating system. An emergency shutdown of the heating system, especially in the winter season, can lead to serious negative consequences: disruption of thermal comfort for people in the residential building, rupture of pipelines and heating devices, flooding of adjacent premises, etc. Thus, the issue of predicting the thermal regime of a residential dwelling after the termination of heat supply is of practical relevance. The object of study: Residential premises in buildings. Subject of study: Patterns of change in the temperature of the indoor air (in dimensional and dimensionless forms), the rate of temperature drop, and the loss of thermal energy during an emergency shutdown of the heating system. Objective of the study: To forecast the thermal regime of a room in a residential building after an emergency shutdown of the heating system. Research methods: Classical theory of thermal stability of building enclosures; theory of regular thermal regime, according to which the temperature field at all points of the cooled body (in this case, the heating system) changes in the same way, obeying the exponential law; software computer calculations. Research results: In a room of a residential building, the indoor air temperature will reach the dew point (12.5 ℃) within 8 hours 42 minutes after an emergency shutdown of the heating system and zero value - after 23 hours 42 minutes. Based on the conducted scientific research, it can be stated that the thermal storage capacity of external enclosures, due to their design features, plays a primary role in preserving the thermal regime of a residential dwelling after the heating system is turned off.

Translucent facade structures are increasingly more often used currently in constructing skyscrapers and high-rise buildings, especially in constructing office complexes. In a number of cases, the facade glazing-to-wall ratio of such structures is 0.8 or higher. Within the framework of this study, the thermal operation conditions of the tower, which is dominant in the Lakhta Center multifunctional complex, are considered. The studied object's space-heating system switching-on and switching-off modes, which characterize the building's indoor microclimate parameters at the heating season beginning and end, as well as in the case of heat supply emergency disconnection, are modeled. A heat-balance equation is set up that takes into account the heat gains and heat losses for the tower. The influence of the heat-storage capacity of the most bulky and heat-absorptive civil structures located within the building's heated volume is taken into account. The advisability of using the coldest day temperature of the specified exceedance probability as the design outdoor air temperature in designing the space-heating system of glazed buildings is substantiated. An equation describing the variation of the indoor air temperature tin in the case of the space-heating system's emergency disconnection is obtained. The tin variation pattern at outdoor air temperatures tout varying from –28 to 8°C at 4°C steps is shown. For each selected tout value, the tentative time taken for tin to reach its permissible value after the emergency disconnection of the building's space-heating system is evaluated. It has been determined that the amount of heat that has to be spent for heating all bulky structural elements located inside of the building heated volume by 1 K makes 160.84 GJ, and the heat losses through the building glazed facades are 179.1 kW/K. The heat-storage coefficient of the considered object under study, which shows the time for which the initial difference of temperatures will decrease by e times after disconnection of the space-heating system, has been found to be approximately 250 h. This means that the tower has a sufficient thermal energy storage, which allows a comfortable indoor air temperature to be maintained for a long period of time. [ABSTRACT FROM AUTHOR]

Photovoltaic energy has the potential to become one of the major energy sources used in the households in the tropical region of Africa, where the solar radiation intensity is abundant and almost constant over the year. Solar photovoltaic systems present many advantages when they are integrated in the building structure envelope and have a significant influence on the indoor air temperature of dwelling buildings due to the thermal resistance modification. In this paper, a simplified model of the photovoltaic system integrated on the roof of a residential building according to the building construction customs and materials has been designed and modeled. The heat transfer is studied in several situations: with and without a Building Integrated Photovoltaic (BIPV) system and for a building with and without false ceiling. The BIPV system installed over an effective area of 35 m2 increases the building indoor air temperature of approximately 5 °C which is corrected by the heat insulation optimization of the false ceiling made up with building local materials. The final indoor air temperature obtained is in good agreement with the ASHRAE standards and can, therefore, be applied to tropical regions.

The accurate prediction of indoor air temperatures is vital for the timely control of thermal environments in livestock buildings. However, ensuring the accuracy of air temperature predictions is challenging due to the uncertainty introduced by external factors and periodic fluctuations. New approaches are required to improve the prediction accuracy of models for indoor air temperatures. A discrete model is developed that incorporates time-period groups (TPGs), the group buffered rolling (GBR) mechanism, and TPG factors. The measured temperature data were divided into four TPGs in chronological order, and the GBR mechanism with TPG factors eliminated fluctuations and periodicity from the time-series data. The efficacy of the proposed model was verified by comparing its results with those of two existing models (the grey model and discrete grey model) and filed experiments in layer hen houses. The traditional model only reflected the increasing trend of original air temperature data, but did not reflect periodic fluctuations. The mean absolute percentage error (APE) was >10% in the traditional model. The GBR mechanism and TPG factors allowed the new model to reflect deviations in the original time-series at each time point in real-time, thus minimizing the APE and improving the prediction performance. The modified model was superior to the traditional models, exhibited higher accuracy and reliable performance (APE < 10%), and accurately captured the periodic fluctuations in air temperature. It is a helpful tool for predicting indoor air temperatures, and may help in the development of improved control strategies for the indoor thermal environment. • A new discrete grey model modified with the group buffered rolling mechanism. • The GBR-DGM shows improved prediction performance for indoor air temperature. • Accurately predicts temporal fluctuations in air temperatures in a layer hen house. • Applications include the development of improved environmental control systems. [ABSTRACT FROM AUTHOR]

Comfort temperature is important to investigate because the chosen office indoor temperatures affect the energy used in a building, and a thermally comfortable environment makes the occupants be more productive. The effects of temperature on comfort are broadly recognized for thermal comfort. Japanese office buildings are well equipped with air-conditioning systems to improve the thermal comfort of the occupants. The main objectives of this research were to compare the winter comfort temperature in mixed mode (MM) and heating, ventilation and air-conditioning (HVAC) office buildings and to investigate the relationship between the comfort temperature and the indoor air temperature. This study measured the thermal environmental conditions of the office buildings and surveyed the thermal comfort of the occupants. The field survey was conducted during winter in seven office buildings located in the Aichi prefecture of Japan. In total, 4466 subjective votes were collected from 46 occupants. The result suggested that the occupants were found to be more satisfied with the thermal environment of MM buildings than that of HVAC office buildings. Overall, 95% of comfort temperatures were in the range 22~28 °C in MM and HVAC buildings, which were higher than the indoor temperature of 20 °C recommended by the Japanese government. The comfort temperature was highly correlated to the indoor air temperature of the MM buildings than to that of HVAC buildings. This indicated that the occupants were more adapted towards the given thermal environment of MM buildings. [ABSTRACT FROM AUTHOR]

Greenhouses are essential for agricultural production in unfavorable climates. Accurate temperature predictions are critical for controlling Heating, Ventilation, Air-Conditioning, and Dehumidification (HVACD) and lighting systems to optimize plant growth and reduce financial losses. In this study, several machine models were employed to predict indoor air temperature in an even-span Mediterranean greenhouse. Radial Basis Function (RBF), Support Vector Machine (SVM), and Gaussian Process Regression (GPR) were applied using external parameters such as outside air, relative humidity, wind speed, and solar radiation. The results showed that an RBF model with the LM learning algorithm outperformed the SVM and GPR models. The RBF model had high accuracy and reliability with an RMSE of 0.82 °C, MAPE of 1.21%, TSSE of 474.07 °C, and EF of 1.00. Accurate temperature prediction can help farmers manage their crops and resources efficiently and reduce energy inefficiencies and lower yields. The integration of the RBF model into greenhouse control systems can lead to significant energy savings and cost reductions.

Windows are crucial in regulating indoor daylighting, cooling, and heating to ensure suitable living conditions. However, conventional windows lack the capability for spectrum splitting and exhibit unpredictable light and heat management. This study introduces a smart window to propose a reversible function by utilizing Antimony Tin Oxide (ATO) nanofluids with near-infrared (NIR) selective absorption. An experimental study was conducted in a controlled chamber to evaluate these smart windows' dynamic light and thermal environment. The study examined the effects of ATO nanofluid concentrations at 10 ppm, 100 ppm, and 500 ppm. Results showed that the maximum indoor air temperature decreased by 4.35 °C , 2.78 °C , and 4.29 °C , respectively. Additionally, the maximum temperature differences on the outer surfaces of the exterior glass (EG) and the control glass (CG) were 4.27 °C , 3.78 °C , and 4.43 °C , respectively. When the smart window was oriented to the east, west, or north, the maximum indoor temperature differences between EG and CG were 1.37 °C , 2.9 °C , and 2.43 °C , respectively. This study demonstrates the potential of ATO nanofluid-based smart windows to enhance indoor environmental control through effective light and heat management. [ABSTRACT FROM AUTHOR]

The indoor climate of a poultry building is essential for the well-being and health of birds and their production performance, but achieving in-time indoor thermal environment control is difficult using the current environmental control systems that are only based on data collected by onsite sensors. Data collected by the onsite sensors can only reflect the current thermal environment conditions. Still, it cannot predict the variation trends in the environment, nor can it reflect whether cold or heat stress will occur in the livestock building. Heat and cold stress have caused significant economic losses to egg production. Predicting the indoor air temperature in poultry houses can aid in forecasting extreme air temperature, formulation of control strategies, saving energy, and reducing losses. This study proposed a novel grey model to forecast indoor air temperature, which captures the essential features of the developing trends and fluctuation characteristics. The novel model was combined with a buffered rolling mechanism, time period groups, and variation indices to enhance the accuracy; the variation indices of each time period group were inserted into each rolling process. The proposed model was employed to forecast the indoor air temperature of a poultry house; the efficacy and reliability of the proposed model were evaluated by conducting field experiments and comparing other similar forecasting models. The results demonstrated that the traditional grey model only showed a growth trend for the measured data but failed to reflect a trend with the fluctuation effect. The absolute percent errors of the conventional and novel grey forecasting models were 16.9%, 13.3%, and 6.3% in the training stage, and 26.3%, 16.4%, and 4.8% in the test stage, respectively. The buffered rolling mechanism and variation indices of each period can reflect the deviation degree in the measured data at each time point from the average trend in real-time, minimize the absolute percentage error, and improve the forecasting performance. The proposed model was superior to the traditional grey forecasting models, exhibiting a more accurate performance based on reduced error (<10%) in both the training and test stage forecasts. The proposed grey model can capture the variation trend and air temperature fluctuation characteristics in a time series. It is a helpful tool for forecasting the indoor air temperature in poultry houses. [ABSTRACT FROM AUTHOR]

Space cooling has utilized lots of electricity in summers which has to be reduced by insulating inside of buildings. This has potential to reduce the environment pollution caused by higher expenditure of energy. The present work has the scope to evaluate thermal performance of a test model house providing cellulose fibre based cardboard insulation and compare the performance with an uninsulated model. The addition of heat through roof, walls and net heat gain during the day has remained lesser for insulated test model than uninsulated test model. The temperature of indoor air has stayed lesser in case of insulated test model than the uninsulated test model during the day particularly in the interval from 12 hrs to 19 hrs The indoor air temperature for the insulated test model has remained lesser by 7°C from 14 hrs till 16 hrs in comparison to uninsulated test model. The energy and financial savings obtainable for summers has value of 144 kWh and Rs 864 respectively for insulated test model. The carbon dioxide emitted has reduced for summers by 226 kg for insulated test model. Hence better thermal comfort conditions have existed in the insulated buildings. [ABSTRACT FROM AUTHOR]

Current control strategies implemented in most environmental control systems rely purely on onsite sensor data to determine the operating status of the control devices, while neglecting to consider the delayed control effects caused by time lags of the devices. These strategies cannot provide accurate in-time indoor environmental control, causing increasingly high energy consumption. To overcome these disadvantages, a new control strategy, which determines the operating status of control devices based on predicted temperature data, was developed, and its effectiveness was evaluated through field experiments. A comparative study was conducted using the new control strategy and a typically implemented control strategy. The two control strategies were respectively assigned to two adjacent commercial poultry houses. The indoor air temperature and electricity usage were recorded during the field experiments. The results showed that the air temperature was significantly higher in the house with the typical control strategy than in the house with the new control strategy (p < 0.05) in summer and significantly lower (p < 0.05) in winter. The new control strategy reduced the maximum temperature difference from 5.8°C to 3.1°C in summer and from 13.4°C to 5.2°C in winter. The new control strategy reduced energy consumption by 4.61% to 7.38% compared to the typical control strategy. It is concluded that the house with the new control strategy had a lower air temperature difference that was beneficial for energy savings. [ABSTRACT FROM AUTHOR]

The importance of sustainability has become one of the major priorities in the field of construction with 40% of total energy worldwide while account for 25% of carbon dioxide emissions are due to buildings. Thus, the implementation of sustainability along with use of Building Information Modelling (BIM) tools are employed in this research to perform energy analysis on buildings using various sustainable materials. The results showed that the incorporation of sustainable materials with low thermal conductivity could reduce the indoor air temperature by 8 ºC and the overall electricity consumption by 30% and carbon emission from 15% to 48% while lowering construction costs. [ABSTRACT FROM AUTHOR]

This study was to explore gender differences in response to subjective comfortable thermal environment by performing human experiments in summer. A total of six discontinuous conditions for self-regulating ambient temperature were designed with five males and five females. Sleep onset latency and slow wave sleep, from the physiological indices of electroencephalograph, electromyography and electrooculography, measure sleep quality objectively. Results indicate that gender differences in subjective temperature preferences, thermal sensation and sleep quality are reflected by temperature or temperature intervals. In particular, women have a significantly higher temperature range than men. Meanwhile, the temperature sections of males and females corresponding to thermal sensation and sleep quality are summarized under the same thermal resistance. Moreover, the results of temperature difference between men and women on thermal perception and sleep quality showed that the temperature difference of subjective sleep quality coincides with the objective one, but greater than the subjective thermal perception results implying that a greater discrepancy between males and females in sleep thermal physiological needs than during their awake. [ABSTRACT FROM AUTHOR]

The study aims to identify the main reason of the thermal response time difference between historical and modern buildings. Therefore, in this study, the thermal response time of historical and modern wall structures and its effect on the interior air temperature change was investigated parametrically. Considering the environmental conditions of Kocaeli province, Turkey, the thermal response time of a historical building wall made of a cut stone was compared with those of brick and gas concrete wall structures having the same overall heat transfer coefficient using the second-order lumped capacitance approach. The insulation thicknesses of the three different construction materials for U-values of 0.6, 0.4 and 0.2 W/m²K were calculated and temperature variations of indoor environment, wall and insulation material were analyzed. In addition, the required thicknesses of insulation material to obtain the same heat transfer coefficients were determined in case of using the 0.1 m thickness of cut stone, brick and gas concrete structure materials. The maximum and minimum amplitudes of the inside air temperature were recorded as 0.59 and 0.18°C for the aerated concrete in Case 3 and for the cut stone in Case 2, respectively. As a result, the walls with high thermal inertia are less affected by the changes in the environmental temperature although their U-value is relatively high. For this reason, it can be stated that one of the reasons why historical buildings have thick walls is to increase thermal inertia and thereby improve thermal comfort by reducing energy loss. [ABSTRACT FROM AUTHOR]

This paper presents wind-tunnel experiments of cross-ventilative cooling in a generic isolated building with an interior heated side wall. Two different sizes of openings are considered: large and small openings. Particle image velocimetry (PIV) is used to determine velocities in the vertical centerplane. Air temperatures in the vertical centerplane are measured using negative temperature coefficient (NTC) sensors. Surface temperatures on the heated wall are measured using an infrared camera. Surface heat fluxes are obtained using heat flux sensors. In both cases the indoor airflow is dominated by the jet through the openings, with higher velocities in the building with large openings. The air temperatures measured with small openings are up to 7.5 % larger than those with large openings. The surface heat fluxes are up to 20 % higher in the building with large openings. The interior convective heat transfer coefficients vary considerably across the heated wall for both opening sizes and can be very different (up to 5 times higher) from those obtained by existing internal convective heat transfer coefficient correlations. The measurement results give insight into the complexity of ventilative cooling and can be used to validate computational fluid dynamics (CFD) simulations of cross-ventilative cooling. • Wind tunnel experiments on ventilative cooling in a building with heated wall. • The experiments are carried out for two different opening sizes (large and small). • Overall, larger temperatures are measured for case with small openings. • Heat flux varies considerably across the heated wall for both cases. • Comparison of CHTC from measurements with CHTC from correlations from literature. [ABSTRACT FROM AUTHOR]

The study aimed to assess the effect of indoor air temperature on cognitive work performance of acclimatized people during the middle of heating season in severely cold region in China. How does temperature affect work performance was also investigated through subjective surveys. Eighteen acclimatized and healthy participants (10 males and 8 females) carried out tasks in a climate chamber under six temperatures (18°C, 20°C, 22°C, 24°C, 26°C and 28°C). They completed 11 neuro-behavioural tests, including perception, learning and memory, thinking, expression and executive function tests, and subjective surveys on thermal sensation, emotion, health, workload and enthusiasm. Results showed that temperature has an effect on the variation on the neuro-behavioural performance depending on task types and difficulties. Temperature could influence the response time significantly more than accuracy when tasks were performed without feedback. Under adverse environment, people would feel (slightly) cool or warm, feel less enthusiastic to work, and would have more negative emotions, perceived sick building syndrome to be more serious and would have evaluated performance as worse. They would also perform badly on neuro-behavioural tests. Subjective surveys were important supplements to objective evaluation on the effect of indoor environment factors on work performance. [ABSTRACT FROM AUTHOR]

A comfortable mattress can create a good sleep environment, but the thermal behaviour of the interaction between the human body and mattress materials is still not well understood. The effect of mattress materials on human–mattress interface temperature and human thermal state was evaluated by subjective questionnaire and measurements to detemine the human–mattress interface temperatures of the whole body and various locations of the body (WTH-M and ETH-M). The woollen fabric and polymeric foam of mattresses were evaluated as optimal cushion materials; these were indicated by measurements at 15.5 and 20°C, due to the higher WTH-M and ETH-M. Moreover, the interface temperatures measured at the back, buttock and thigh were higher with most materials than at other parts of the body, indicating a lower temperature response at the body extremities due to the body–mattress interface. Under the cold climate, people would prefer warm thermal sensation rather than the neutral thermal feeling. There should be more consideration on the thermal insulation of the extremities such as the feet. The human thermal response was not only affected by mattress materials, but also by body parts and indoor air temperatures. These findings are important to understand the heat transfer and human thermal comfort requirements, providing a comfortable thermal environment to ensure sleep quality. [ABSTRACT FROM AUTHOR]

Methods to model animal occupied zones in computational fluid dynamics (CFD) for predictions of air properties are explored. The study was based on CFD analysis supported by experimental validation. Animal occupied zone (AOZ) are modelled as a porous media to reduce the computational cost. Here, the porous media was modelled in two ways. The first with constant and uniform values of porous-resistance coefficients throughout AOZ and called the porous-media method (POM), and the second with porous resistance considered according to the spatial location of the occupants and called profiled-porous-media method (PPOM). For POM, a CFD sub-study was required to estimate the porous media resistance coefficients. For PPOM, porous resistances were considered as infinite at the spatial-locations of occupants but elsewhere the resistance was zero – no separate study for estimating resistance coefficients were required. Results of the simulations with both POM and PPOM were compared with the CFD simulations when animals were present in AOZ. It was concluded that it is possible to model AOZ as a POM or PPOM. However, in POM, the coefficients of porous-media were layout dependent and a separate study for estimation of porous-media coefficients will always be required for each single layout. Moreover, POM under-predicted the average velocities in AOZ. On the other hand, there were two benefits of PPOM. Firstly, no separate study was required for the estimation of porous coefficients and the secondly it gave more realistic average velocities in the AOZ. Average temperatures in AOZ were accurately predicted by both POM and PPOM. [Display omitted] • A new method is proposed for modelling occupants in ventilated spaces in CFD studies. [ABSTRACT FROM AUTHOR]

A hot-humid climate, which is characterized by uniform diurnal temperature throughout the year, can provide a year-round opportunity to benefit from phase change materials (PCMs) for buildings' application. However, the small diurnal temperature fluctuation might restrict the PCMs' performance and efficiency. This paper investigates the capability of PCMs to decrease the peak indoor air temperature (Tᵢ) of naturally conditioned buildings in a hot-humid climate region by storing night coolness. To this end, field measurement has been conducted in existing buildings to identify the appropriate transition temperatures' range for PCM. Then, a macro-encapsulated PCM with different transition temperatures and quantities has been numerically examined. The results showed a considerable reduction in peak Tᵢ, particularly when using PCMs with lower transition temperatures and higher quantities. Although uncompleted PCM freezing was observed when lower transition temperatures were used, it was tackled by combining night ventilation. Moreover, the PCMs showed effective year-round performance and achieved a reduction in the peak Tᵢ reached up to 4.87 °C. The optimal performance was achieved when using a combination of NV and PCMs with the lowest transition temperature that ensured complete PCMs freezing. Therefore, the PCMs’ freezing temperature should be considered when selecting PCMs for such conditions.

A Mobile Monitoring System (MMS) has been designed taking into account the use of technology with high sensor accuracy and the ability to be installed easily and quickly in different cardinal locations, distribution spaces, volumes and at different heights of a tertiary in-use building located in Leioa (Bilbao). Two types of MMS have been designed with the objective of carrying out two types of analysis; one intended to do a global indoor air temperature uncertainty analysis and the other focused on doing a global outdoor air temperature uncertainty analysis.Eight tripods make up the interior MMS with twenty sensors at different heights, which have been installed in different offices in the building to collect indoor air temperature measurements at different heights and locations. In addition, eight sensors make up the exterior MMS to collect data from outdoor air temperature measurements around the building envelope. Both MMS have been integrated into the existing Building Automation System (BAS) of the tertiary building; some other data collected by the BAS has also been taken into account for the uncertainty analysis of indoor and outdoor air temperature.The interior and exterior MMS datasets have been compiled based on a rigorous data collection process, with the potential to use the data to study the spatial air temperature behavior, taking into account the impact of solar radiation, the heating system and the electrical energy consumption. Furthermore, it enables the global uncertainty of indoor and outdoor air temperature measurements on an in-use building to be estimated and to break it down into the different uncertainty sources, such as the sensor accuracy, vertical and horizontal temperature variability, solar radiation, occupancy and heating system effects. Finally, it enables the optimization of monitoring and control systems for BAS, heating and HVAC systems, as well as any monitoring system implemented in research tests using indoor and/or outdoor temperature measurements as key variables.

Buildings consume huge amounts of electrical energy in the Kingdom of Saudi Arabia, particularly during the summer months, due to the enormous air conditioning demands created by very hot outdoor temperatures. Residential buildings consume more than half of the electricity used in Saudi Arabia, with the air conditioning load making up 70% of this use. The main aim of this study is to evaluate the thermal performance of two mid-rise residential buildings in Makkah, Saudi Arabia. These buildings are five floors in height and have the same orientation, but the first building is thermally insulated, while the second building is not. To investigate the indoor thermal performance of the two buildings, physical measurements were taken during May 2019. The data gathered included indoor air temperature values as recorded every fifteen minutes for a period of sixty-eight hours in two equivalent rooms in each building. Analysis of site monitoring data was conducted, and the results obtained offer a better idea of the effectiveness of the existing building fabric characteristics, in particular external walls and rooves, in relation to indoor thermal performance. These data were calibrated with simulated results taken from thermal analysis software (TAS) to validate them and to thus quantify the cooling load in the case study buildings. The outcomes illustrate the similarity between the measured and simulated results and as well as indicating that thermal insulation can decrease cooling loads by to up to 50%.

Space heating and cooling inside the building consume a lot of energy. Energy efficient buildings can reduce this energy consumption. Thermal insulators reduce the heat flow into and out of the buildings. Providing air gap and optimizing window area also reduce the cooling load. Present work is the comparison of thermal performance of an uninsulated and an insulated & window area of a house occupied during summer. The indoor air temperature is much lesser for summer season in case of house with insulation on inside and smaller window area as compared to an uninsulated house. The maximum indoor air temperature difference for insulated house as compared to uninsulated house is 5.73°C, 5.65°C, 2.96°C, 3.23°C and 3.08°C for the months from May to September respectively. There are energy savings for an insulated house as compared to an uninsulated house for the summer season. The total energy savings for the summer season are 4514 units. There are money savings also for the insulated house for the summer season. The total money savings for the summer season are Rs. 22574. The carbon dioxide (CO2) emission is also reduced for the insulated house for each month of the summer season. Total reduction in carbon dioxide emission for the summer season is 7078 kg. The thermal performance of an insulated and window area optimized house is better than an uninsulated house. In addition to the better human comfort the insulated house also offers money and energy savings. Also the insulated house is environment friendly.

Due to the non- stationary nature of heat dissipation in the rooms, part of the excess heat is absorbed by internal and external structures, as well as by the equipment of the rooms. The higher the heat capacity of the structures, the more they absorb excess heat, and as a result, the temperature amplitude of the indoor air decreases. Since the surface temperature of the floor differs little from the temperature of the indoor air ( ∆t ≈ 50 C ), low- potential sources are used in the heating process, the internal temperature rises due to them. As the indoor temperature increases, the amount of heat received from the heated floor decreases. This process is called the "self-regulation effect" because it occurs automatically. The smaller the temperature difference between the floor surface and the indoor air is, the higher the self-regulation effect in the system is. [ABSTRACT FROM AUTHOR]

The aim of the present study was to evaluate the relationship between cognitive performance, health and environmental comfort as a function of indoor air temperature (Ta) variation. A total of 360 undergraduate students were subjected to the variation of the Ta at 20, 24 and 30 °C; their thermal responses were evaluated over three consecutive days. Performance variables measured in the study were cognitive performance, blood pressure, heart rate (HR) and comfort. The environmental variables measured were Ta, globe temperature (Tg), illumination, noise, airflow velocity and air quality. The variation in HR was influenced by the variables, relative air humidity and mean radiant temperature (Trm) during the three days of observation, where HR was higher than 100 bpm when Tg was greater than Ta. Trm increased proportionally to the increase in Tg, thus characterising heat exchange by radiation. The number of correct answers and test response time were also positively influenced by Trm when Ta was 20 °C. Teaching environments (TEs) with increased heat load due to the individual body heat of students, increased outdoor Ta and urban morphology associated with the building of the TEs result in increasing in Trm due to the Tg being higher than the air temperature, with possible impacts on health and performance variables. [ABSTRACT FROM AUTHOR]

Buildings consume huge amounts of electrical energy in the Kingdom of Saudi Arabia, particularly during the summer months, due to the enormous air conditioning demands created by very hot outdoor temperatures. Residential buildings consume more than half of the electricity used in Saudi Arabia, with the air conditioning load making up 70% of this use. The main aim of this study is to evaluate the thermal performance of two mid-rise residential buildings in Makkah, Saudi Arabia. These buildings are five floors in height and have the same orientation, but the first building is thermally insulated, while the second building is not. To investigate the indoor thermal performance of the two buildings, physical measurements were taken during May 2019. The data gathered included indoor air temperature values as recorded every fifteen minutes for a period of sixty-eight hours in two equivalent rooms in each building. Analysis of site monitoring data was conducted, and the results obtained offer a better idea of the effectiveness of the existing building fabric characteristics, in particular external walls and rooves, in relation to indoor thermal performance. These data were calibrated with simulated results taken from thermal analysis software (TAS) to validate them and to thus quantify the cooling load in the case study buildings. The outcomes illustrate the similarity between the measured and simulated results and as well as indicating that thermal insulation can decrease cooling loads by to up to 50%. [ABSTRACT FROM AUTHOR]

The determination of the indoor air temperature is necessary for evaluating human comfort, health, and living conditions. Existing measuring methods require entering a room, which can disturb the daily lives of residents and consume large amounts of manpower, material, and financial resources. To overcome these obstacles, an exploratory approach was proposed in this study to estimate the indoor air temperature by obtaining the outdoor building window surface temperature without intrusion using infrared technology. A numerical model was established to describe the heat transfer process between the indoor and outdoor air via window glass. Experiments were conducted in a test room to capture infrared images of the exterior window and measure indoor air temperatures and window surface temperatures under different modes. The estimated indoor air temperatures were compared with the experimental data. The effects of window property parameters and ambient parameters on indoor air temperature estimation were analyzed. Results show that the deviations of the indoor air temperature between estimated and measured values in heating, natural ventilation, and cooling modes varied from −0.7 °C to 0.6 °C, −1.1 °C–0.7 °C, and −0.1 °C–1.3 °C, respectively. Based on the sensitivity analysis, the outer surface temperature of the window outer layer was crucial for estimating the accuracy of the indoor air temperature in practical applications. The proposed exploratory approach provides a potential means for remotely obtaining indoor air temperatures using infrared technology. • An exploratory approach is proposed to remotely estimate indoor air temperature. • A numerical model is established to describe the heat transfer process. • The estimated indoor air temperatures are compared with the measured data. • The effects of different parameters on indoor air temperature are analyzed. [ABSTRACT FROM AUTHOR]

• 50% of the time during night remains below the lowest acceptable temperature, 11 °C. • Total heat loss coefficient per floor area needs to be reduced to 2∼7 W/(m2•K). • The reduction of heat loss coefficient can be achieved by adding insulating materials. • 70% of nocturnal indoor air temperature would be above 11 °C after improvement. After massive earthquake 2015, thousands of Nepalese who lost their permanent houses by the hardest hits were forced to live in makeshift temporary shelters. The field measurement on indoor thermal environment in five shelters was conducted in one of the district hit by the earthquake, Lalitpur, in winter. The mean indoor and outdoor air temperatures during the measured nighttime were found to be 10.3 °C and 7.6 °C, respectively, and the nocturnal indoor air temperature remained below the lowest acceptable temperature of 11 °C. This result assured that these shelters are not good for winter and must create various problems. We therefore analyzed the thermal characteristics of those shelters based on the measured results in order to seek a possible improvement. The total heat loss coefficient estimated per floor area in five shelters ranged from 11.3 to 15.2 W/(m2·K); that is thermal insulation was very low. We made a simple numerical analysis on the variation of indoor air temperature with the assumption of improved thermal characteristics and thereby found that it needs to be reduced about 2∼7 W/(m2·K) to have the indoor air temperature higher than 11 °C for 70% of the whole nocturnal hours. Such reduction of heat loss was found to be realized by adding affordable materials, e.g., cellular polyethylene foam and clothes for respective walls and roof. Thus, the knowledge obtained from this study should hopefully be applied to actual improvement of indoor thermal environment in existing shelters and also to a development for the preparation against future disaster. [ABSTRACT FROM AUTHOR]

Currently, to achieve the optimal thermal comfort for a given occupant, the optimal indoor air temperature should be set by the occupant himself/herself. Then the air conditioner can be used to control the actual indoor air temperature so that it converges to the optimal temperature. In this study, we develop a method for predicting the optimal indoor air temperature; thereby, the air conditioner can be adjusted automatically without the involvement of the occupant. We first apply the predicted mean vote (PMV) model to describe the relationship between the indoor air temperature and the occupant's thermal comfort. We then adopt the deep learning method to obtain two deep neural network models, which are used to predict the optimal indoor air temperature. One is the regression model and the other is the classification model. We test these models and the results show that the mean average error is about 0.1 ?, which satisfies the accuracy requirements of practical systems. [ABSTRACT FROM AUTHOR]

Abstract This paper presents a comprehensive review of the significant studies exploited Artificial Neural Networks (ANNs) in BEA (Building Energy Analysis). To achieve a full coverage of the relevant studies to the scope of the research, a three-decade time span of the publishing date of the existing studies was taken into account. The review focuses on the studies utilized ANN to analyze the energy-related issues associated with buildings in major areas, including modeling of water heating and cooling systems, heating and cooling loads prediction, modeling heating ventilation air conditioning systems, indoor air temperature prediction, and building energy consumption prediction. Moreover, the findings of the abundant reviewed studies along with the potential future research to be carried out are discussed elaborately. Regarding the comprehensive review conducted, it is found out that the majority of studies focused on building energy consumption and indoor air temperature prediction. Additionally, it is observed that there has been a growing interest in the application of newly-developed ANNs to BEA areas, such as general regression neural network and recurrent neural network, due to their abilities in improving the modeling and prediction of buildings energy analysis. It is believed that this thorough review paper is useful for the researchers and scientific engineers working on the application of AI-based techniques to the building-energy-related areas to find out the relevant references and current state of the field. [ABSTRACT FROM AUTHOR]

The capillary ceiling radiation cooling panel (C-CRCP) is a newly developed terminal device for air conditioning to provide indoor thermal comfort for occupants and to save energy. In this paper, numerical modelling of a room with a C-CRCP system was used to analyze radiant heat transfer under non-steady-state conditions. Experiments were conducted in an environmental chamber to verify simulation results. The chamber was equipped with a C-CRCP, which was covered with gypsumboard. The results showed that the inlet water temperature had a significant influence on ceiling surface temperature. The ceiling surface temperature and the steady-state time were increased with higher inlet water temperature. A vertical gradient in indoor air temperature was found. Ceiling surface temperature and indoor air temperature were increased with an increase in inlet air temperature. The energy supply rate of the radiant panel had a positive correlation with chilled water velocity. The amount of radiation was double the amount of convection, and the thickness of the gypsumboard had virtually no effect on the proportions of radiation and convection. The model was considered validated since the maximum relative errors between experimental data and simulation results of inlet water temperature and supply air conditions were within 10%. [ABSTRACT FROM AUTHOR]