Search Results (48)

With the continuous expansion of data center scales worldwide, the problem of energy consumption has become increasingly prominent. To address the multi-parameter control challenge in environmental temperature regulation for large data center computer rooms, achieve precise control of hot-aisle temperatures in data centers, and reduce energy waste, this paper designs a multi-parameter model-free adaptive control (MMFAC) algorithm suitable for computer room environmental temperatures. The algorithm integrates the model-free adaptive control (MFAC) algorithm with a weight matrix to perform scaling transformations. Considering the large parameter space of the MFAC controller and the dynamic complexity of data center temperature control systems, compact-form dynamic linearization (CFDL) technology and optimization mathematical methods are used to simplify the parameter identification of the pseudo-Jacobian matrices and the calculation of control quantities for the regulation devices. Simulation experiments based on measured data from a data center show that the proposed algorithm can calculate control quantities for equipment such as air conditioners according to real-time environmental parameter measurements and drive each device based on these control quantities. Meanwhile, the algorithm can reduce errors in key parameters by adjusting the weight matrix. Comparative tests with other control algorithms show that the algorithm has faster response in temperature control and smaller control errors, verifying the effectiveness and application prospects of the algorithm in data center temperature control. Full article

This study addresses the issue of excessive cooling in data center server rooms caused by the sparse deployment of server cabinets. A precise air-conditioning control strategy based on the working temperature response of target cabinets is proposed. CFD software is used to establish the server room model and set control objectives. The simulations reveal that, under the condition of ensuring normal operation and equipment safety in the data center, the supply air temperature of the CRAC (computer room air conditioner) system can be adjusted to provide more flexibility, thereby reducing energy consumption. Based on this strategy, the dynamic load of the server room is simulated to obtain the supply air temperature of the CRAC system, forming a simulation dataset. A graph structure is created based on the distribution characteristics of the servers, and a regression prediction model for the supply air temperature of the CRAC system is trained using graph neural networks. The results show that, in the test set, 95.8% of the predicted supply air temperature errors are less than 0.5 °C, meeting ASHRAE standards. The model can be used to optimize the parameter settings of CRAC systems under real load conditions, reducing local hotspots in the server room while achieving energy-saving effects. Full article

This research has developed a heat stroke warning system prototype for athletes utilizing the following sensors: DHT22, GY-906-BAA MLX90614, MAX30102. The device calculates the heat stroke risk and notifies users. The data is recorded, stored, displayed on a free-access website which graphs body temperature, ambient temperature, humidity, heart rate and heat stroke risk, and provides notifications for athletes engaged in outdoor activities. The researchers recorded sensors data (n = 1) for two sessions (12 min/session) in a closed room, at the sixth-minute marker, with an air conditioner activated to observe the changes observed by the sensors. For accuracy, the researchers employed Criterion-Related Validity, comparing sensor against standard equipment measurement. For reliability, we utilized Test-Retest Reliability, comparing sensor data from the first and second measurements. Accuracy and reliability were evaluated using the Pearson Correlation Coefficient, with significance set at p < 0.01. The DHT22 sensor demonstrates very high accuracy (r = 0.923) in ambient temperature and (r = 0.774) humidity measurements. It showed no significant reliability (r = 0.489) in temperature and (r = 0.185) humidity measurements. The GY-906-BAA MLX90614 sensor exhibited very high accuracy (r = 0.923) and reliability (r = 0.866) in body temperature measurements. The MAX30102 sensor lacked significant accuracy (r = 0.179) and reliability (r = 0.171) in heart rate measurements. The development of accuracy and reliability of sensors are important for preventing heat stroke in future applications. Full article

Climate change is one of the biggest challenges today. An increasing population accelerates the construction of concrete houses and the use of air conditioners, thereby leading to an increase in energy consumption. When the walls of buildings are well-designed and insulated, energy consumption can be reduced. Therefore, it is important to measure the thermal performance of wall systems accurately. The existing traditional methods of measuring R- and U-values provide acceptable solutions for steady-state controlled, uncontrolled or transient state-controlled conditions. However, a need to develop a novel approach for transient state-uncontrolled realistic conditions has been identified. The present study involves both experimental and numerical investigations. An in situ model room with dimensions of 1.60 m × 1.73 m × 1.50 m was built for the experimental work, and a series of experiments were conducted. For numerical work, two models using Ansys Fluent 2021/2022 and MATLAB Simulink 2021/2022 were developed. The real-time experimental data were fed into numerical models to predict the thermal behavior of the wall system. The results include the evaluation of a concept called ‘Time-Lag’ for all three models. ‘Time-Lag’ is the time taken for the heat energy to flow across the wall system. The Time-Lag for the experimental model was 8 h 45 min, while for MATLAB and Ansys models, it was 8 h 22 min. (average) and 7 h 30 min, respectively. Minor variations validate the accuracy of the numerical models. Further, a novel method using a new parameter in building systems called ‘thermal impedance Z-value’ was developed to estimate the real-time thermal performance of walls using MATLAB Simulink. The Z-value measures the ability of a wall system to resist the flow of heat (thermal resistance, R-value) combined with its ability to store heat energy (thermal capacitance, C_th-value). It is evaluated for steady-state and dynamic (transient) systems. For the steady-state system, the Z-values on the outer and inner walls were 18.2683 K/W and 18.6761 K/W, respectively with a minor difference of 0.4078 K/W at the end of 72 h. For the dynamic system, the Z-value did not reach a constant value and fluctuated in a particular pattern during 24 h of the solar cycle with average values of 3.2969 K/W on the outer and 1.2886 K/W on the inner walls at the end of 72 h, thus presenting more accurate and realistic thermal performance results of a wall system. Full article

In this study, individual control of a four-way air conditioner was developed based on the distribution of occupants to prevent unnecessary energy consumption during room-wide control. An occupancy detection algorithm was created in Python using YOLOv5 object recognition technology to identify the occupants’ distribution in space. Recorded video data were used to test the algorithm. A simulation case study for a building energy model was conducted, assuming that this algorithm was applied using surveillance cameras in commercial buildings, such as cafés and restaurants. A grey-box model was established based on measurements in a thermal zone, dividing one space into two zones. The temperature data for the two zones were collected by individually turning on the air conditioner for each zone in turns for a specific period. Manual closure was applied to each supply blade using a tape to provide cooling to the target zone. Finally, through energy simulations, the decreased rates in energy consumption between the proposed individual control and existing room-wide controls were compared. Different scenarios for the occupants’ schedules were considered, and average rates in energy savings of 21–22% were observed, demonstrating the significance of individual control in terms of energy consumption. However, marginal comfort violations were observed, which is inevitable. The developed control method is expected to contribute to sustainable energy management in buildings. Full article

The heat rejected by outdoor units of split A/C conditioners can impact the ambient outdoor environment of mixed-mode buildings. Nevertheless, how this environmental impact may affect the space-conditioning energy use and indoor air pollution is poorly understood. By coupling EnergyPlus and Fluent, this study examines the effects of outdoor units’ heat rejection on the building surroundings, building cooling load, and indoor PM_2.5 exposure of a six-storey mixed-mode building. The building had an open-plan room on each floor, with the outdoor unit positioned below the window. The coupled model was run for a selected day when the building was cooled by air conditioning and natural ventilation. Five mixed-mode cooling strategies were simulated, reflecting different window-opening schedules, airflow rates of outdoor units, and cooling set-points. The results indicate that compared with the always-air-conditioned mode, the mixed-mode operation could significantly mitigate the negative impact of heat rejection on space-cooling energy consumption. Increasing the airflow rate of outdoor units led to a lower increase in demand for space cooling and lower indoor PM_2.5 exposure. If one of the six rooms needs to be cooled to a lower temperature than the others; choosing the bottom-floor room helped achieve more energy savings and better indoor air quality. Full article

One of the most energy-intensive facilities requiring a comprehensive and well-optimised cooling system is the data centre. Air containment across the data centre is a key thermal management and energy-saving strategy that enhances the performance of data centres. The majority of modern energy-efficient data centres use some type of air containment. The primary advantage of aisle separation and containment is the decrease in the air temperature at the server inlet by reducing the mixing of hot air with cold air. In order to ascertain the volume of literature relating to corridor insulation, we conducted a literature review. Currently, there have been numerous articles regarding the application of computational fluid dynamics (CFD) analysis, however, publications delineating the integration of building information modelling (BIM) principles for corridor separation are still limited. Research specifically targeting data centre corridor insulation is somewhat limited. As a result of this analysis, the most common methods used to isolate hot or cold aisles within a data centre were identified. To determine the most effective type of corridor insulation, the BIM family was created in Autodesk Revit. The model includes 15 telecom cabinets containing information technology (IT) equipment, eight inter-row air conditioners, and one UPS. The model was used for the CFD analysis of the air temperature in different zones of the room. Visualisation of the results using gradient temperature distributions at different levels provides a complete picture of the microclimate formation in the room and allowed the advantage of the hot aisle isolation scheme to be demonstrated. Full article

Studies show that people can tolerate elevated temperatures in the presence of appreciable air movement (e.g., from using ceiling fans). This minimises the use of air-conditioners and extends their set-point temperature ($T_{set}$), resulting in energy savings in space cooling. However, there is little empirical evidence on the energy savings from using ceiling fans with Room Air-Conditioners (RACs). To address this gap, we analysed the energy performance of RACs with both fixed-speed compressors and inverter technology at different set-point temperatures and ceiling fan speed settings in 15 residential Mixed-Mode Buildings (MMBs) in India. Thermal comfort conditions (as predicted by the Indian Model for Adaptive Comfort-Residential (IMAC-R)) with minimum energy consumption were maintained at a set-point temperature ($T_{set}$) of 28 and 30 ${}^{\circ }$C and a fan speed setting of one. Compared with a $T_{set}$ of 24 ${}^{°}$C, a $T_{set}$ of 28 and 30 ${}^{°}$C resulted in energy savings of 44 and 67%, respectively. With the use of RACs, a configuration with a minimum fan speed was satisfactory for an optimal use of energy and for maintaining the conditions of thermal comfort. In addition, RACs with inverter technology used 34–68% less energy than fixed-speed compressors. With the rising use of RACs, particularly in tropical regions, the study’s outcomes offer a significant potential for reducing space-cooling energy consumption and the resultant greenhouse gas (GHG) emissions. Full article

The comprehensive and accurate assessment of the indoor air quality (IAQ) in large spaces, such as offices or multipurpose facilities, is essential for IAQ management. It is widely recognized that various IAQ factors affect the well-being, health, and productivity of indoor occupants. In indoor environments, it is important to assess the IAQ in places where it is difficult to install sensors due to space constraints. Spatial interpolation is a technique that uses sample values of known points to predict the values of other unknown points. Unlike in outdoor environments, spatial interpolation is difficult in large indoor spaces due to various constraints, such as being separated into rooms by walls or having facilities such as air conditioners or heaters installed. Therefore, it is necessary to identify independent or related regions in indoor spaces and to utilize them for spatial interpolation. In this paper, we propose a spatial interpolation technique that groups points with similar characteristics in indoor spaces and utilizes the characteristics of these groups for spatial interpolation. We integrated the IAQ data collected from multiple locations within an office space and subsequently conducted a comparative experiment to assess the accuracy of our proposed method in comparison to commonly used approaches, such as inverse distance weighting (IDW), kriging, natural neighbor interpolation, and the radial basis function (RBF). Additionally, we performed experiments using the publicly available Intel Lab dataset. The experimental results demonstrate that our proposed scheme outperformed the existing methods. The experimental results show that the proposed method was able to obtain better predictions by reflecting the characteristics of regions with similar characteristics within the indoor space. Full article

Due to energy constraints and people’s increasing requirements for indoor thermal comfort, improving energy efficiency while ensuring thermal comfort has become the focus of research in the design and operation of HVAC systems. This study took office rooms with few people occupying them in Wuhan as the research object. The EnergyPlus-Fluent co-simulation method was used to study the impact of 12 forms of air distribution on the thermal environment and air-conditioner energy consumption. The results indicate that 3 m/s supply air velocity and 45° supply air angle are more suitable for the case model in this study. The EnergyPlus-Fluent co-simulation method used in this paper provides a reference for the study of indoor environments in offices with few people occupying them. Full article

The Executive Yuan in Taiwan plans to install air-conditioning (A/C) in all elementary schools within two years. However, besides the associated energy consumption and environmental issues, the use of A/C will inevitably result in the doors and windows of the classroom being closed, which will increase the accumulation of carbon dioxide (CO₂) within the classroom. An excessive indoor CO₂ concentration can result in reduced cognitive performance and an impaired learning efficiency. Therefore, the moderate introduction of external air into the classroom is essential to increase the air exchange rate (AER) and reduce the CO₂ concentration level. Accordingly, the present study conducts a numerical investigation into the effects of various A/C operation strategies on the CO₂ concentration within the classroom given different proportions of students remaining in the classroom during the recess. Overall, the results indicate that the optimal usage strategy is to operate the A/C over the full school day (08:00~15:50 p.m.) in conjunction with a mechanical ventilation system providing a fresh air exchange rate of 5 l/s for every person in the room. However, the use of a mechanical ventilation system inevitably incurs an additional hardware and energy consumption. Thus, an alternative recommendation is also proposed, in which the windows are opened and the air conditioner is turned off at every recess and during the lunchtime period. It is shown that the resulting CO₂ concentration in the classroom is still consistent with the Taiwan Environmental Protection Administration (EPA) regulations and the thermal comfort of the students is achieved for more than three-quarters of the school day. Full article

The use of an air conditioner (AC) becomes essential, particularly in a hot and humid climate, to provide a comfortable environment for human activities. The setpoint is the agreed temperature that the building will meet, and the use of the lowest setpoint temperature to accelerate the cooling of indoor spaces should be avoided. A comprehensive field study was conducted under various cooling temperature settings in two student activity rooms in a university building in Malaysia, so as to understand respondents’ characteristics and behavior toward AC usage, to estimate the comfort at various indoor temperatures, to develop an adaptive model of thermal comfort in AC spaces, and to compare the comfort temperature with related local and international indoor thermal environmental standards. The findings indicated that water intake and clothing insulation affected personal thermal comfort. Moreover, the mean comfort temperature for respondents was 24.3 °C, which is within an indoor thermal comfort zone of 23–27 °C. The findings suggest that the preference of occupants living in a hot and humid region for lower temperatures means that setting temperatures lower than 24 °C might underestimate the indoor comfort temperature. Additionally, an adaptive relationship can be derived to estimate the indoor comfort temperature from the prevailing outdoor temperature. Full article

This study measured ventilation volumes and particle concentrations in indoor environments with open windows and doors. In addition, the effect of the airflow mode of the air conditioner on the ventilation volume and indoor particle concentration variations was also measured. The ventilation fan could only provide approximately 43% of the ventilation volume during the design phase. The amount of ventilation differed depending on the opening area in windows and doors. The ventilation volume was increased by opening multiple windows or doors, even when the area of the opening was the same. No significant change in the ventilation rate was observed, although the air conditioner was expected to promote the ventilation rate in the room when set on blow mode. It was confirmed that both 0.3 and 1 μm particles could enter through the gaps around the windows and doors. Although most of the 5 μm particles were from the outdoor air, when the air conditioner was operated in airflow mode, the removal of 5 μm particles was performed by the air conditioner filter. The use of medium-performance or HEPA filters is expected to remove smaller particulates. Full article

In recent years, the increasing winter load peak has brought great pressure on the operation of power grids. The demand response on the load side helps to alleviate the expansion of the power grid and promote the consumption of renewable energy. However, the response of large-scale electric heat loads to the same electricity price curve will lead to new load peaks and regulation failure. This paper proposes a grouping coordinated preheating framework based on a demand response model, which realizes the interaction of information between the central controller and each regulation group. The room thermal parameter model and the performance map of the inverter air conditioner/heat pump are integrated into the demand response model. In this framework, the coordination mechanism is adopted to avoid regulation failure, an edge computing structure is applied to consider the users’ preferences and plans, the grouping and parallel computing structure is proposed to improve the computing efficiency. Users optimize their heat load curves based on a demand response model, which can consider travel planning and ensure user comfort. The central controller updates the marginal cost curve based on the predicted scenario set to coordinate the regulation groups and suppress the new peaks. The simulation results show that the proposed method can promote the consumption of renewable energy through coordinated preheating and reduce the system energy consumption cost and user bills. The parallel computing structure within the regulation group also ensures the computing efficiency under large-scale loads. Full article

Automation can now be found in nearly every industry. However, home automation has yet to reach Pakistan. This paper presents an Internet of Things smart kitchen project that includes automation and monitoring. In this project, a system was developed that automatically detects the kitchen temperature. It also monitors the humidity level in the kitchen. This system includes built-in gas detection sensors that detect any gas leaks in the kitchen and notify the user if the gas pressure in the kitchen exceeds a certain level. This system also allows the user to remotely control appliances such as freezers, ovens, and air conditioners using a mobile phone. The user can control gas levels using their phone with this system. In this paper, the ESP32, DHT11 Sensor, 5 V Relay X 8, and MQ-135 gas sensors create a smart kitchen by controlling the temperature, managing humidity, and detecting gas leakage. The system was built on an Arduino board that is connected to the Internet. The hardware was integrated and programmed using an Arduino, and a user Android application was developed. The project’s goal is to allow any Android smartphone to remotely control devices. This method is commonly used in homes, businesses, and grocery stores. Users will be able to control all of their instruments from anywhere, including switches, fans, and lights. Furthermore, simulation was performed using Matlab2016b on multiple houses. In the simulation, not only was the kitchen considered, but also two, four, and six houses. Each house has two bedrooms, one living room, one guest room, two bathrooms, and one kitchen. The results revealed that using this system will have a scientifically significant impact on electricity consumption and cost. In the case of the houses, the cost was USD 33.32, 32.64, 22.32, and 19.54 for unscheduled, two, four, and six houses, respectively. Thus, it was observed that the cost and power are directly proportional to each other. The results reveal that the proposed solution efficiently reduces the cost as compared to that of unscheduled houses. Full article