Comfort Control Research Articles

Investigating compliance with cow comfort and mastitis control recommendations on mastitis outcomes in smallholder dairy farms in Central Kenya

A randomized controlled trial was conducted in Kenya in 2020 with the objectives being to assess compliance of smallholder dairy farmers (SDF) with farm-specific mastitis and cow comfort recommendations, to determine factors associated with compliance, and to determine the impact of these recommendations in reducing cases of subclinical mastitis.A total of 114 SDFs (124 cows) were recruited into the study and randomly allocated into intervention (74 farms) and control (40 farms) groups during the first farm visit. Existing farm-level mastitis control and cow comfort strategies were assessed in both intervention and control farms. Cow parameters (e.g. udder hygiene) were observed or measured from individual cows. A California Mastitis Test (CMT) was done on all quarters and scored from 0 (negative) to 3 (strong positive). All quarters with a CMT score of 2 and 3 were treated with intramammary antibacterial cefapirin preparation according to the label (Cefalak®). Intervention farms received farm-specific mastitis and cow comfort recommendations at the end of the first visit. On the second farm visit, cow parameters were measured again, CMT was repeated on all quarters on all cows, and compliance with recommendations was assessed and given a percentage score. Control farms were then given their farm-specific recommendations. Regression models were utilized to assess associations with compliance and improvement in CMT scores between visits.Intervention farms received an average of 3.3 and 3.9 recommendations pertaining to mastitis control and cow comfort, respectively. Farms complied with an average of 2.8 and 2.1 mastitis and cow comfort recommendations, respectively, leading to an overall average compliance score of 63.2 % for all recommendations. On the first visit, CMT scores 1 and 2 were significantly higher in quarters among cows on the intervention than control farms. There was a significantly lower proportion of quarters with CMT scores 1 and 2 in the intervention group on the second visit than the first visit and no quarters with CMT score of 3. The principal farmer, type of recommendation and number of recommendations given were significantly associated with compliance to the recommendations. The principal farmer and their age group were significantly associated with quarter CMT scores improving between the first and second visits.Farm-specific mastitis and cow comfort recommendations can significantly reduce the number of cases of subclinical mastitis in SDFs and should thus be included in farmers' training. Farmers should be given a small number of recommendations at a time to enhance compliance.

Towards various occupants with different thermal comfort requirements: A deep reinforcement learning approach combined with a dynamic PMV model for HVAC control in buildings

Reinforcement learning (RL) has great potential in achieving energy-efficient, comfortable and intelligent control of heating, ventilation and air conditioning (HVAC) systems. Although research on RL-based HVAC control has attracted increasing interest, current studies generally use simple building simulation as the environment to train agents, and the definition of thermal comfort is limited to a wide temperature range, which cannot meet the different thermal comfort requirements of various occupants. This study proposes a deep reinforcement learning (DRL) control framework based on the Dueling Deep Q-network (DQN) algorithm, combined with a self-designed environmental model and reward function, for HVAC control meeting different thermal comfort requirements. Specifically, based on the theory of building thermal dynamics, a nonlinear equation modified by experimental data is used for the environmental model that reflects the actual thermal change of building. Different thermal comfort requirements are considered and analysed through a dynamic predicted mean vote (PMV) model that focuses on the metabolic rate and clothing level of occupants. By systematically exploring different heating modes for occupants and control time intervals, the proposed framework demonstrates that heating energy consumption can be reduced by 4.8%-39.58% under various conditions compared to rule-based control. In addition, the study found that the HVAC control based on DRL has greater potential in saving energy when the heating demand of building is higher. Our study is helpful for researchers to make HVAC control more energy-efficient and user-friendly with the help of artificial intelligence.

Research on Ride Comfort Control of Air Suspension Based on Genetic Algorithm Optimized Fuzzy PID

The air suspension system’s superior variable stiffness, low vibration frequency, and resistance to road impacts significantly elevate both the comfort of vehicle occupants and the overall ride quality. By effectively controlling the air suspension system, its superior characteristics can be fully exploited to enhance the overall performance of vehicles. However, the parameter tuning process of the fuzzy PID controller for air suspension involves subjectivity and blindness, which affects the performance of the suspension system. To overcome these shortcomings, a control strategy combining genetic algorithms with fuzzy PID control is proposed. This strategy involves a genetic algorithm-optimized fuzzy PID air suspension control approach specifically targeting the fuzzy PID controller for air suspension. A 1/4 two-degree-of-freedom air suspension fuzzy PID controller is designed in MATLAB 2019a, utilizing genetic algorithms to optimize the PID parameter tuning process. The ride comfort of the fuzzy PID air suspension after tuning is then investigated. In the study of ride comfort on Class B road surfaces, the simulation and experimental results were consistent. Using a genetic algorithm to optimize a fuzzy PID-controlled air suspension resulted in reductions of the root mean square values for vertical body acceleration, suspension deflection, and wheel dynamic load by 30%, 26%, and 9%, respectively, compared to passive suspension. These reductions are further improvements over the corresponding indices controlled by the fuzzy PID alone, which decreased by 23%, 18%, and 6%, respectively. Thus, the control effect of the genetic algorithm-optimized fuzzy PID is superior to that of the fuzzy PID control. This demonstrates that the fuzzy PID control of air suspension optimized by genetic algorithms can further improve the comfort of vehicle occupants and the ride comfort of driving, providing a reference for active control of air suspension systems.

Exercise and resting periods: Thermal comfort dynamics in gym environments

Physical exercise spaces emerged as popular facilities due to recognizing the significance of physical well-being. This study investigates the relationship among physiological responses, human body energy transfer modes, and indoor environmental conditions in influencing thermal comfort perception within indoor physical exercise space. Seven male participants engaged in a 30 min constant-work-rate cycling exercise and a 20 min resting period in a climatic chamber. The physiological and environmental responses were recorded during the experiments, and the body’s energy transfer modes were calculated using the collected data. The dataset was prepared using the 2 min averages of the collected data and calculated parameters across the experiment phases, including the features of skin temperature, core temperature, skin relative humidity, heart rate, oxygen consumption, body’s heat transfer rates through convection, radiation, evaporation, and respiration, net metabolic heat production rate (metabolic rate minus external work rate), indoor air temperature, indoor relative humidity, air velocity, and radiant temperature. Gradient boosting regressor (GBR) was selected as the analyzing method to estimate predicted mean vote (PMV) and thermal sensation vote (TSV) indices during exercise and resting periods using features determined in the study. Thus, the four GBR models were defined as PMV-Exercise, PMV-Resting, TSV-Exercise, and TSV-Resting. In order to optimize the models’ performances, the hyperparameter tuning process was executed using the GridSearchCV method. A permutation feature importance analysis was performed, emphasizing the significance of net metabolic heat production rate (24.2%), radiant temperature (17.0%), and evaporative heat transfer rate (13.1%). According to the results, PMV-Exercise, PMV-Resting, and TSV-Resting GBR models performed better, while TSV-Exercise faced challenges in predicting exercise thermal sensations. Critically, this study addresses the need to understanding the interrelationship among physiological responses, environmental conditions, and human body energy transfer modes during both exercise and resting periods to optimize thermal comfort within indoor exercise spaces. The results of this study contribute to the operation of indoor gym environments to refine their indoor environmental parameters to optimize users’ thermal comfort and well-being. The study is limited to a small sample size consisting solely of male participants, which may restrict the generalizability of the findings. Future research could explore personalized thermal comfort control systems and synergies between comfort optimization and energy efficiency in indoor exercise spaces.

New physiotherapy device for interincisal mouth opening in oral submucous fibrosis patients: A pilot study

Objectives: It is estimated that 0.2–0.5% of Indians suffer from oral submucous fibrosis. The states of Gujarat, Bihar, Madhya Pradesh, and Maharashtra are the most severely impacted by this illness. Antioxidants, effective physiotherapy exercises, and topical corticosteroid injections are among the treatment options available for oral submucous fibrosis (OSMF). Despite being widely used, there are few studies assessing the effectiveness and patient compliance of the conventionally used technique for physiotherapy in patients with OSMF, such as ice cream sticks, tapered screws, and Heister jaw opener. Material and Methods: A device for physical rehabilitation that may be used at home was made using 3D printing technology. Ten patients participated in a pilot study that was carried out. These patients participated in the device’s pilot testing to assess the mechanical features and its ability to improve interincisal mouth opening, comfort (pain and control), and enjoyment. Comfort [Pain, comfort, control] with device use, ease of use [Portability] and satisfaction with device use. Results: A statistically significant (P < 0.0006) improvement in the mean mouth opening of OSMF patients was shown by quantitative data. After 1 month of use, there was a 1.8 mm difference in mouth opening. This implies that the device worked well for these people. Based on feedback, the majority of patients reported that the device was comfortable, easy to use, and showed satisfaction with the device use. Conclusion: It was found that people with OSMF can open their mouth more with the use of a new physiotherapy device. In addition to pharmacological therapy, this pilot study needs more investigation with a bigger sample size and a randomized control trial. Considering that this method of managing OSMF seems to be effective.

Investigation of the free-base Zr-porphyrin MOFs as relative humidity sensors for an indoor setting

Maintaining optimal relative humidity is paramount for human comfort. Therefore, the utilization of quartz crystal microbalance (QCM) as a relative humidity sensor platform holds significant promise due to its cost-effectiveness and high sensitivity. This study explores the efficacy of three free-base Zr porphyrin metal-organic frameworks (MOFs) - namely MOF-525, MOF-545, and NU-902 - as sensitive materials for QCM-based relative humidity sensors. Our extended experimental findings reveal that these materials exhibit notable sensitivity, particularly within relative humidity ranges of 40–100 %. However, we observe potential irreversible adsorption sites within the MOF-545 framework, hindering its ability to revert to its initial state after prolonged exposure. In light of this observation, we conduct periodic cycling experiments at relative humidity levels of 40–70 % to evaluate the measurement repeatability and feasibility of these sensors for indoor applications. Interestingly, the periodic cycling study demonstrates that MOF-545 shows promising repeatability, positioning it as a strong contender for indoor relative humidity sensing. In contrast, MOF-525 may necessitate extended desorption time, and NU-902 displays diminished sensitivity at low relative humidity levels. Nevertheless, a preliminary treatment of the MOF-545 QCM sensor may be necessary to address irreversible adsorption sites and uphold measurement repeatability, as only reversible adsorption sites are currently accessible. This study underscores the potential of MOF-based QCM sensors for effective relative humidity monitoring in indoor environments, thus facilitating improved comfort and environmental control.

Workplace Well-Being and Classroom Space: An Exploratory Survey of K–12 Music Teachers

Abstract Kindergarten through 12th-grade (K–12) music teachers spend much of their professional lives occupying physical workspaces like classrooms and offices. Given that teachers occupy these spaces for extended periods of time, the impact of these spaces on teacher well-being should be considered. This exploratory survey study examined the relationships between K–12 music teachers’ workplace well-being and perceptions of the physical classroom spaces. A questionnaire was completed by 484 music teachers who were members of the National Association for Music Education. The questionnaire included the Workplace PERMA Profiler (a measure of workplace well-being) and the Structural, Physical, and Campus Environment Survey (a measure of teachers’ perceptions of their classrooms). Higher levels of well-being were positively correlated with favorable perceptions of classroom space. Scale and aesthetics, the visual environment, and spatial environment elements were the strongest predictors of well-being. Taking statistical and textual data together, specific features including windows, good natural light, quality and controllable lighting, aesthetically pleasant (e.g., decorations and colors) rooms, ample space and storage, comfortable temperatures, good organization, and control over the classroom space had the largest positive impact on well-being among the participating teachers.

PTC power control of electric vehicle thermal management system based on neural network feedforward

Thermal management system (TMS) plays a crucial role in improving driving range and passenger comfort for electric vehicles (EVs) in winter. However, EVTMS still faces unsolved challenges such as accurate modeling of time-delay, compensating for temperature variations, and achieving efficient control strategies, which are important for enhancing performance, safety, and energy efficiency of EVs. To address above issues, this study builds an integrated EVTMS model and corresponding controllers, aiming to obtain superior battery temperature and cabin comfort control performance through co-simulations. Uniquely, neural networks are set as control feedforward to predict the temperature changes because of their strong nonlinear mapping ability. The results reveal that, NN feedforward can well predict and compensate for the system time-delay and nonlinearity, significantly improving the performance of PI controller and MPC, reducing control overshoot, saving energy and extending component lifespan. The battery temperature and PMV fluctuations can be effectively reduced up to 32.50 % and 30.82 %, and the cabin and battery PTC consumption can be reduced by up to 6.03 % and 6.74 %. The results can provide valuable insights for TMS engineers, aiding in the selection of advanced control strategies.

Photothermal Utility Heating with Diffused Indoor Light via Multiple Transparent Fe3O4@Cu2−xS Thin Films

By introducing a novel photothermal radiator that effectively harnesses diffused light through plasmonic Fe3O4@Cu2−xS nanoparticles, it is sought to offer a sustainable solution for maintaining comfortable indoor temperatures without heavy reliance on traditional solar sources. The approach involves the use of ultraviolet (UV) and infrared (IR) lights to photothermally activate transparent Fe3O4@Cu2−xS thin films, showcasing a proactive strategy to optimize energy capture even in low‐light scenarios such as cloudy days or nighttime hours. This innovative technology carries immense potential for energy‐neutral buildings, paving the way to reduce dependence on external energy grids and promoting a more sustainable future for indoor heating and comfort control. The developed photothermal radiator incorporates multiple transparent thin films infused with plasmonic Fe3O4@Cu2−xS nanoparticles, known for their robust UV and IR absorptions driven by localized surface plasmon resonance. Through the application of UV and IR lights, these thin films efficiently convert incident photons into thermal energy. The experiments within a specially constructed diffused light photothermal box, designed to simulate indoor environments, demonstrate the system's capability to raise temperatures above 50 °C effectively. This pioneering photothermal radiator offers a promising pathway for sustainable heat generation in indoor spaces, harnessing ubiquitous diffused light sources to enhance energy efficiency.

Impact of impinging jet ventilation on thermal comfort and aerosol transmission: A numerical investigation in a densely-occupied classroom with solar effect

Urgent demands for ensuring safe and healthy spaces for children have been raised due to the post-COVID-19 pandemic. This study numerically modelled a densely populated classroom with 40 students and one teacher across two layouts with different relative vent positions to assess the effectiveness of an emerging ventilation scheme, impinging jet ventilation (IJV), in balancing thermal comfort and controlling indoor transmission. The effect of solar radiation was carefully analysed. Cough-induced contaminants were expelled from the infectious teacher and were traced by the Eulerian-Lagrangian approach. The exposure risk of students was further assessed via the inhalation index (ID) based on the spatial characteristics of the released particles. The thermal comfort was evaluated by the Fanger model. The results demonstrated that IJV with Layout 2 (teacher at the supply inlet and exhaust side) was found more optimal in counterbalancing the occupants’ thermal comfort and indoor transmission control. This layout not only aligned better with ASHRAE standards for thermal comfort, showing a reduced vertical thermal gradient but also lowered student exposure risk by 18.9%–135.2%. With solar radiation, heat absorbed by the windows led to non-uniform wall temperature distributions, causing the air near the windows to also exhibit a non-uniform profile. This interaction with the cooler air from the IJV system was found to facilitate contaminant dispersion. This resulted in a 10.3% to 26% increase in students’ average exposure risk compared to those without solar radiation cases. This study aimed to investigate the practical applications of IJV system for densely-occupied classrooms.

Online model adaptation in Monte Carlo tree search planning

AbstractWe propose a model-based reinforcement learning method using Monte Carlo Tree Search planning. The approach assumes a black-box approximated model of the environment developed by an expert using any kind of modeling framework and it improves the model as new information from the environment is collected. This is crucial in real-world applications, since having a complete knowledge of complex environments is impractical. The expert’s model is first translated into a neural network and then it is updated periodically using data, i.e., state-action-next-state triplets, collected from the real environment. We propose three different methods to integrate data acquired from the environment with prior knowledge provided by the expert and we evaluate our approach on a domain concerning air quality and thermal comfort control in smart buildings. We compare the three proposed versions with standard Monte Carlo Tree Search planning using the expert’s model (without adaptation), Proximal Policy Optimization (a popular model-free DRL approach) and Stochastic Lower Bounds Optimization (a popular model-based DRL approach). Results show that our approach achieves the best results, outperforming all analyzed competitors.

Real-time spatial-temporal mapping and visualization of thermal comfort and HVAC control by integrating immersive augmented reality technologies and IoT-enabled wireless sensor networks: Towards immersive human-building interactions

Existing methods to assess and visualize thermal comfort mainly focused on either individual users (e.g., through wearables) or for specific locations where sensors are installed, rather than continuously for the entire indoor space. Also, most of the existing thermal comfort visualization methods and HVAC system control approaches rely on phones and mobile devices and lack a fully immersive/interactive experience and human-building interaction. Thus, this paper develops an immersive human-building interaction framework for real-time spatial-temporal mapping and visualization of indoor thermal comfort by integrating immersive augmented reality (AR) technologies and IoT-enabled smart wireless sensor networks (WSNs) (i.e., temperature and relative humidity data) to provide thermal comfort assessment for the entire space (rather than at localized/individual locations/points) and to enable users to interact with and adjust the HVAC systems from a distance using fully immersive and realistic environments. First, a smart WSN comprised of four IoT-enabled sensing stations was developed to collect real-time thermal comfort-related information (i.e., temperature and relative humidity). Second, an AR environment was created using the Unity engine, where virtual objects were overlayed into, and aligned with, the real-world environment. Third, the real-time data from the IoT-enabled WSN was integrated into the AR environment using various programming scripts that add multiple functionalities to the users, including controlling the HVAC system and visualizing the thermal comfort along the entire indoor space or room of interest through a spatial-temporal map. Fourth, the developed framework was validated and evaluated by 118 participants using a survey questionnaire. The findings showed that the graphical satisfaction achieved an overall average score of 4.75 out of 5, the user's sense of spatial presence was rated at an average score of 4.68 out of 5, the involvement aspects of the developed approach received an overall average score of 4.80 out of 5, and experienced realism was rated at an average score of 4.58 out of 5. Ultimately, this paper contributes to enhanced and intelligent human-building interaction and visualization through the use of emerging technologies by allowing the building users to control and interact with the HVAC systems using AR capabilities to improve the health and well-being of the building occupants.

Investigating the effect of lower body local radiant warming on occupant thermal comfort in battery electric vehicles during cold conditions

The wintertime decrease in the driving range of battery electric vehicles (BEVs), partially attributed to cabin heating with improper setpoints, necessitates the improvement of occupant thermal comfort with reduced energy consumption. While local warming shows promise, understanding its impact on occupant comfort with thermal transients from cabin heating, ventilation, and air conditioning (HVAC) is limited. To address this gap, we investigated thermal perception in twenty-five participants under three distinct warming modes within BEVs operating through typical winter conditions: HVAC alone, HVAC with continuous lower body local radiant warming (LRW), and HVAC with periodic lower body LRW. Results show that the rapid change in skin temperature from lower body local warming does not immediately improve comfort. While a significant improvement in comfort (p < 0.05) is observed with lower body warming, it becomes evident only when the cabin transients subside. Consequently, we propose a framework that advocates for reducing HVAC setpoint and control of LRW of the lower body based on occupant preferences. Our feasibility study demonstrates a 45.3 % reduction in energy consumption by reducing the HVAC setpoint from 25 to 18 °C. These findings enhance the prospects for efficient thermal comfort management in BEVs.

A decentralized federated learning based interoperable and heterogeneity aware predictive optimization method for energy and comfort in smart homes environment

It is a known fact that a comfortable residential space or workspace is crucial to productivity and well-being. In smart home environments, sensors can be used to sense environment and occupant data and actuators can be controlled accordingly for maximizing thermal comfort. However, keeping in view the growing demand of energy and scarcity of energy generation sources, it is mandatory to optimize the energy usage complementary to optimize thermal comfort. In order for the developed method to be interoperable and heterogeneity aware across different edge device vendors and for ease of hardware connectivity in smart homes, a REST API based framework is developed. Sensors mounted with IoT devices collect data from its respective smart home environment and store the data in its local database. An edge server is used to control the connectivity among these IoT devices and to initiate the decentralized federated learning mechanism. Each IoT device train local deep learning models for predicting energy usage, indoor temperature, indoor humidity for maintaining a comfortable environment state using minimal energy. Models from each IoT devices are aggregated at each IoT device iteratively to make federated learning independent of central server. Global deep learning model is iteratively trained for each prediction requirement. Later, these models are used to predict indoor temperature, humidity and energy consumption for time stamp (t+1), keeping in view environmental and occupant related sensed data at time stamp (t). Optimization of energy consumption and thermal comfort is mathematically modeled and based on the predictions of DL models, environment is controlled by handling actuators using a particle swarm optimization (PSO) mechanism. Relevant constraints are incorporated in PSO for limiting the positional variations in particles for efficient learning. The method continually learns and adapts to changes in the surroundings, providing real-time monitoring and control of thermal comfort to the end users. To validate our approach, data is collected and experiments are conducted in a real-time smart home’s environment. The results are compared with those obtained without using the proposed predictive optimization mechanism. The study demonstrates the effectiveness of proposed predictive optimization mechanism in providing a comfortable and productive smart home or workspace environment utilizing 38% less energy as compared to competitor.

Indoor quality-oriented approach for the performance evaluation of building retrofit with façade transformation: Case study of student dormitory in Mediterranean climate

The rapid expanding of refurbishment incentives requires to deserve more attention on how to reach nearly zero energy standard without compromising occupant wellness, when the building is undergone to important architectural transformations. To overcome the above drawbacks mainly for student dormitories, the paper introduces a new indoor quality oriented post-retrofit evaluation approach that simultaneously quantifies the impact of design decisions on thermal, respiratory and visual comfort. Investigations on the building quality and readily monitored indoor variables are the core of the approach that also provides sensitivity analysis on some subjective parameters and questionnaires for the main involved stakeholders.The proposed approach is tested on a student dormitory belonging at National and Kapodistrian University of Athens, refurbished in the frame of Horizon Project Pro-GET-OnE. The results reveal an improvement in the passive control of thermo-hygrometric comfort as well as in the satisfaction level. The adoption of mechanical ventilation consistently ensures dioxide emissions lower than 430 ppm, TVOC below 300 μg/m3 and PM2.5 and PM10 lower than 6 μg/m3. More in general, it is remarkable the importance of considering all comfort domains and occupant behavior, as one design or management choice might improve one indoor quality domain at the cost of others.

A Systematic Review on the Use of AI for Energy Efficiency and Indoor Environmental Quality in Buildings

Global warming, climate change and the energy crisis are trending topics around the world, especially within the energy sector. The rising cost of energy, greenhouse gas (GHG) emissions and global temperatures stem from the over-reliance on fossil fuel as the major energy resource. These challenges have highlighted the need for alternative energy resources and urgent intervention strategies like energy consumption reduction and improving energy efficiency. The heating, ventilation, and air-conditioning (HVAC) system in a building accounts for about 70% of energy consumption, and a decision to reduce energy consumption may impact the indoor environmental quality (IEQ) of the building. It is important to adequately balance the tradeoff between IEQ and energy management. Artificial intelligence (AI)-based solutions are being explored for improving building energy performance without compromising IEQ. This paper systematically reviews recent studies on AI and machine learning (ML) for building energy management and IEQ by exploring common use areas, the methods or algorithms applied and the results obtained. The overall purpose of this research is to add to the existing body of work and to highlight energy-related AI applications in buildings and the related gaps. The result shows five common application areas: thermal comfort and indoor air quality (IAQ) control; energy management and energy consumption prediction; indoor temperature prediction; anomaly detection; and HVAC controls. Gaps involving policy, real-life scenario applications, and insufficient study of the visual and acoustic comfort areas are also identified. Very few studies take into consideration the need to follow IEQ standards in the selection process and positioning of sensors in AI applications for IEQ in buildings. This study reveals a need for more systematically summarized research.

An intelligent thermal comfort control strategy for air conditioning of fuel cell vehicles

Thermal comfort is an important indicator for evaluating vehicle air conditioning, with many influencing factors, but traditional vehicle air conditioning control only take temperature as the control target, neglecting the passenger's actual thermal comfort situation, which cannot automatically adapt to the thermal comfort needs of different passengers. To address this issue, this paper establishes an air conditioning model based on a fuel cell vehicle (FCV) and proposes a control strategy based on thermal comfort, using deep reinforcement learning based on PPO algorithm to establish the control model. Which can automatically control the compressor and blower, this paper conducts simulation analysis under four different environmental conditions which including static and dynamic situations. The results show that when using the intelligent thermal comfort control strategy, the overall thermal sensation values of passengers are 0.006, 0.005, 0.033, and 0.102, respectively; which are 0.088, 0.333, 0.093, and 0.120 lower than the temperature-based control strategy, this proves that the control strategy proposed in this paper can achieve better thermal comfort for passengers.

Model predictive control for thermal comfort and energy optimization of an air handling unit system in airport terminals using occupant feedback

The dynamic fluctuations in occupant flow within airport terminals contribute to delays in the system's response under traditional feedback control. This imbalance between supply and demand can result in discomfort and unnecessary energy consumption. While Occupant-Based Model Predictive Control (OBMPC) has gained research interest as it leverages occupant forecasts to enhance control performance, there is limited knowledge regarding its application in the air-conditioning systems of airport terminals, mainly due to the intricate dynamics of occupant flow in terminal buildings. In this study, we proposed a model predictive control strategy based on dynamic occupant flow with the goal of fast responding to occupant flow and reducing energy consumption. We integrated mathematical formulas and the Anylogic simulation software to predict occupant variations in the baggage claim hall. The model predictive control strategy of the air conditioning system was proposed with the predicted occupant flow. The proposed strategy was evaluated throughout the whole cooling season in the validated simulation model of the air handling unit system of the baggage claim hall at an airport terminal. The results demonstrated that the proposed strategy could effectively respond to variations in occupancy and achieve 10% energy savings throughout the entire cooling season, compared to conventional feedback control. Our work presents a viable solution to address system regulation lag and reduce energy consumption without jeopardizing thermal comfort.

Reconfigurable supply-based feedback control for enhanced energy flexibility of air-conditioning systems facilitating grid-interactive buildings

Air-conditioning systems have great potential to provide energy flexibility services to the power grids of high-renewable penetration, due to their high power consumption and inherent energy flexibilities. Direct load control by switching off some operating chillers is the simplest and effective means for air-conditioning systems in buildings to respond to urgent power reduction requests of power grids. However, the implementation of this approach in today's buildings, which widely adopt demand-based feedback controls, would result in serious problems including disordered cooling distribution and likely extra energy consumption. This study, therefore, proposes a reconfigurable control strategy to address these problems. This strategy consists of supply-based feedback control, incorporated with the conventional demand-based feedback control, a control loop reconfiguration scheme and a setpoint reset scheme, facilitating effective control under limited cooling supply and smooth transition between supply-based and demand-based feedback control modes. The proposed control strategy is deployed in a commonly-used digital controller to conduct hardware-in-the-loop control tests on an air-conditioning system involving six AHUs. Test results show that the reconfigurable control achieves commendable control performance. Proper chilled water distribution enables even thermal comfort control among the building zones during demand response and rebound periods. Temperature deviation of the building zones is controlled below 0.2 K most of the time. 11.6 % and 27 % of power demand reductions are achieved during demand response and rebound periods respectively, using the proposed reconfigurable control compared with that using conventional controls.

Microclimate-Monitoring: Examining the Indoor Environment of Greek Museums and Historical Buildings in the Face of Climate Change

The preservation of cultural artifacts within museums and historical buildings requires control of microclimatic conditions, and the constantly evolving climate certainly poses a challenge to maintaining recommended conditions. Focused on the Archaeological Museum of Delphi and the Church of Acheiropoietos in Greece, our study evaluates the hygrothermal behavior of these buildings with a specific emphasis on the preservation of cultural heritage objects hosted there. An innovative approach to the real-time analysis of data is utilized, aiming to achieve a timely detection of extreme temperature and humidity levels. A one-year monitoring campaign was carried out to achieve a detailed assessment of the indoor climate in selected museums and historical buildings in Greece. The monitoring campaign was performed using dataloggers that were set to measure and record temperature (T) and relative humidity (RH) values hourly. The results allowed for the detection of extreme temperature and relative humidity values, pinpointing the time period that requires more attention. The museum’s heating, ventilation, and air conditioning (HVAC) systems provide temperature control for visitor comfort, but the temperature still rises in summer, highlighting the impact of external climate factors. The church’s lack of HVAC systems widens the temperature range compared to the museum, but significant hourly fluctuations are not observed, underlining the building’s high thermal mass and inertia. Both buildings demonstrate a significant response to changes in outdoor temperature, emphasizing the need for future adaptation to climate change. The HMRhs and PRD indices indicate minimal microclimate risk in both buildings for temperature and RH, reducing the probability of material damage. The church’s slightly higher HMRhs index values, attributed to relative humidity, increases susceptibility due to sensitive materials. Overall, the study highlights the importance of managing microclimatic conditions in historical buildings and proposes careful adaptations for the protection of cultural heritage.