Search Results (41)

This work presents a study on the calculation of transmittance in an air handling unit (AHU) through three methods. A semi-empirical estimation based on simplified models of heat and mass transfer has been used. In addition, experimental tests were carried out in a real AHU under controlled conditions. The measured temperature inside and outside the AHU were used to calculate the transmittance. Finally, numerical simulations were performed on specific sections of the AHU and on a global model, with and without radiation. The simulations provided detailed results on the flow behavior and temperature distribution. The results were compared and analyzed to assess the accuracy and applicability of the three methods. The heat transfer obtained with the semi-empirical method is 38% larger than that obtained with the experimental measurement, in contrast with the 8% of difference observed with numerical simulations. It is revealed that radiation, and thus the emissivity of surfaces, plays an important role in heat transfer of the AHU. This research contributes to the knowledge and understanding of transmittance in AHUs, providing valuable information for the design and optimization of heating, ventilation, and air conditioning (HVAC) systems. Full article

The air-handling unit (AHU) is an essential component of heating, ventilation, and air-conditioning (HVAC) systems. Hence, detecting the faults in AHUs is essential for maintaining continuous HVAC operation and preventing system breakdowns. The advent of artificial intelligence has transformed the AHU fault diagnosis techniques. Specifically, deep learning has obviated the necessity for manual feature extraction and selection, thereby streamlining the fault diagnosis process. While conventional convolutional neural networks (CNNs) effectively detect defects, incorporating more spatial variables could enhance their performance further. This paper presents a hybrid architecture combining a CNN model with a long short-term memory (LSTM) model to diagnose the faults in AHUs. The advantages of the LSTM model and convolutional layers are combined to identify significant patterns in the input data, which considerably facilitates the detection of AHU defects. The hybrid design enhances the network’s capability to capture both local and global characteristics, thus improving its ability to differentiate between normal and abnormal circumstances. The proposed approach achieves strong diagnostic accuracy, exhibiting high sensitivity to nuanced fault patterns. Furthermore, its efficacy is corroborated through comparisons with state-of-the-art AHU fault identification techniques. Full article

Heating systems in a building’s mechanical infrastructure account for a significant share of global building energy consumption, underscoring the need for improved efficiency. This study evaluates 31 predictive models—including neural networks, gradient boosting (XGBoost), bagging, and multiple linear regression (MLR) as a baseline—to estimate heating-coil performance. Experiments were conducted on a water-based air-handling unit (AHU), and the dataset was cleaned to eliminate illogical and missing values before training and validation. Among the evaluated models, neural networks, gradient boosting, and bagging demonstrated superior accuracy across various error metrics. Bagging offered the best balance between outlier robustness and pattern recognition, while neural networks showed strong capability in capturing complex relationships. An input-importance analysis further identified key variables influencing model predictions. Future work should focus on refining these modeling techniques and expanding their application to other HVAC components to improve adaptability and efficiency. Full article

A meticulous thermo-hygrometric control is essential for various industrial production processes, particularly those involving the painting phases of body-in-white, in which the air temperature and relative humidity in production boots must be limited in strict intervals to ensure the high quality of the final product. However, traditional proportional integrative derivative (PID) controllers may result in non-optimal control strategies, leading to energy wastage due to response delays and unnecessary superheatings. In this regard, predictive models designed for control can significantly aid in achieving all the targets set by the European Union. This paper focuses on the development of a predictive model for the energy consumption of an air handling unit (AHU) used in the paint-shop area of an automotive production process. The model, developed in MATLAB 2024b, is based on mass and energy balances within each component, and phenomenological equations for heat exchangers. It enables the evaluation of thermal powers and water mass flow rates required to process an inlet air flow rate to achieve a target condition for the temperature and relative humidity. The model was calibrated and validated using experimental data of a real case study of an automotive production process, obtaining mean errors of 16% and 31% for the hot and cold heat exchangers, respectively, in predicting the water mass flow rate. Additionally, a control logic based on six regulation thermo-hygrometric zones was developed, which depended on the external conditions of temperature and relative humidity. Finally, as the main outcome, several examples are provided to demonstrate both the applicability of the developed model and its potential in optimizing energy consumption, achieving energy savings of up to 46% compared to the actual baseline control strategy, and external boundary conditions, identifying an optimal trade-off between energy saving and operation feasibility. Full article

Heating, Ventilation, and Air Conditioning (HVAC) systems primarily consist of pre-cooling air handling units (PAUs), air handling units (AHUs), and air ducts. Existing HVAC control methods, such as Proportional–Integral–Derivative (PID) control or Model Predictive Control (MPC), face limitations in understanding high-level information, handling rare events, and optimizing control decisions. Therefore, to address the various challenges in temperature and humidity control, a more sophisticated control approach is required to make high-level decisions and coordinate the operation of HVAC components. This paper utilizes Large Language Models (LLMs) as a core component for interpreting complex operational scenarios and making high-level decisions. A chain-of-thought mechanism is designed to enable comprehensive reasoning through LLMs, and an algorithm is developed to convert LLM decisions into executable HVAC control commands. This approach leverages adaptive guidance through parameter matrices to seamlessly integrate LLMs with underlying MPC controllers. Simulated experimental results demonstrate that the improved control strategy, optimized through LLM-enhanced Model Predictive Control (MPC), significantly enhances the energy efficiency and stability of HVAC system control. During the summer conditions, energy consumption is reduced by 33.3% compared to the on–off control strategy and by 6.7% relative to the conventional low-level MPC strategy. Additionally, during the system startup phase, energy consumption is slightly reduced by approximately 17.1% compared to the on–off control strategy. Moreover, the proposed method achieves superior temperature stability, with the mean squared error (MSE) reduced by approximately 35% compared to MPC and by 45% relative to on–off control. Full article

Faults of heating, ventilation, and air-conditioning (HVAC) systems can cause significant consequences, such as negatively affecting thermal comfort of occupants, energy demand, indoor air quality, etc. Several methods of fault detection and diagnosis (FDD) in building energy systems have been proposed since the late 1980s in order to reduce the consequences of faults in heating, ventilation, and air-conditioning (HVAC) systems. All the proposed FDD methods require laboratory data, or simulated data, or field data. Furthermore, the majority of the recently proposed FDD methods require labelled faulty and normal data to be developed. Thus, providing reliable ground truth data of HVAC systems with different technical characteristics is of great importance for advances in FDD methods for HVAC units. The primary objective of this study is to examine the operational behaviour of a typical single-duct dual-fan constant air volume air-handling unit (AHU) in both faulty and fault-free conditions. The investigation encompasses a series of experiments conducted under Mediterranean climatic conditions in southern Italy during summer and winter. This study investigates the performance of the AHU by artificially introducing seven distinct typical faults: (1) return air damper kept always closed (stuck at 0%); (2) fresh air damper kept always closed (stuck at 0%); (3) fresh air damper kept always opened (stuck at 100%); (4) exhaust air damper kept always closed (stuck at 0%); (5) supply air filter partially clogged at 50%; (6) fresh air filter partially clogged at 50%; and (7) return air filter partially clogged at 50%. The collected data from the faulty scenarios are compared to the corresponding data obtained from fault-free performance measurements conducted under similar boundary conditions. Indoor thermo-hygrometric conditions, electrical power and energy consumption, operation time of AHU components, and all key operating parameters are measured for all the aforementioned faulty tests and their corresponding normal tests. In particular, the experimental results demonstrated that the exhaust air damper stuck at 0% significantly reduces the percentage of time with indoor air relative humidity kept within the defined deadbands by about 29% (together with a reduction in the percentage of time with indoor air temperature kept within the defined deadbands by 7.2%) and increases electric energy consumption by about 13% during winter. Moreover, the measured data underlined that the effects on electrical energy demand and indoor thermo-hygrometric conditions are minimal (with deviations not exceeding 5.6% during both summer and winter) in the cases of 50% clogging of supply air filter, fresh air filter, and return air filter. The results of this study can be exploited by researchers, facility managers, and building operators to better recognize root causes of faulty evidences in AHUs and also to develop and test new FDD tools. Full article

The purpose of this study was to determine the annual energy consumption that can be attributed to heating, ventilation, and air conditioning (HVAC) systems’ mixing temperature error. To develop a mixing temperature prediction model for a single-duct variable air volume (VAV) system, the mixing temperature was measured using 15 temperature sensors installed in an HVAC mixing chamber as well as the existing air handling unit’s (AHU) mixing temperature sensor. The mixing chamber was modeled using computational fluid dynamics (CFD), and a coefficient of variation of the root-mean-square error of 7.927% indicated that the model was reliable. Next, CFD simulation cases were formulated, and the temperature distribution of the mixing chamber was analyzed. This revealed that the amount of outdoor airflow input and the change in the temperature distribution of the mixing chamber were directly proportional to each other and that the mixing temperature measurements for the mixing chamber were not accurate. The mixing temperature prediction model was developed through multiple regression analysis and was successfully applied and verified. Compared with the measurements provided by existing mixing temperature sensors, the mixing temperature prediction model indicated an absolute error of 0.008–0.42 °C, confirming the model’s prediction performance. Full article

Emerging antimicrobial-resistant (AMR) Candida auris presents a formidable global health challenge, causing severe healthcare-associated infections (HAIs) with high mortality rates. Its ability to colonize surfaces and resist standard disinfectants undermines traditional hygiene practices, prompting an urgent need for new strategies. Ultraviolet C (UVC) light offers a promising approach with rapid and broad-spectrum germicidal efficacy. This review examines current literature on UVC LED technology in combating C. auris, highlighting its effectiveness, limitations, and applications in healthcare hygiene. UVC light has potent activity against C. auris, with up to 99.9999% inactivation depending on certain conditions such as microbial load, type of organism, surface, environmental, equipment, and UVC radiation factors. UVC LEDs can effectively combat C. auris, driving down healthcare costs and reducing attributable global mortality. Here, we explore implementation strategies for the targeted disinfection of high-risk areas and equipment, air handling units (AHUs), and water treatment systems. Challenges associated with UVC LED disinfection devices in healthcare settings, current performance limitations, and radiation safety are discussed. This will help in optimizing application protocols for effective disinfection and radiation safety. To further strengthen healthcare facility hygiene practices and curb the global spread of C. auris, recommendations for integrating UVC LED disinfection into infection control programs are shared. Full article

A simple explicit formula for evaluating the total capacity of chilled-water cooling coils under wet conditions is reported in this paper. The formula is developed through theoretical and analogical analysis from a practical viewpoint. With the formula, a wet coil’s total cooling capacity can be predicted straightforwardly, given the inlet air and water conditions. The formula was cross-validated against a set of catalog performance data from a series of fan coil unit (FCU) coils and simulated performance data from a series of air handling unit (AHU) coils. The mean errors in the calculated results of the present formula did not exceed 5% in the training and test sets for each coil, showing it has good accuracy and generalizability over a wide operating range and various coil types. This formula is expected to have wide applications in energy simulation and control optimization of building air-conditioning systems. Full article

An effective control of air handling unit (AHU) systems is crucial not only for managing the energy consumption of buildings but ensuring indoor thermal comfort for occupants. Although the initial control schema of AHU is appropriate at installation and testing, it is frequently necessary to adjust the control variables due to the changing thermal response of the building envelope and space usage. This paper presents a novel optimization process for the control parameters of old AHU systems in existing commercial buildings without system downtime and massive operational data. First, calibrating the building and system simulator with limited system operation data and unknown building parameters can provide identical responses to the system operation with the Hooke–Jeeves algorithm during the cooling season. The deep deterministic policy gradient algorithm is employed to determine the optimal control parameters for the valve opening position of the cooling coil within less than three hours of training based on the calibrated simulator. By using actual implementations with the developed optimal control variables for an old AHU in a real building, the proposed auto-tuned PID control in the simulator and with machine learning improves thermal environments with a steady room temperature (23.5 ± 0.5 °C) by 97% in occupied periods. It is also proved that this can reduce cooling energy consumption by up to 13.71% on a daily average. The successful AHU controller can improve not only the stability of AHU systems but the efficiency of a building’s energy use and indoor thermal comfort. Full article

As new algorithms incorporate occupancy count information into more sophisticated HVAC control, these technologies offer great potential for reductions in energy costs while enhancing flexibility. This study presents results from a two-year field evaluation of an occupancy-based HVAC control system installed in an office building. Two wings on each of the building’s 2–11 floors were equipped with occupancy counters to learn occupancy patterns. In combination with proprietary machine learning algorithms and thermal modeling, the occupancy data were leveraged to implement optimized start, early closure, and adjustments to fan operation at the air handling unit (AHU) level. This study conducted a holistic evaluation of technical performance, cost-effectiveness analysis, and user satisfaction. Results show the platform reduced weekday AHU run times by 2 h and 35 min per AHU per day during the pandemic time period. Simulation shows that 6.1% annual whole-building savings can be achieved when the building is fully occupied. The results are compared with prior studies, and potential drivers are discussed for future opportunities. The assessment results shed light on the expected in-the-field performance for researchers and industry stakeholders and enabled practical considerations as the technology strives to move beyond research-grade pilot trials into product-grade deployment. Full article

Over the past decade, the integration of desiccant technology with evaporative cooling methods has proven to be highly effective and efficient in providing comfortable indoor environments. The performance of desiccant-based direct evaporative cooling (DEC) systems is strongly influenced by environmental conditions, and their output behavior varies across multiple climatic zones. It is not easy to assess the system performance in numerous climatic zones as it is a time-consuming process. The current study focuses on determining the feasibility of a solid desiccant integrated with a direct evaporative cooler (SDI-DEC) for three different climatic zones of Pakistan: Lahore (hot and humid), Islamabad (hot and semi-humid) and Karachi (moderate and humid). To serve this purpose, a specially designed controlled climate chamber with an integrated air handling unit (AHU) was installed to create multiple environmental conditions artificially. It could also provide global climatic conditions under temperature and absolute humidity ranges of 10 °C to 50 °C and 10 g/kg to 20 g/kg, respectively. The weather conditions of the selected cities were artificially generated in the climate chamber. Based on different operating conditions, such as inlet air temperature, humidity and regeneration temperature, the performance of the system was estimated using performance indicators like COP, dehumidification effectiveness, solar fraction and supply air conditions. Results showed that the maximum temperature achieved from solar collectors was about 70 °C from collectors with an area of 9.5 m². Moreover, the observations showed that when the regeneration temperature was increased from 60 °C to 80 °C, the COP of the system decreased about 41% in a moderate and humid climate, 28% in a hot and semi-humid environment and 23% in a hot and humid climate. The results revealed that an SDI-DEC system has the potential to overcome the humidity and cooling loads of the multiple climatic scenarios of Pakistan. Full article

Underground thermal imbalance poses a challenge to the sustainability of ground source heat pump systems. Designing hybrid GSHP systems with a back-up energy source offers a potential way to address underground thermal imbalance and maintain system performance. This study aims to investigate different methods, including adjusting indoor heating and cooling setpoints and dimensioning air handling unit (AHU) cooling coils, heat pump and borehole field, for improving the long-term performance of a hybrid GSHP system coupled to district heating and an air-cooled chiller. The system performance, life cycle cost and CO₂ emissions were analyzed based on 25-year simulations in IDA ICE 4.8. The results showed studied methods can significantly improve the hybrid GSHP system performance. By increasing the AHU cooling water temperature level and decreasing indoor heating and cooling setpoints, the ground thermal imbalance ratio was reduced by 12 percentage points, and the minimum borehole outlet brine temperature was increased by 3 °C in the last year. However, ensuring long-term operation still required a reduction in GSHP capacity or an increase in the total borehole length. The studied methods had varying effects on the total CO₂ emissions, while insignificantly affecting the life cycle cost of the hybrid GSHP system. Full article

Previous studies have shown that the amount of energy consumed by mechanical cooling can be significantly reduced by the indirect evaporative cooling (IEC) process. By increasing the heat recovery efficiency of air handling units (AHUs), sensible cooling performance can be achieved with the IEC process for a significant part of the cooling season. This study determined the sensible cooling performance under which outdoor air conditions can be achieved. With IEC, the indoor humidity load cannot be adequately managed and must be solved by a supplementary cooling system, which may require additional cooling energy. This study shows the effect of the set indoor humidity on the amount of cooling energy required. The increase in energy consumption of the supplementary cooling system has been determined by simulation and for which indoor air conditions the amount of cooling energy used can be optimized if only IEC cooling is used in the air handling unit. Full article

This paper proposes the optimal algorithm for controlling the HVAC system in the target building. Previous studies have analyzed pre-selected algorithms without considering the unique data characteristics of the target building, such as location, climate conditions, and HVAC system type. To address this, we compare the accuracy of cooling load prediction using ANN and LSTM algorithms, widely used in building energy research, to determine the optimal algorithm for HVAC control in the target building. We develop a simulation model calibrated with actual data to ensure data reliability and compare the energy consumption of the existing HVAC control method and the two algorithms-based methods. Results show that the ANN algorithm, with a CV(RMSE) of 12.7%, has a higher prediction accuracy than the LSTM algorithm, CV(RMSE) of 17.3%, making it a more suitable algorithm for HVAC control. Furthermore, implementing the ANN-based approach results in a 3.2% cooling energy reduction from the optimal control of Air Handling Unit (AHU) Discharge Air Temperature (DAT) compared to the fixed DAT at 12.8 °C in a representative day. This study demonstrates that ML-based HVAC system control can effectively reduce cooling energy consumption in HVAC systems, providing an effective strategy for energy conservation and improved HVAC system efficiency. Full article