Operation Of HVAC Systems Research Articles

Unsupervised identification of zone-level anomalies in VAV terminal units utilizing autoencoders and PCA

The effective operation of HVAC systems is crucial to minimize energy inefficiencies and occupant discomfort. However, these systems can experience various problems, including hardware and software-related anomalies. In contrast to most existing fault detection and diagnostic approaches, which rely on simple rules and alarms, this study introduces novel unsupervised approaches for detecting zone anomalies in variable air volume (VAV) air handling units (AHUs). The methods utilize autoencoders (AE) and principal component analysis (PCA). To evaluate the effectiveness of the proposed methods, both a synthetic dataset and measured data from a 28-zone VAV AHU system were investigated. The proposed method successfully detected several zone temperature and airflow anomalies using the AE-based method, and several zone anomalies were also identified using the PCA-AE approach by considering four commonly available zone-level trend logs in VAV AHUs namely temperature, airflow, airflow set-point, and VAV terminal damper position. The findings demonstrated the great adaptability of the proposed methods in detecting a wide range of zone anomalies in any modern building equipped with VAV AHUs, giving operators valuable insights about the system and notifying them of potential faults at an early stage.

Assessment of airborne transmitted infection risk in classrooms using computational fluid dynamics and machine learning-based surrogate modeling

In recent years, the assessment of airborne transmitted infection risk has been extensively performed using Computational Fluid Dynamics (CFD) simulations, especially in response to the COVID-19 pandemic. Nevertheless, the high computational demands and time-intensive nature of CFD simulations highlight the need for fast or real-time infection risk predictions. This capability is crucial for swift decision-making in dynamic environments where timely health interventions are critical. This paper presents a thorough analysis of airborne infection risks in classroom environments based on CFD simulations to understand key factors such as ventilation strategies, air change rates, occupant arrangements, source locations, and particle sizes. This study also employs data-driven supervised learning methods—specifically, Long Short-Term Memory (LSTM) and Artificial Neural Networks (ANN)—to generate surrogate models for predicting airborne infection risk. Key findings reveal that different ventilation strategies significantly affect airborne infection risk, reducing it by 49 %–77 %. Moreover, the conventional Wells-Riley model was identified as lacking in its ability to accurately predict local infection risks. The study further challenges the assumption that higher air change rates are universally beneficial, considering that occupants seated in the back rows of a classroom experienced up to a 166 % increased risk, despite elevating air change rates from 1.1 h−1 to 11 h−1. These results suggest that physical distancing alone may be insufficient and highlight the importance of considering other factors such as occupant arrangements. Regarding the model performance, the ANN-based surrogate model demonstrated varying prediction accuracy. For inhalable particle concentration predictions for susceptible occupants, R2 values ranged from 0.31 to 0.65 with CVRMSE values between 100 % and 180 %. In contrast, the model achieved an R2 of 0.79 and a CVRMSE of 34 % for infectors. The insights and methodologies from this study can inform HVAC system design and operation strategies to better mitigate infectious disease transmission in densely occupied indoor environments.

Large-scale energy cost optimization and performance analysis for dedicated outdoor air system: simulation results from ASHRAE RP-1865

This is the first journal article from the ASHRAE research project RP 1865, “Optimizing Supply Air Temperature Control for Dedicated Outdoor Air Systems,” focusing on the large-scale energy performance evaluation of the optimal control of dedicated outdoor air systems (DOAS). DOAS is a type of air handling system that is installed in buildings to ensure proper ventilation for the built environment. It is acknowledged that DOAS with proper operation strategies can provide sufficient ventilation and dehumidification while achieving energy efficiency. In this regard, there have been efforts to develop advanced control sequences (e.g., optimal control) for DOAS. Still, limited studies have demonstrated the effectiveness of such DOAS controls on a larger scale under varying climate zones with different DOAS configurations. To fill the research gap, this study aims to quantify the energy-saving potentials of applying an optimal supply air temperature (SAT) control strategy for DOAS considering diverse configurations of HVAC systems in various climate conditions across the United States. For this study, we first developed a total of 180 EnergyPlus models accommodating different types of buildings (i.e., detailed medium-sized office and primary school) and HVAC systems (i.e., DOAS types, DOAS sizes, and terminal units) under varying ASHRAE climate zones (i.e., 2A, 3B, 4A, 4C, 6A, and 6B). Next, genetic algorithm (GA)-based optimizations were conducted to determine the optimal DOAS SAT control that would minimize the energy cost of the entire HVAC system operation. Our study of comparing the optimal control to a typical rule-based control (i.e., outdoor air temperature-based reset control) revealed approximately 4–34% of energy cost saving potential by optimizing the DOAS SAT control sequence.

Impact of refrigerant undercharge faults on building indoor conditions and HVAC system operation in residential Buildings: A simulation study

This study investigates the impact of refrigerant undercharge on indoor temperature and HVAC system performance in residential buildings. Simulation models for typical residential buildings in Orlando, FL and Indianapolis, IN were developed using the ResStock database. A refrigerant undercharge fault model was then applied to the simulations with varying levels of fault intensity. The paper offers an extensive analysis, revealing that variations in supply air temperature, equipment runtime, and cooling energy consumption due to the level of refrigerant undercharge faults are notably significant on a summer representative day. Similarly, on a winter representative day, changes in supply air temperature and runtime are significant as well as changes in supplemental heat energy consumption. We find that occupants may remain oblivious to these faults during the cooling season, particularly when the HVAC system is oversized; in that case, supply air temperature data could help detect a fault. Another challenge is that during the heating season, when the supplemental heater operates, it is difficult to identify a refrigerant undercharge fault using only indoor and supply air temperature data. This study finds that supply air temperature, equipment runtime, and supplemental heater energy consumption data can help in detecting refrigerant undercharge faults.

Developing a new adaptive heat balance model to enhance thermal comfort predictions and reduce energy consumption

Accurate thermal comfort prediction is essential for enhancing both thermal comfort and energy efficiency in buildings. The heat balance and adaptive models, while widely used, have been often questioned in relation to their strengths and limitations. To overcome these challenges, a comprehensive understanding of both the physical parameters influencing heat exchange and occupants’ adaptive capacities is essential.This study introduces an analytical method to formulate a new adaptive heat balance model, the adPMV, which integrates the strengths of both models considering various parameters influencing thermal perception. The model integrates the PMV with an adaptive factor associated with the running mean outdoor temperature, in line with adaptive theory.Tested on 1377 samples from European university classrooms, the adPMV demonstrates enhanced accuracy (MAE=0.74, RMSE=1.01, MBE=-0.11) compared to PMV and other adaptive heat balance models. Validation on naturally ventilated university classrooms from ASHRAE’s databases further confirms promising results, showcasing reduced error indices (MAE=0.68, RMSE=0.81, MBE=-0.03). Notably, using adPMV setpoints not only improves thermal sensation prediction accuracy but also leads to a substantial reduction in heating demand, reaching up to 40 %.The adaptability of this model to different contexts, such as building types, climates, and HVAC system operations, presents it as a versatile tool for exploring adaptive principles.

Developing building-specific, occupant-centric thermal comfort models: A methodological approach

This study addresses the research problem of accurately predicting thermal comfort in buildings by developing occupant-centric, building-specific models. Traditional models like PMV and adaptive models often fall short in reflecting individual comfort preferences. This research aims to create more accurate and personalised thermal comfort models by considering human-related factors such as clothing, mood, and activities, along with environmental variables like temperature and humidity. The methodology involved collecting data through two self-reporting campaigns using mobile and smartwatch applications in a university building in The Hague. Regression and classification models were developed, achieving accuracy rates of 72 % and 89 % respectively, which surpass the performance of traditional models. The findings indicate that human factors significantly influence thermal comfort, with mood and clothing being particularly impactful. Seasonal variations were also accounted for, emphasising the need for periodic data collection to capture changes in occupant behaviour and environmental conditions. The key contribution of this research lies in its ability to enhance the accuracy of thermal comfort predictions, leading to more effective and user-centric HVAC system operations. This approach not only has the potential to improve the comfort of building occupants but also has implications for energy efficiency, as HVAC systems can be better tailored to actual comfort needs.

A practical real-time control strategy for high-performance building HVAC operation concerning potential pandemic outbreaks in post-pandemic era

The lessons learned from the COVID-19 pandemic emphasize the need for building HVAC systems to be adaptable and prepared to adjust their operation for infection risk mitigation for future pandemics. Most guidelines suggest increasing outdoor air fraction in HVAC systems during pandemics. However, this approach is not energy-efficient due to the conditioning of additional outdoor air, and many practical systems face limitations in introducing more outdoor air than normal due to capacity or design constraints. Therefore, there is an urgent need for an alternative solution for HVAC system operation during pandemics that is energy-efficient, can effectively mitigate infection risks and does not require extensive retrofits to existing systems. To meet this need, this study proposes a practical real-time control strategy for high-performance HVAC operation in response to potential pandemic outbreaks. First, a supply air temperature (SAT) reset scheme for infection risk mitigation is proposed based on Mamdani fuzzy inference theory. Then, a coordinated control scheme is proposed to coordinate SAT reset with chilled water temperature adjustment for energy-efficiency enhancement. This strategy does not require system equipment retrofits but only changes in control logic to transition to pandemic operation. The effectiveness of the proposed strategy is validated in a high-fidelity Modelica-based simulation environment under various weather conditions. Results show that, compared to normal operation, the proposed strategy can significantly reduce new infection cases by 32∼46 % without increasing energy use, and may even achieve slight energy savings of 9∼13 %, while maintaining expected occupant comfort.

HVAC system operation, consumption and compressor size optimization in urban buses of Mediterranean cities

Electric buses are a key element in the transition to sustainable urban mobility. The HVAC system is the primary auxiliary load and significantly affects the efficiency and the driving range. In this work, advanced dynamic models have been developed to simulate accurately and optimize the urban bus energy consumption under real operating and extreme conditions, with a particular emphasis on HVAC systems. This study integrates a 3D city model, a weighted stochastic driving cycle, a climate model, a transient thermal model coupled with a physical HVAC system model using IMST-ART and a battery model. Different bus lines and urban typologies have been analyzed in the city of València. The results indicate that the overall consumption is similar across the different bus lines, around 2.10 kWh/km. The HVAC is the second largest contributor, after the powertrain, and can reduce the driving range by 15%–20% on mild and hot summer days, respectively. Finally, the size of the compressor has been optimized, revealing that a scale factor of 75% is more convenient, since the energy consumption can be reduced by 3% with lower costs in the compressor.

Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning

This paper proposes a novel optimal control method for multi-zone HVAC systems to enhance energy efficiency and improve occupant comfort. To address the disturbances in outdoor weather and indoor loads, the proposed method formulates the HVAC control problem as a dynamic Markov decision process, and employs deep reinforcement learning (DRL) techniques to obtain the optimal control policy. To manage multiple goals among thermal comfort, indoor air quality (IAQ) and system energy efficiency, a preference-inspired (P-ins) mechanism is designed to achieve the optimal balance among different objectives. The P-ins mechanism effectively guides the agent towards the optimal control policy with high convergence rate. The proposed method has been validated through EnergyPlus-Python co-simulation testbed with real-world data traces, and assessed by an overall evaluation indicator. Results demonstrate that the proposed method can reduce energy consumption without compromising thermal comfort and IAQ. Specifically, the occurrence of temperature violations is reduced below 0.8 %, and a maximum energy saving of 9.41 % can be achieved, compared with traditional methods.

Efficient management of HVAC systems through coordinated operation of parallel chiller units: An economic predictive control approach

In developed countries, air conditioning systems have become major contributors to energy consumption in buildings. Cooling installations made up of independent chiller units connected in parallel pose a challenge in finding the most energy-efficient management strategy. This article proposes a novel economic model predictive control strategy to optimize the operation of multiple-chiller HVAC systems according to an economic cost comprising energy consumption as well as a thermal comfort index. Provided that the gradient of the economic cost function can be calculated or estimated, the proposed controller only entails the solution of a single quadratic programming (QP) problem at each sampling period, reducing computational requirements and thus facilitating the deployment on commercial embedded HVAC management platforms. The proposed controller improves economic performance. As our numerical analysis shows, optimal sequencing is achieved for the case of dual-chiller plants. Moreover, the controller adapts to possible variations in the economic criteria, enabling the system to respond to changes in electricity price and user preferences. A realistic case study, using a high fidelity model of a building simulated in TRNSYS, demonstrates the effectiveness of the proposed methodology. In particular, the proposed controller demonstrates to be more efficient than state-of-the-art QP-based economic predictive controllers in achieving a reduction of the energy consumption up to 5.19%, which is in line with the targeted reduction of 7.9% by 2030 in the EU Green Deal's ‘Fit for 55’ package.

Deployment of real-time building automation system-integrated inverse-model-based fault detection and diagnostics algorithms

The complex operation of HVAC systems in large commercial buildings warrants regular implementation of advanced analytical approaches to operations and maintenance, and subsequent corrective measures to improve and maintain optimal energy performance. Despite the established capabilities of data-driven fault detection and diagnostics (FDD) to identify suboptimal controls policies and mechanical faults resulting in poor energy performance, few attempts have been made to deploy scalable solutions around these approaches. Furthermore, real-time BAS-integrated FDD methods are predominantly rule-based, offering limited insights to faults with gradual negative impacts to energy performance. This paper demonstrates the application of various established data-driven, inverse-model-based FDD methodologies in a BAS-integrated environment. Traditionally implemented sparingly, the novelty of recursive and automatic execution of advanced FDD methodologies, facilitated through a direct data pipeline to an existing BAS, capitalizes on the BAS’s real-time monitoring capabilities to enable continuously refreshed inverse model generation that can capture the gradual degradation of building performance, and provide up-to-date actionable visualizations and key performance indicators (KPI) to building operators. Since deployment, the application has successfully identified a scheduling fault on two separate occasions in a case study building in Ottawa, Canada, and the visualizations were presented to the building operators who resolved the issues.

Low-cost data-driven estimation of indoor occupancy based on carbon dioxide (CO2) concentration: A multi-scenario case study

Occupancy levels significantly influence HVAC system operation, making accurate occupancy prediction essential for the advancement of Occupant-Centered HVAC control. This study aims to develop a simple and effective occupant prediction model in buildings using low-cost indoor environmental sensors and artificial intelligence technology. In-situ measurements were taken in two university classrooms in South Korea over a three-month period, collecting data on indoor and outdoor temperature, humidity, and CO2 levels. Five machine learning algorithms, including Linear Regression (LR), Random Forest (RF), Gradient Boosting Regression (GBR), Multi-Layer Perceptron (MLP), and Long Short-Term Memory neural networks (LSTM), were applied to compare models of indoor occupancy. The results demonstrate that, among the five machine learning models evaluated, the LSTM model outperforms the others, achieving an RMSE of 3.43. This result indicates a close match between predicted and actual indoor occupancy based on CO2 concentration. The integration of a multivariate multi-step input method further enhances its accuracy, making it suitable for a variety of real-world scenarios in indoor occupancy prediction. This study reveals that using processed data as input sources leads to improved prediction performance for indoor occupant states. Importantly, this work does not infringe on biometric information, such as human image privacy, and relies on minimal measurement data. Furthermore, it not only emphasizes the model's feasibility and practicality in predicting indoor occupancy but also its potential in HVAC system automation, building energy conservation, and indoor environmental management. This study offers guidance and support for the advancement of smart cities and intelligent buildings in the future.

Enhancing energy efficiency and comfort with a multi-domain approach: Development of a novel human thermoregulatory model for occupant-centric control

Occupant Centric Control (OCC) strategies aims to achieve a more personalized and comfortable indoor environment, translating occupants' comfort requirements into real environmental conditions. This strategy relies on a multilevel non-linear programming optimization procedure to determine optimal thermo-hygrometric setpoints. The objective is to minimize space heating and cooling requirements while addressing multi-domain comfort concerns related to individual thermal sensations and perceived air quality. To facilitate this process, this study adopts a novel Personal Comfort Model (PCM), incorporating physiological factors to predict occupants' thermal sensations and CO2 intakes. The PCM's reliable predictive capabilities are confirmed through validation with real experimental data from a test room at the University of Perugia. For detailed energy analyses, considering occupants' subjective comfort preferences, the PCM is seamlessly integrated into DETECt, an in-house building energy performance simulation tool developed by the University of Naples Federico II, for the design of advanced control algorithms and energy efficiency strategies. To effectively manage the HVAC system, a model predictive control is implemented, using the determined setpoints to ensure mechanical ventilation and maximize cost savings. The proof of concept for the developed methodology involves simulating experimental tests using the thermal model of the human body and the new facility management system, to be simulated and optimized by means of the enhanced building energy performance simulation tool, exploited for the design and operation of more occupant-centric and sustainable buildings. The proposed study demonstrates that through the developed OCC strategy enables a significant reduction in thermal discomfort (40 % and 60 % less than the occupation time for test 1 and test 2, respectively) compared to reference scenarios. Additionally, energy analysis reveals high efficiency, achieving savings of 12.8 % and 7.8 % of electricity consumed for HVAC system operation.

Impacts of energy saving measures on IEQ, task performance and COVID-19 contagion risk in public buildings: analysis of a case-study in Bozen-Bolzano, Italy

The recent 2022 energy crisis has pushed several Italian public administrations to adopt solutions to reduce energy demand for space heating in public buildings. Reduction of HVAC system operation time, decrease of heating setpoint and changes to ventilation schedules are some examples of typically implemented measures. However, some of them are in direct competition with those adopted so far to limit the risk of COVID-19 contagion, such as higher ventilation rates. Furthermore, limited operation time and lower setpoints can lead to suboptimal thermal comfort conditions for workers in public buildings, with subsequent decrease of their task performances. In this framework, to assess the global efficacy of the most recent energy saving policies in public buildings, this research focuses not only on the quantification of energy savings but also on the analysis of increased COVID-19 contagion risk, as well as performance loss due to suboptimal building operation conditions. To investigate the impact of new building operation policies for energy savings, an intermediate storey of a public office building in Bolzano, Italy, was selected as a case-study.

Impacts of different AHU configurations on health and students’ performance in Italian schools in post-pandemic era

With the spread of the COVID-19 pandemic, several countermeasures in HVAC systems operation have been undertaken to reduce the risk of infection, guaranteeing proper indoor conditions for occupants’ health and performance. Especially in school buildings, where students spend most of their time inside classroom, ensuring a good indoor air quality represents a key factor. For this reason, to gradually return to normal HVAC systems operation, innovative technologies, among which photocatalytic air filters, are in the spotlight. Due to their higher investment costs, there is the need to develop proper methodological approaches able to make their energy, health and performance benefits quantifiable and apparent to consumers, to support the decision-making process. In this context, the paper aims to develop a cost-benefit analysis to compare the impacts of two air handling unit configurations installed in typical Italian schools, being representative of both COVID-19 and post-COVID-19 conditions. The benefit-cost ratio indicator is chosen to present the results showing the goodness of the installation of innovative filtering technology in the post-COVID-19 configuration, in terms of energy-, health- and performance-related benefits.

Case Study: Impacts of Air-Conditioner Air Supply Strategy on Thermal Environment and Energy Consumption in Offices Using BES-CFD Co-Simulation.

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.

The Impact of HVAC Operational Strategies on Energy Use in an Office Building in a Hot Climate

Office buildings, characterized by high internal heat gains, represent a major share of energy use, mainly due to HVAC systems, especially in commercial facilities. Such energy use becomes more significant in locations with harsh climatic conditions. The careful design, selection, and operation of HVAC and energy systems, however, can contribute to major reductions in the energy use in buildings. In this paper, we present the outcomes of a study conducted to assess the impact of HVAC operational strategies on energy use in office buildings in the hot and humid climate of Saudi Arabia. The study utilized detailed building-energy simulation coupled with utility-use data. The results revealed annual electrical energy savings ranging from an average of 1.9% for each 1°C increase in the thermostat setpoint temperature up to 42.4%, when limiting HVAC system operation to occupied hours only. On an average, there was about a 1.4% penalty in the annual energy for each hour of increase in the HVAC system start-up time. However, this penalty should not be avoided at the expense of compromising thermal comfort. In summary, a combination of appropriate HVAC operational strategies can have a significant impact on a building’s annual energy savings, as demonstrated in the presented case study.

New ventilation design criteria for energy sustainability and indoor air quality in a post Covid-19 scenario

The Covid-19 outbreak raised great attention to the importance of indoor air quality in buildings. Even if the Covid-19 epidemic is nearing an end, all stakeholders agree that increasing outside air flow rates is beneficial for decreasing the likelihood of contagion, lowering the risk of future pandemics, and enhancing the general safety of the interior environment. Indeed, diverse concerns raised about whether the ventilation standards in place are still adequate. In this context, this research intends to assess the suitability of current ventilation standards in addressing the current pandemic scenario and to offer novel criteria and guidelines for the design and operation of HVAC systems, as well as useful guidance for the creation of future ventilation standards in a post-Covid-19 scenario. To that end, a comprehensive analysis of the ANSI/ASHRAE 62.1 is carried out, with an emphasis on its effectiveness in reducing the risk of infection. Furthermore, the efficacy of various ventilation strategies in reducing the likelihood of contagion has been investigated. Finally, because building ventilation is inextricably linked to energy consumption, the energy and economic implications of the proposed enhancements have been assessed. To carry out the described analysis, a novel method was developed that combines Building Energy Modelling (BEM) and virus contagion risk assessment. The analyses conducted produced interesting insights and criteria for ventilation system design and operation, as well as recommendations for the development of future standards.

Characterization of Envelope Air Leakage Behavior for Centrally Air-Conditioned Single-Family Detached Houses

Air leakage is an essential factor contributing to overall building performance. It plays a major role in determining energy consumption in harsh climates, particularly in residential buildings, as it represents a significant component of the envelope-induced thermal load. In centrally air-conditioned houses, the HVAC system can substantially alter the pressure distribution across the exterior envelope, reforming the air leakage behavior. Nonetheless, limited information is available to characterize and better understand such behavior to accurately predict building performance and energy consumption toward meeting the emerging requirements for sustainable buildings. This study experimentally investigated the air leakage behavior of a selected sample of centrally air-conditioned typical single-family detached houses in Saudi Arabia. The air leakage behavior was investigated by measuring the overall airtightness and the contribution of the different air leakage paths using the viable method of the blower door test (BDT). The air leakage rate was then calculated using the measured induced pressure across the envelope during the HVAC system operation. Results indicated that the air leakage behavior is significantly altered by the pressurization induced by the central HVAC system, eliminating air infiltration and producing an outward airflow across the entire envelope. The study addresses a current challenge in characterizing envelope air leakage behavior for a common type of house and, thus, would indirectly contribute to more accurate thermal and energy performance assessments. Several aspects were highlighted for consideration when defining the contribution of air leakage to energy consumption prediction and studying air leakage behavior in other types of buildings.

HVAC Systems Operation Control Based on Indirect Occupant-Centric Method for Ensuring Safety Conditions and Reducing Energy Use in Public Buildings after Covid-19

HVAC Systems Operation Control Based on Indirect Occupant-Centric Method for Ensuring Safety Conditions and Reducing Energy Use in Public Buildings after Covid-19