Building HVAC Systems Research Articles

OPTIMIZING HVAC EFFICIENCY AND RELIABILITY: A REVIEW OF MANAGEMENT STRATEGIES FOR COMMERCIAL AND INDUSTRIAL BUILDINGS

This study presents a comprehensive review of strategies to optimize HVAC systems in commercial and industrial buildings, focusing on enhancing energy efficiency, system reliability, and environmental sustainability. HVAC systems are major energy consumers, contributing significantly to operational costs in large buildings. With increasing energy costs, regulatory pressures, and the push for sustainability, technological advancements and management strategies have emerged to improve HVAC performance. Following PRISMA guidelines, this review analyzed 58 high-quality peer-reviewed studies published between 2010 and 2023. Key findings show that energy management systems (EMS) can reduce energy consumption by up to 30%, while Building Management Systems (BMS) enhance system reliability through centralized control and predictive maintenance. The adoption of eco-friendly refrigerants and energy-efficient designs, such as heat recovery and variable refrigerant flow (VRF) technologies, further lowers energy usage and environmental impact. Additionally, integrating renewable energy sources like solar and geothermal into HVAC systems can reduce energy consumption by as much as 40%. Green building certifications, such as LEED and BREEAM, drive the adoption of optimized HVAC technologies, delivering both environmental and financial benefits. This review underscores the crucial role of HVAC optimization in reducing energy consumption, lowering carbon footprints, and improving the operational performance of buildings, offering valuable insights for building managers, engineers, and policymakers.

The role of the electricity grid in operation-induced greenhouse gas emissions by a residential building: A multi-year retrospective simulation study

In view of the growing shares of renewables in the electricity grid in combination with the electrification of hvac (heating, ventilation, air conditioning) systems in residential buildings, the grid intensity (in terms of carbon dioxide emissions per unit of electric energy) becomes increasingly sensitive to weather conditions, and synchronicity between weather and the grid becomes a more critical aspect in building performance assessment. Using building performance simulation techniques to seek robust building designs requires awareness about the uncertainties in circumstantial factors that affect performance. This 2016 – 2022 retrospective study highlights the effects of using low or high temporal resolution grid emissions intensity data on projected operation-induced carbon dioxide emissions for a terraced dwelling in the Netherlands. Building fabric quality, the occupant profile, and systems configurations (i.e., hvac and photovoltaics) are varied to investigate the effects of the applied grid model resolution. This study shows that ignoring high-resolution grid intensity data is getting increasingly problematic; applying low resolution (annual) instead of high-resolution (15-minute) grid intensity data leads to an increasingly unjustified optimistic assessment both for net and gross emissions (either or not allowing for carbon displacement by feeding locally generated electricity into the grid).

Quantifying the potential of load flexibility for building HVAC system using model predictive control strategy

Building as a Battery (BaB) is a concept aimed at utilizing building energy systems for load shifting purposes. However, there is a lack of study to accurately quantify building load flexibility and analyze how it is impacted by various factors. This study addresses this gap by first quantifying building load flexibility using battery parameters, specifically charging power, energy storage capacity, and round-trip efficiency. Subsequently, an analysis is conducted to examine the impacts of internal and external factors on building load flexibility, Specifically, we studied how ToU utility structure, load shifting duration, comfort range, ambient weather conditions, and building thermal properties influence charging power, energy storage capacity, and round-trip efficiency. Our simulation results revealed that an increase in the peak-to-valley ratio, load shifting duration, building thermal capacity, and cooling demand leads to higher charging energy and energy storage capacity, while an increase in building thermal resistance and widening of the comfort range result in their decrease. Regarding the round-trip efficiency, we found most parameters, except for building thermal capability, have a positive relation with the round-trip efficiency. Furthermore, sensitivity analysis showed that ToU utility structure and ambient weather conditions have the largest impacts on charging power, energy storage capacity, and round-trip efficiency. Additionally, our study indicates that leveraging buildings as virtual batteries for peak demand reduction is a feasible and cost-effective method compared with other energy storage techniques such as electric batteries. Overall, our findings offer valuable insights for parameterizing building load flexibility using battery parameters, analyzing factors that impact building load flexibility and informing grid operator to design property utility structures to meet their load shifting goals.

Optimal control strategy for building HVAC systems: Satisfying flexible demand response with different value-based selection

Growing renewable energy integration leads to significant fluctuations in electricity generation. To address this challenge, market-based mechanisms for dynamic carbon emission factors and electricity prices are emerging, particularly in regions like China pursuing carbon neutrality. Buildings, with HVAC systems as the largest consumers, require flexible Demand Response (DR) strategies to balance these fluctuations while maintaining occupant comfort. This paper proposes a hierarchical control framework for building HVAC systems. It optimizes operation for various objectives (minimizing energy consumption, reducing electricity costs, and lowering carbon emissions), while ensuring thermal comfort. The framework involves three modules. First is the time-based demand allocation, which uses genetic algorithms to optimize energy use based on future cooling demands, indoor temperature, and fluctuating electricity and carbon prices. It provides building-level temperature setpoints and electricity consumption plans. Second is the spatial indoor demand adjustment, which balances overall cooling load and temperature satisfaction across individual spaces, taking into account room-specific costs and difficulty confidents for temperature adjustments. This module assigns future temperature setpoints to each space. Third is HVAC system follow-up control, which Implements the optimized setpoints for real-time control of the HVAC system. The framework’s effectiveness is verified through application to a real commercial building project. Compared to a baseline scenario, the optimal electricity cost strategy achieved a 6.5% cost reduction, and the optimal carbon emission reduction strategy achieved an 8.2% reduction. Real-world control based on carbon emission optimization resulted in an actual year-on-year carbon emission reduction of 8.7%.

Identifying untraced faults associated with high return temperatures from heating systems in buildings connected to district heating networks

The district heating (DH) system is in its transition towards the 4th generation district heating (4GDH), and the high DH return temperatures need to be addressed during the process. In the existing building heating systems, many faults, malfunctions, or sub-optimal operations can lead to high DH return temperatures. However, the field of fault detection and diagnostics (FDD) within heating systems is notably under-researched in contrast to their counterparts in ventilation and air conditioning in building HVAC systems. This divergence can be attributed to several factors, including limited digital integration, a scarcity of data, and the non-obvious nature of faults in heating systems. In this study, we utilized heat cost allocators (HCA) and energy meters to investigate the features and potential impacts of four untraced faults that can lead to high district heating (DH) return temperatures in both space heating and domestic hot water systems in large buildings. We identified component-level faults, including heat exchanger overflow, space heating temperature controller failures, and excessive operating temperatures due to bypass, as well as system-level issues such as non-uniform heat distribution in buildings and its impacts. This exploration provides new, critical insights for advancing FDD research in HVAC and DH systems.

Economic model predictive control for building HVAC system: A comparative analysis of model-based and data-driven approaches using the BOPTEST Framework

Model Predictive Control (MPC) has demonstrated its capability to significantly enhance energy efficiency while ensuring indoor comfort. This study focuses on the development and fair comparison of two Economic Model Predictive Control(EMPC) frameworks with the goal of optimizing demand response for utility cost reduction and ensuring thermal comfort in residential buildings. The first framework, known as RC-MPC, utilizes a reduced-order thermal Resistance–Capacitance network model to represent the thermal dynamics of the building. It formulates the optimization problem as a Linear Programming (LP) task, which is solved using CPLEX. The second framework, known as ANN-MPC, employs an Artificial Neural Network model capture the thermal behavior of the building, It addresses a non-convex optimization problem through projected stochastic gradient descent. To evaluate the performance of these frameworks, experiments are conducted using a single-zone dwelling within the BOPTEST framework. The results demonstrate that both approaches effectively reduce operational costs and enhance comfort, achieving comparable performance and outperforming the baseline Rule-Based Control (RBC) method. The discomfort reductions range from 30% to 95%, and energy expense savings range from 17% to 34%. Furthermore, the study examines the impact of various MPC hyperparameters. The findings highlight that a proper control horizon and formulating the objective function accurately are crucial for achieving optimal control performance. Additionally, tightening the comfort range is found to be important in managing the comfort violations, and the choice of solver can influence both the control performance and computational efficiency.

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.

Energy optimization algorithms for multi-residential buildings: A model predictive control application

This study presents an optimization algorithm for Model Predictive Control (MPC) of the HVAC systems in multi-family residential buildings assessing the performance of four objective functions. Implemented in C++, using the free OR-Tools optimization library, the model is formulated a Mixed Integer-Linear Programming (MILP) problem. The study analyses the results of tests conducted on a 20-dwelling block in Switzerland across various weather and occupancy conditions, resulting in a parametric study of 64 cases.The models developed for the MPC are Grey-box type for the interconnected energy systems: the building, thermal storage tanks, a heat pump, the ventilation system and PV collectors, highlighting a radiant wall heating system integrated into the building facade. The tanks and the heat pump models were informed with manufacturer data, while for the building a R3C3 thermal-electrical equivalent model was developed, calibrated using TRNSYS simulations with a root mean square error of 1.7%.Findings demonstrate how the algorithm optimizes the operation according to the desired criteria, while ensuring indoor comfort with a 15-minute time resolution. The time execution of the majority of cases is under 3 min in a low-specs computer, affirming its practical viability for real-world implementation.

SS-CWGAN: A novel fault diagnosis model for building HVAC systems under limited labeled data

Fault diagnosis (FD) systems are important to the reliable operations and energy management of the building HVAC systems. However, traditional supervised learning-based FD methods heavily rely on labeled data and underutilize massive unlabeled data. This study proposes a semi-supervised conditional Wasserstein generative adversarial network (SS-CWGAN) for the FD of HVAC systems under limited labeled data. The information contained in a large number of unlabeled data is fully leveraged to improve the fault diagnosis performance of HVAC systems. Besides, a unique semi-supervised learning approach is developed for SS-CWGAN to extract the common distribution information of labeled and unlabeled data, enhancing the model generalization, especially in the practical scenario with limited labeled data. Comparison experiments demonstrate that when the number of labeled data is within 100, the proposed method outperforms the existing supervised and semi-supervised FD methods by more than 14% and 4% in diagnosis accuracy, respectively. Furthermore, the diagnosis performance improvement of the proposed method is more significant at severity level 1, suggesting the promising ability to diagnose the faults at an early stage.

Mechanistic indoor air quality model and data-driven calibration for intelligent HVAC AI control system through real-time data in underground buildings

Abstract Modeling the dynamics of indoor air quality (IAQ) in subway environments is challenging due to the complex interplay of variables like subway schedules, ventilation, and passenger numbers. This study developed a high-precision mechanistic model for IAQ management and intelligent HVAC control in underground buildings, focusing on Y-station. Global Sensitivity Analysis (GSA) highlighted the significance of the train piston factor during operational hours and the increased sensitivity of penetration and deposition factors when trains are not operational. The model, calibrated in real-time using a Genetic Algorithm (GA), exhibited remarkable accuracy in simulating PM2.5 levels, affirming its effectiveness in forecasting future air quality. The model adeptly captures the complexities of air quality dynamics, providing a comprehensive understanding of temporal IAQ variations. The result demonstrates the model’s efficacy as a tool offering a foundation for strategies to forecast IAQ and control the HVAC system in underground buildings.

Reinforcement learning optimal control method for multi chiller HVAC system in an existing office building

Abstract Given that buildings consume approximately 33% of global energy, and HVAC systems contribute nearly half of a building’s total energy demand, optimizing their efficiency is imperative for sustainable energy use. Many existing buildings operate HVAC systems inefficiently, displaying non-stationary behavior. Current reinforcement learning (RL) training methods rely on historical data, which is often obtained through costly modeling or trial-and-error methods in real buildings. This paper introduces a novel reinforcement learning construction framework designed to improve the robustness and learning speed of RL control while reducing learning costs. The framework is specifically tailored for existing office buildings. Applying this framework to control HVAC systems in real office buildings in Beijing, engineering practice results demonstrate: during the data collection phase, energy efficiency surpasses traditional rule-based control methods from the previous year, achieving significantly improved energy performance (a 17.27% reduction) with minimal comfort sacrifices. The system achieves acceptable robustness, learning speed, and control stability. Reduced ongoing manual supervision leads to savings in optimization labor. Systematic exploration of actions required for RL training lays the foundation for RL algorithm development. Furthermore, by leveraging collected data, a reinforcement learning control algorithm is established, validating the reliability of this approach. This construction framework reduces the prerequisites for historical data and models, providing an acceptable alternative for systems with insufficient data or equipment conditions.

A New Tailored Approach to Calculate the Optimal Number of Outdoor Air Changes in School Building HVAC Systems in the Post-COVID-19 Era

Air conditioning systems can play a positive or negative role in the spread of COVID-19 infection. The importance of sufficient outdoor air changes in buildings was highlighted by the World Health Organization, therefore these should be guaranteed by mechanical ventilation systems or adequate air conditioning systems. The proposed case study concerns the optimal number of outdoor air changes to limit COVID-19 contagion for a school building in Central Italy. The Wells–Riley model is used to assess the risk of airborne infection, while energy consumption is calculated by a dynamic energy simulation software. The scope of the paper offers an innovative method to define the optimal ventilation strategy for the building’s HVAC system design to reduce the risk of infection with limited increases in energy consumption and greenhouse gas emissions. Results show that the desirable approach is the one in which the same low value of contagion risk is set in all rooms. This new approach results in significant energy savings, compared to the most common ones (setting the same high outdoor air rates for all rooms) to counteract the risk of infection. Finally, the zero-emission building target is verified by introducing a suitable photovoltaic system to offset pollutant emissions.

An entropy-based causality framework for cross-level faults diagnosis and isolation in building HVAC systems

Faults, such as malfunctioning sensors, equipment, and control systems, significantly affect a building’s performance. Automatic fault detection and diagnosis (AFDD) tools have shown great potential in improving building performances, including both energy efficiency and indoor environment quality. Since modern buildings have integrated systems where multiple subsystems and equipment are coupled, many faults in a building are cross-level faults, i.e., faults occurring in one component that trigger operational abnormalities in other subsystems. Compared with non-cross-level faults, it is more challenging to isolate the root cause of a cross-level faults due to the system coupling effects. Bayesian networks (BNs) have been studied for the root cause isolation for building faults. While promising, existing BN-based diagnosis methods highly rely on expert domain knowledge, which is time-consuming and labor expensive, especially for cross-level faults. To address this challenge, we propose an entropy-based causality learning framework, termed Eigen-Entropy Causal Learning (EECL), to learn BN structures. The proposed method is data-driven without the use of expert domain knowledge; it utilizes causal inference to determine the causal mechanisms between faults status and symptoms to construct the BN model. To demonstrate the effectiveness of the proposed framework, three fault test cases are used for evaluation in this study. Experimental results show that the BN constructed by the proposed framework is able to conduct building cross-level faults diagnosis with a comparable isolation accuracy to those by domain knowledge while maintaining less complexed BN structure.

A novel study on power consumption of an HVAC system using CatBoost and AdaBoost algorithms combined with the metaheuristic algorithms

Efficient power management is fundamental for organizations and systems to conserve resources, reduce costs, and improve environmental sustainability. To achieve the mentioned purposes, it is vital to perceive power consumption patterns and trends to make informed decisions about energy usage. By evaluating power consumption, inefficiencies and areas for improvement can be identified. In this regard, this paper presents a comprehensive study to predict power usage in a non-residential building HVAC system. The study employs the integration of machine learning models and optimization algorithms to achieve accurate predictions. The results demonstrate the effectiveness of the approach, with the CatBoost-AO model depicting superior performance in comparison with other models across a range of statistical evaluation metrics including NSE, JSD, RMSE, MAPE, VAF, KLD, as well as R2 and runtime. Remarkably, the CatBoost-AO model achieves the maximum R2 value of 0.91, indicating strong predictive capability. Overall, the presented study highlights the potential of employing machine learning and optimization techniques to improve power management and resource efficiency in HVAC systems, contributing to more sustainable and cost-effective operations in non-residential buildings.

Fault detection and diagnosis in light commercial buildings’ HVAC systems: A comprehensive framework, application, and performance evaluation

The data-driven approach currently dominates the field of Automatic Fault Detection and Diagnosis (AFDD) in HVAC systems. However, a significant concern lies in the prevalent use of labeled experimental and simulation data, which often does not represent real-world operational conditions. This study unveils a comprehensive framework for AFDD in light commercial buildings, effectively leveraging unlabeled raw data extracted directly from their Building BAS. Its main goal is to provide a versatile methodology tailored for real-world applicability. Buildings classified as “light commercial” typically have less than 2,500 square meters of floor area and no more than six stories, such as small offices, medical facilities, banks, small manufacturing facilities, etc. A common feature of these buildings is the fact that the HVAC systems tend to be relatively simple and have similar configurations, thus making it easy to develop scalable and reproducible fault detection methods. This paper focuses on a practical case study in a light commercial building HVAC system situated in Montreal, Canada, encompassing a single Air Handling Unit (AHU) and four Variable Air Volume (VAV) reheating boxes to evaluate the framework. This comprehensive framework encompasses a sequence of sub-objectives: creating a sizable, synchronized raw dataset from diverse BAS sensor tags, comprehensive data cleansing to address inconsistencies, developing an anomaly detection method, investigating these anomalies to extract underlying rules, and finally, dataset labeling. An AFDD classification model is then applied to evaluate its ability to distinguish normal from faulty conditions across various fault types. The study highlights the potential of dimensional reduction techniques and unsupervised clustering for effective anomaly detection in light commercial buildings, as well as the power of the Decision Tree classifier for uncovering hidden patterns, especially in anomaly conditions. It also highlights the significance of addressing imbalanced datasets in AFDD and the complexities of detecting sizing-related faults. Despite these challenges, the framework exhibits robust performance in detecting and diagnosing a range of HVAC faults. It offers a systematic and adaptable approach for handling real-world operational data in light commercial building HVAC systems, extendible to other building types, bridging the gap between data-driven methods and practical applications.

A Numerical Investigation of the Influence of Humid Environments on the Thermal Performance of a Phase Change Thermal Storage Cooling System in Buildings

Phase change thermal energy storage (PCTES) technology has garnered significant attention in addressing thermal management challenges in building HVAC systems. However, the cooling performance of PCTES systems in humid scenarios remains unexplored, which is crucial in subtropical regions, high-humidity underground areas, and densely populated spaces. Taking the mine refuge chamber (MRC) as an example, this study focuses on a passive temperature and humidity control system by employing cold storage phase change plates (PCPs) for 96 h. First, an improved and simplified full-scale numerical model including PCPs and MRC parts is established. Then, the model is validated through the experimental results and solved using a numerical method. Finally, the influence of various factors within the system is investigated and an optimization method involving batch operation is proposed. The results indicate that (1) within 40 h, the use of cold storage PCPs leads to an indoor temperature reduction of 4.8 °C and a 7% decrease in relative humidity; (2) the PCPs show asynchronous states in sensible and latent heat transfer rates; (3) for every 50 additional PCPs, the average indoor temperature increases by 0.6 °C and the relative humidity decreases by 1.5%; (4) implementing batch operation of PCPs ensures that the indoor Heat Index drops by 10 °C, which is vital for human survival. The findings will play a crucial role in the global expansion and application (including geographical and functional aspects) of phase change thermal storage technology.

BIM-based automated fault detection and diagnostics of HVAC systems in commercial buildings

In order to meet the growing demand for effective Automated Fault Detection and Diagnostics (AFDD) for HVAC systems, innovative approaches are needed to address limitations in data diversity and access to contextual information. This study introduces a methodology that leverages Building Information Modeling (BIM) to enhance the development of the AFDD model. Feature engineering techniques are utilized to generate dynamic BIM features, compensating for the lack of sensory and contextual data in Building Management Systems (BMS). By integrating AFDD analytics with BIM, a comprehensive digital twin of the facility is created, which enables facility managers to compare, reuse, and develop AFDD models for HVAC systems. The proposed methodology demonstrates the potential of leveraging BIM-based knowledge models to overcome the challenges associated with the limited sensor and contextual information availability by utilizing BIM for feature generation and, conversely, updating the BIM model with AFDD analytics.

USING DEEP LEARNING TO OPTIMIZE HVAC SYSTEMS IN RESIDENTIAL BUILDINGS

ABSTRACT HVAC systems are crucial for maintaining indoor temperature and humidity in buildings but consume significant energy, accounting for over 50% of a building’s energy use. This study proposes a deep reinforcement learning (DRL) algorithm for optimizing energy consumption in residential building HVAC systems while maintaining occupant comfort. Climate data was collected using low-cost sensors, and a co-simulation framework was developed for offline training and validation of our DRL-based algorithm. The proposed DRL-based algorithm was compared to a rule-based HVAC system regarding energy consumption and occupant comfort. Results show that the proposed algorithm can reduce energy consumption by up to 15% compared to the rule-based HVAC system. DRL is a suitable approach for optimizing HVAC systems due to its ability to adapt to the dynamics of multi-parameterized systems. This study contributes to sustainable building design by proposing a DRL-based algorithm to reduce energy consumption while maintaining a comfortable indoor temperature. Using low-cost sensors and a co-simulation framework provides a practical and cost-effective method for training and validating the proposed algorithm.

Heating ventilation air‐conditioner system for multi‐regional commercial buildings based on deep reinforcement learning

AbstractIn an era of significant energy consumption by commercial building HVAC systems, this study introduces a Deep Reinforcement Learning (DRL) approach to optimize these systems in multi‐zone commercial buildings, targeting reduced energy usage and enhanced user comfort. The research begins with the development of an energy consumption model for multi‐zone HVAC systems, considering the complexity and uncertainty of system parameters. This model informs the creation of a novel DRL‐based optimization algorithm, which incorporates multi‐stage training and a multi‐agent attention mechanism, enhancing stability and scalability. Comparative analysis against traditional control methods shows the proposed algorithm's effectiveness in reducing energy consumption while maintaining indoor comfort. The study presents an innovative DRL strategy for energy management in commercial HVAC systems, offering substantial potential for sustainable practices in building management.

Key Performance Indicators for Decision Support in Building Retrofit Planning: An Italian Case Study

To achieve climate and energy goals in the building sector, the current pace of renovating existing structures must double, overcoming prevailing barriers. Key Performance Indicators play a pivotal role in science-based decision making, emphasizing both direct and indirect benefits of building retrofits. The authors aim to contribute to proper metric identification for multi-perspective building performance assessment and formulate a methodology supporting energy planning decisions. They introduce the Global Cost per Emission Savings (GCES), an aggregated indicator encompassing both public (CO2 emissions) and private (global cost) perspectives of diverse retrofit technologies for building HVAC systems. Applied to the Italian residential building stock via the Reference Building approach, the methodology is tested using condensing gas boilers, biomass boilers, and electric heat pumps, revealing diverse environmental and economic performances. Addressing the establishment of effective decision-support tools for policymakers, the paper explores the potential impact of various policies on the favorability of technologies. Different policy scenarios are delineated to analyze how distinct approaches may influence the attractiveness of technologies. Notably, in the baseline scenario, biomass boilers hold an advantage over heat pumps according to the GCES index. However, scenarios involving technology-specific incentives or a greenhouse gases emission tax failed to alter the technological ranking, leaving heat pumps financially uncompetitive. In contrast, the TXPM scenario positions heat pumps as the most financially appealing option, penalizing biomass boilers for high particulate matter emissions.