HVAC Systems In Commercial Buildings 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.

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.

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.

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.

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.

Empirical analysis of the prevalence of HVAC faults in commercial buildings

Commercial building HVAC systems experience many sensing, mechanical, and control-related faults that increase energy consumption and impact occupant comfort. Fault detection & diagnostics (FDD) software has been demonstrated to identify and help diagnose these types of faults. Several studies have demonstrated FDD energy savings potential, but there is limited empirical data characterizing the quantity and type of faults reported by FDD tools. This paper presents results of an FDD fault reporting study, employing multi-year monitoring data for over 60,000 pieces of HVAC equipment, covering over 90 fault types, and using new metrics that we developed to characterize fault prevalence. Study results offer an unprecedented accounting of the quantity of faults reported, the most commonly occurring faults, and fault persistence. We find that 21 air handling unit (AHU) faults were reported on 20% or more AHUs in our dataset, and 18 AHU faults persisted for more than 20% of the time period covered by the data. On any given day, 40% of AHUs and 30% of air terminal units saw a reported fault of some kind. Based on in-depth analysis of these results we provide recommendations for building operators, FDD software developers, and researchers to enable more efficient commercial building operation.

Peak load reduction and load shaping in HVAC and refrigeration systems in commercial buildings by using a novel lightweight dynamic priority-based control strategy

Reducing peak power demand in a building can reduce electricity expenses for the building owner and contribute to the efficiency and reliability of the electrical power grid. For the building owner, reduced expenses come from the reduction or elimination of peak power charges on electricity bills. For the power system operator, reducing peak power demand leads to a more predictable load profile and reduces stress on the electric grid system. We present a computationally inexpensive, dynamic, and retrofit-deployable control strategy to effect peak load reduction and load shaping. The effectiveness of the control strategy is examined in a simulation with 80 air-conditioning units and 40 refrigeration units. The results show that a peak demand reduction of 60 kW can be achieved relative to peak demand in a typical set point–based approach. The proposed strategy was deployed in a gymnasium building with four rooftop HVAC units, where it showed over 15% peak demand (kW) reduction savings while maintaining or lowering energy consumption (in kilowatt-hours) relative to the set point–based thermostat controls.

Explaining inefficiencies in commercial buildings providing power system ancillary services

Ancillary services are required to balance supply and demand in electric power systems. Demand response may provide attractive options for these services, through means such as varying the power consumption of commercial building HVAC systems. However, experimental results from a 30,000m2 office building suggest that when a building provides ancillary services it consumes more energy than when it is operated normally. This translates to additional costs and environmental impacts.This paper investigates potential causes for building inefficiency associated with ancillary services provision. We develop a physics-based simulation model that captures heat exchange processes and fan and air duct dynamics. During an ancillary service event, we vary the fan power consumption, and then compute the difference between the baseline and actual energy consumption to determine the efficiency of the actions. We explore the impact of building parameters, control design, and baseline model accuracy on the efficiency.In simulation, we find that shorter duration power changes and less aggressive controllers result in less change in energy consumption. We also find that baseline error has outsized effects on the efficiency calculations. These results offer new understanding of the mechanisms underlying inefficiency and point to opportunities to reduce associated costs and environmental burdens.

A mixed-integer linear programming model for real-time cost optimization of building heating, ventilation, and air conditioning equipment

In this paper, we present a framework for the formulation and solution of mixed-integer linear programming (MILP) models for operational planning of HVAC systems in commercial buildings. We introduce the general concepts of generators (e.g., chillers, boilers, cooling towers) and resources (e.g., electricity, chilled water), which allow us to model a wide range of central plants. By using discrete variables for on/off states of central plant equipment and continuous variables for equipment load, storage tank usage, and building temperature trajectory, all critical-to-cost decisions are taken into account. In addition, we consider both time-varying use charges and peak demand charges as well as building models of varying complexity. Because equipment models are often nonlinear, piecewise-linear approximations are used, which can be made arbitrarily accurate and enable real-time solution to the resulting optimization problems. We also employ a symmetry-free formulation to enhance the solution of the MILP model. Such features lead to improved performance compared to approaches employing heuristics or optimization without discrete variables. We demonstrate optimization for a small-scale cooling system and show favorable scaling of solution time with respect to number of units and temperature zones. In addition, we show simultaneous optimization of heating and cooling loops when a forecast of resource demand is available. Finally, we demonstrate closed-loop operation of our proposed optimization scheme. In all cases examined, a solution with an optimality gap below 1% can be obtained within 5min, and thus the proposed optimization can be solved in real time, allowing for rapid response to changing weather, price, or demand forecasts.

Trusted Interconnections Between a Centralized Controller and Commercial Building HVAC Systems for Reliable Demand Response

Dynamic smart grid operations require that utilities that incorporate intermittent renewable energy resources provide creative and inclusive solutions to reduce and shift electrical demand. Commercial building HVAC systems have been used as a dispatchable load, but are limited by the lack of interoperability. Increased operability can be achieved using a secure and reliable interconnection device, which can provide direct bidirectional communications between the utility and the building automation system (BAS) controllers. This paper developed and tested a building automation intrusion detection system (BAIDS) that can provide a cyber-secure connection between public and private BAS networks. The BAIDS was used in a hardware-in-the-loop experiment that connected an actual photovoltaic array with a BAS control test bed and a building zone model. The BAIDS device allowed for critical control signals to pass from the public network directly to a fan controller in a BAS private network. At the same time, the BAIDS device provided intrusion detection monitoring to identify malicious activity. The network traffic was evaluated using an adaptive resonance theory (ART) artificial neural network. The ART algorithm was able to learn normal traffic activity on the private and public networks. The algorithm was then used to detect unauthorized attempts to access the interconnection device and a malicious cyber-physical attack on the BAS.

A Performance Assessment Method for Main HVAC Equipment with Electricity Submetering Data

HVAC system is one of the biggest contributors to building energy use, and meanwhile a considerable part of energy is wasted due to the faulty operation of it. Hence, detecting and diagnosing HVAC system faults and improving energy efficiency are of large economic and environmental impact worldwide. However, the current study or application is highly limited by the scarcity of energy usage data, while in recent years electricity submeter was widely implemented and massive amounts of submetering data were accumulated in China. The aim of this paper is therefore to develop a performance assessment framework which is specifically designed for HVAC systems in such commercial buildings with electricity submetering data. This framework consists of three parts. 1) A HVAC terminal energy usage disaggregation algorithm and 2) a cooling load estimation algorithm are firstly performed to make up data deficiency. And then, 3) a diagnosis algorithm is proposed, which uses EPI generated from a Chinese standard (GB/T 17981-2007) as the benchmarks. Finally, a real case study in Shanghai is presented to illustrate the proposed framework and shows that the building operators can easily identify the poor performance area.

A parametric study of energy savings from cleaning coils and filters in constant air volume HVAC systems

This study evaluates the energy savings resulting from the remediation of coil and filter fouling in constant air volume HVAC systems in residential and small commercial buildings. A computer model was developed to simulate the behavior of buildings and their duct systems under various levels of fouling. The model was verified through laboratory and field testing. Parametric simulation and sensitivity analysis results suggest that although fouling can have an impact on both air conditioner and furnace energy use, for the levels of fouling found in the literature, energy savings will be negligible for residential buildings and negative for small commercial buildings.

HVAC system strategies for energy conservation in commercial buildings in Saudi Arabia

In recent years, there has been a dramatic increase in energy consumption in Saudi Arabia. The building sector being the largest consumer of electric energy represents a major potential contributor for reducing energy consumption. Due to their functional and operational characteristics, commercial buildings relatively consume more energy (per unit area) than other types of buildings. The heating, ventilating and air-conditioning system (HVAC) is one of the largest end-users of energy in these buildings, particularly in harsh climates. Energy efficient design and operation of HVAC systems in commercial buildings can offer major opportunities for reduced energy consumption and contribute to sustainable development. However, improper utilization of energy conservation measures can result in reduced environmental quality. This in turn exposes the occupants to thermal discomfort and health risks, and consequently diminishes the economic value of the facility. Therefore, a well assessed and balanced energy conservation strategy is required to achieve energy efficiency while maintaining desired level of thermal comfort. In this study, major design and operational parameters for different types of HVAC systems influencing energy consumption are investigated utilizing the Visual-DOE program. Results indicate that energy savings of up to 30% can be obtained while maintaining acceptable level of thermal comfort when HVAC systems are properly selected and operated.

HVAC Fault Detection and Diagnosis Toolkit

This toolkit supports component-level model-based fault detection methods in commercial building HVAC systems. The toolbox consists of five basic modules: a parameter estimator for model calibration, a preprocessor, an AHU model simulator, a steady-state detector, and a comparator. Each of these modules and the fuzzy logic rules for fault diagnosis are described in detail. The toolbox is written in C++ and also invokes the SPARK simulation program.

ARX and AFMM model-based on-line real-time data base diagnosis of sudden fault in AHU of VAV system

On-line diagnostic testing in automated processes requires practical fault detection and diagnosis techniques. The paper presents a model based methodology for sudden online fault detection in one of the most widely used Variable Air Volume (VAV) HVAC Systems in Commercial and Institutional Buildings. Two models, Auto Regressive Exogenous (ARX) and Adaptive Forgetting Through Multiple Models (AFMM), are trained and validated on data obtained from a real building. The models are trained using normal real time operational data and validated on data obtained by inducing a fault artificially in the damper control sub-system under normal operating conditions. It may be concluded on the basis of results obtained that the variation of parameters rather than the difference between the predicted and actual output is more prominent and reflective of the sudden fault in the system. The AFMM can detect any change in the system, i.e., when a fault was implemented and when the fault was rectified. However, it requires a long window length and, therefore, may not detect faults of low magnitude. The ARX model, on the other hand, can be used with very short window length and is more robust.

Investigation of some large building energy conservation opportunities using the doe-2 model

Owing to their complexity, the design and efficient operation of HVAC systems in commercial buildings can be greatly enhanced by using accurate thermal simulation models. The DOE-2 model was validated using monitored data for a large (28,000 m 2) commercial building. The model provided a means to evaluate some conservation measures which included window glazings, occupancy sensors, cold deck temperature set point and reduced ventilation air. Reductions in cold deck temperature and ventilation air were attractive as no-cost options which could be easily returned to original settings if problems arose. The potential energy savings for occupancy sensors was substantial and could prove economical.