Commercial Heating, Ventilation, And Air Conditioning Systems Research Articles

Impact of low-frequency noise on a residential zone due to commercial HVAC systems

Low frequency noise (LFN) is a serious growing global challenge for the urban environment where noise itself is an undervalued pollutant. The commercial establishments are equipped with multiple HVAC (Heating Ventilation and Air Conditioning) units responsible for the generation of LFN. To assess the impact of LFN, the noise was monitored using A and C-weighting sound level meters at HVAC outlets as well as in and around the residential premises. The noise due to HVAC units is found to be highly intensive in the lower frequency zones (<250Hz), affecting psychological and physiological health of living beings in the residential zones. A substantial 39.4 dB sound pressure level discrepancy between A and C-weighting inside the residential premises underscores the pronounced predominance of low-frequency noise. Noise monitoring using A-weighting failed to highlight the significant impact of lower frequency octave bands. The C-weighting noise monitoring revealed a substantial contribution of lower frequency octave bands (8Hz–250Hz) with 48.2 dB(C), in the overall equivalent noise level of 48.6(C), primarily attributed to HVAC units. The study suggests monitoring and assessment of the LFN, its mitigation and control strategies and its impact on the noise environment, especially on a residential zone.

Performance benchmarking of residential air conditioning systems using smart thermostat data

Faulty residential Heating, Ventilation, and Air Conditioning (HVAC) systems waste billions of dollars of energy usage in the U.S. each year, meanwhile heavily affecting occupant comfort and causing complete operational failures. Fault detection and diagnosis (FDD) methods assist technicians in discovering and locating faults as early as possible, thereby optimizing system efficiency through predictive maintenance. However, the majority of FDD methods are developed for commercial HVAC systems. This is because FDD needs data acquired from sensors installed on the HVAC equipment, but in the residential sector, the initial cost of installing additional sensors is uneconomical. In recent years, the development of smart thermostats and the Internet of Things (IoT) enables large-scale system operational data to be recorded. The collection of smart thermostat data has nearly no cost, and FDD can be conducted using aggregated data obtained from a large number of systems using IoT technologies. Hence, this paper proposes a performance benchmarking method for residential systems that only use smart thermostats as the data source. The proposed method analyzes the system pseudo steady-state behavior and compares a critical feature termed the weighted average difference of cooling effort (ΔE¯c) both between systems and within each system. Three applications of the proposed method are detailed: identification of inadequate capacity, quantification of active capacity degradation, and verification of repair after maintenance. Within each application, real faulty systems are illustrated to demonstrate the usefulness of this method.

Decentralized Management of Commercial HVAC Systems

With the growth of commercial building sizes, it is more beneficial to make them “smart” by controlling the schedule of the heating, ventilation, and air conditioning (HVAC) system adaptively. Single-building-based scheduling methods are more focused on individual interests and usually result in overlapped schedules that can cause voltage deviations in their microgrid. This paper proposes a decentralized management framework that is able to minimize the total electricity costs of a commercial microgrid and limit the voltage deviations. The proposed scheme is a two-level optimization where the lower level ensures the thermal comfort inside the buildings while the upper level consider system-wise constraints and costs. The decentralization of the framework is able to maintain the privacy of individual buildings. Multiple data-driven building models are developed and compared. The effect of the building modeling on the overall operation of coordinated buildings is discussed. The proposed framework is validated on a modified IEEE 13-bus system with different connected types of commercial buildings. The results show that coordinated optimization outperforms the commonly used commercial controller and individual optimization of buildings. The results also show that the total costs are greatly affected by the building modeling.

An internet of things upgrade for smart and scalable heating, ventilation and air-conditioning control in commercial buildings

Scalability of control algorithms used for savings energy in commercial building Heating, Ventilation and Air-Conditioning (HVAC) system and their implementation on low cost resource constrained hardware is a challenging problem. This paper presents the Internet of Things (IoT) prototype which implements a smart and scalable control approach called the Smart-Token Based Scheduling Algorithm (Smart-TBSA) to minimize energy in commercial building HVAC systems. The IoT prototype is formalized with an architecture that encapsulates the different components (hardware, software, networking, and their integration) along with their interactions. A detailed description of the different components, hardware design, deployment issues, and their integration with legacy systems as well as cloud-connectivity is presented. In addition, simple modifications required for transforming the optimization models to an active control technique is also presented. While scalability is provided by the decentralized control, recursive zone thermal model identification, prediction occupant’s thermal sensation, and embedding them within the optimization models enhances the smartness. Consequently, due to the implementation of Smart-TBSA using IoT devices, an otherwise centralized control architecture of the legacy building automation system is transformed to a more scalable and smart decentralized one. The proposed Smart-TBSA and IoT prototype are illustrated on a pilot building in Nanyang Technological University, Singapore having 85 zones. Our results shows that by combining IoT with decentralized control, energy savings up to 20% can be derived. Moreover, we show that legacy building automation system can be transformed into a more smart, adaptable, scalable, and decentralized control by deploying IoT devices without incurring significant costs.

Support vector machine based fault detection and diagnosis for HVAC systems

Various faults occurred in the heating, ventilation and airconditioning (HVAC) systems usually lead to more energy consumption and worse thermal comfort inevitably. This paper presents a feasible and valid solution of HVAC fault detection and diagnosis (FDD) problem based on statistical machine learning technology. It learns the consistent nature of different types of faults of HVAC operation based on support vector machine (SVM), and then identify types of fault in all subsystems using the statistical relationships between groups of measurements. In order to speed up the learning process, principle component analysis (PCA) has been applied to compress the training data. Our approach models the dynamical sub-systems and sequence data in HVAC system. The learnt models can then be used for automatic fault detection and diagnosis. The approach has been tested on commercial HVAC systems. It had successfully detected and identified a number of typical AHU faults.

Support vector machine based fault detection and diagnosis for HVAC systems

Various faults occurred in the heating, ventilation and airconditioning (HVAC) systems usually lead to more energy consumption and worse thermal comfort inevitably. This paper presents a feasible and valid solution of HVAC fault detection and diagnosis (FDD) problem based on statistical machine learning technology. It learns the consistent nature of different types of faults of HVAC operation based on support vector machine (SVM), and then identify types of fault in all subsystems using the statistical relationships between groups of measurements. In order to speed up the learning process, principle component analysis (PCA) has been applied to compress the training data. Our approach models the dynamical sub-systems and sequence data in HVAC system. The learnt models can then be used for automatic fault detection and diagnosis. The approach has been tested on commercial HVAC systems. It had successfully detected and identified a number of typical AHU faults.

Economic MPC and real-time decision making with application to large-scale HVAC energy systems

With the potential to decrease operating costs and improve energy efficiency, model predictive control (MPC) has been proposed as a replacement for traditional heuristic, PID, and other conventional control strategies for heating, ventilation, and air conditioning (HVAC) systems in commercial buildings. Due to the size of large commercial HVAC systems, implementing MPC as a single monolithic optimization problem is not practical nor desirable given real-time operating requirements. In this paper, we present a hierarchical decomposition for economic MPC in large-scale commercial HVAC systems using a two-layer approach. We show a sample optimization for a campus of 25 buildings with 500 total zones and a central plant consisting of eight chillers. Then, we discuss an application of the ideas presented here in the recently completed $485-million replacement of the Stanford campus heating and cooling systems and conclude with some of the control theory challenges presented by this new class of applications.

Switched Control Strategies of Aggregated Commercial HVAC Systems for Demand Response in Smart Grids

This work proposes three switched control strategies for aggregated heating, ventilation, and air conditioning (HVAC) systems in commercial buildings to track the automatic generation control (AGC) signal in smart grid. The existing control strategies include the direct load control strategy and the setpoint regulation strategy. The direct load control strategy cannot track the AGC signal when the state of charge (SOC) of the aggregated thermostatically controlled loads (TCLs) exceeds their regulation capacity, while the setpoint regulation strategy provides flexible regulation capacity, but causes larger tracking errors. To improve the tracking performance, we took the advantages of the two control modes and developed three switched control strategies. The control strategies switch between the direct load control mode and the setpoint regulation mode according to different switching indices. Specifically, we design a discrete-time controller and optimize the controller parameter for the setpoint regulation strategy using the Fibonacci optimization algorithm, enabling us to propose two switched control strategies across multiple time steps. Furthermore, we extend the switched control strategies by introducing a two-stage regulation in a single time step. Simulation results demonstrate that the proposed switched control strategies can reduce the tracking errors for frequency regulation.

Transactive Control of Commercial Buildings for Demand Response

Transactive control is a type of distributed control strategy that uses market mechanisms to engage self-interested responsive loads to achieve power balance in the electrical power grid. In this paper, we propose a transactive control approach of commercial building heating, ventilation, and air-conditioning (HVAC) systems for demand response. We first describe the system models, and identify their model parameters using data collected from systems engineering building (SEB) located on our Pacific Northwest National Laboratory campus. We next present a transactive control market structure for commercial building HVAC systems, and describe its agent bidding and market clearing strategies. Several case studies are performed in a simulation environment using building controls virtual test bed (BCVTB) and calibrated SEB EnergyPlus model. We show that the proposed transactive control approach is very effective at peak shaving, load shifting, and strategic conservation for commercial building HVAC systems.

Frequency Regulation From Commercial Building HVAC Demand Response

The expanding penetration of nondispatchable renewable resources within power system generation portfolios is motivating the development of demand-side strategies for balancing generation and load. Commercial heating, ventilation, and air conditioning (HVAC) loads are potential candidates for providing such demand-response (DR) services as they consume significant energy and because of the temporal flexibility offered by their inherent thermal inertia. Several ancillary services markets have recently opened up to participation by DR resources, provided they can satisfy certain performance metrics. We discuss different control strategies for providing frequency regulation DR from commercial HVAC systems and components, and compare performance results from experiments and simulation. We also present experimental results from a single $\sim$ 30 000-m $^{2}$ office building and quantify the DR control performance using standardized performance criteria. Additionally, we evaluate the cost of delivering this service by comparing the energy consumed while providing DR against a counterfactual baseline.

Automated building information modeling for fault detection and diagnostics in commercial HVAC systems

Purpose Organizations involved in facility management (FM) can use building information modeling (BIM) as a knowledge repository to document evolving facility information and to support decisions made by the facility managers during the operational life of a facility. Despite ongoing advances in FM technologies, FM practices in most facilities are still labor intensive, time consuming and often rely on unreliable and outdated information. To address these shortcomings, the purpose of this study is to propose an automated approach that demonstrates the potential of using BIM to develop algorithms that automate decision-making for FM applications. Design/methodology/approach A BIM plug-in tool is developed that uses a fault detection and diagnostics (FDD) algorithm to automate the process of detecting malfunctioning heating, ventilation, and air conditioning (HVAC) equipment. The algorithm connects to a complaint ticket database and automates BIM to determine potentially damaged HVAC system components and develops a plan of action for the facility inspectors accordingly. The approach has been implemented as a case study in an operating facility to improve the process of HVAC system diagnosis and repair. Findings By implementing the proposed application in a case study, the authors found that automated BIM approaches such as the one developed in this study, can be highly beneficial in FM practices by increasing productivity and lowering costs associated with decision-making. Originality/value This study introduces an innovative approach that leverages BIM for automated fault detection in operational buildings. FM personnel in charge of HVAC inspection and repair can highly benefit from the proposed approach, as it eliminates the time required to locate HVAC equipment at fault manually.

Experimental Evaluation of Frequency Regulation From Commercial Building HVAC Systems

Automated demand response can be a valuable resource for ancillary services in the power grid. This paper illustrates this value with the first experimental demonstration of frequency regulation from commercial building heating ventilation and air conditioning (HVAC) systems. The experiments were conducted in Pugh Hall, a 40000 sq. ft. commercial building located at the University of Florida. Detailed are the steps required to make this possible, including control architecture, system identification, and control design. Experiments demonstrate: that satisfactory frequency regulation service can be provided by the HVAC system without noticeable effect on the indoor climate, and the ancillary service provided by this system passes the qualification criteria for participating in the Pennsylvania-New Jersey-Maryland (PJM) interconnection's frequency regulation market.

Ancillary Service to the Grid Through Control of Fans in Commercial Building HVAC Systems

The thermal storage potential in commercial buildings is an enormous resource for providing various ancillary services to the grid. In this paper, we show how fans in Heating, Ventilation, and Air Conditioning (HVAC) systems of commercial buildings alone can provide substantial frequency regulation service, with little change in their indoor environments. A feedforward architecture is proposed to control the fan power consumption to track a regulation signal. The proposed control scheme is then tested through simulations based on a calibrated high fidelity non-linear model of a building. Model parameters are identified from data collected in Pugh Hall, a commercial building located on the University of Florida campus. For the HVAC system under consideration, numerical experiments demonstrate how up to 15% of the rated fan power can be deployed for regulation purpose while having little effect on the building indoor temperature. The regulation signal that can be successfully tracked is constrained in the frequency band [1/τ0,1/τ1], where τ0 ≈ 3 minutes and τ1 ≈ 8 seconds. Our results indicate that fans in existing commercial buildings in the U.S. can provide about 70% of the current national regulation reserve requirements in the aforementioned frequency band. A unique advantage of the proposed control scheme is that assessing the value of the ancillary service provided is trivial, which is in stark contrast to many demand-response programs.