Packaged Rooftop Units Research Articles

Experimental performance characterization of variable-speed packaged rooftop units with fouled evaporator

Variable-speed packaged rooftop units have been increasingly adopted in the commercial sector in the last decade. Several studies, mainly focused on constant-speed rooftop units, have revealed that poorly maintained units may experience serious performance degradation, due to the frequent presence of common faults such as heat exchangers fouling and refrigerant leakage. In this work, a novel assessment of the impact of evaporator fouling on the performance of variable-speed rooftop units operating in cooling mode is presented, based on the results of a large experimental campaign carried out on a variable-speed 17.5 kWc unit. An innovative and time-saving testing procedure is also proposed, aimed at covering a broad range of operating conditions. Referring to a “heavy fouling”, two faulty scenarios are investigated, referring to (i) conditions where the controller is capable to restore the cooling capacity of the fouled unit, thus balancing the load and (ii) conditions where the intensity of the fault does not allow the controller to restore the cooling capacity. In the former case, an increase in the indoor fan rotating speed and the power consumption (+47%) is observed, resulting in negligible variations of the evaporating pressure and the inlet temperature of cooled air. In the latter case, the load vs. capacity mismatch leads to deviations in indoor air condition from the setpoint value. In this respect, a regression model is proposed to predict the off-design indoor conditions induced by the insufficient capacity of the fouled unit. The model is applied to a high cooling load scenario, assessing a 13.3% reduction in the cooling capacity of the unit; a 3.9 °C deviation from the indoor setpoint temperature is also predicted, together with a decrease in its relative humidity. The analysis provides solid knowledge that could serve as a basis for the future development of reliable fault detection techniques; also, the proposed approach is suitable for the characterization of the effects of other common faults.

Semi-Supervised Machine Learning for Fault Detection and Diagnosis of a Rooftop Unit

Most heating, ventilation, and air-conditioning (HVAC) systems operate with one or more faults that result in increased energy consumption and that could lead to system failure over time. Today, most building owners are performing reactive maintenance only and may be less concerned or less able to assess the health of the system until catastrophic failure occurs. This is mainly because the building owners do not previously have good tools to detect and diagnose these faults, determine their impact, and act on findings. Commercially available fault detection and diagnostics (FDD) tools have been developed to address this issue and have the potential to reduce equipment downtime, energy costs, maintenance costs, and improve occupant comfort and system reliability. However, many of these tools require an in-depth knowledge of system behavior and thermodynamic principles to interpret the results. In this paper, supervised and semi-supervised machine learning (ML) approaches are applied to datasets collected from an operating system in the field to develop new FDD methods and to help building owners see the value proposition of performing proactive maintenance. The study data was collected from one packaged rooftop unit (RTU) HVAC system running under normal operating conditions at an industrial facility in Connecticut. This paper compares three different approaches for fault classification for a real-time operating RTU using semi-supervised learning, achieving accuracies as high as 95.7% using few-shot learning.

Classroom mock-up meeting Standard S12.60

The Acoustical Society of America (ASA) developed a standard entitled Acoustical Performance Criteria, Design Requirements, and Guidelines for Schools (ASA/ANSI S12.60-2010) that provides a minimum set of requirements to help school planners and designers “provide good acoustical characteristics for classrooms and other learning spaces in which speech communication is an important part of the learning process.” In this revision, several details were revised for incorporation into building codes. Despite the obvious benefits of quieter classrooms, incorporating the standard into building codes met with resistance. A common perception was that in order to meet the standard’s requirements, specialized HVAC equipment and installation methods would be necessary, resulting in an installed cost beyond what most schools could afford. Acoustical prediction tools indicated that the requirements could be met using standard HVAC equipment and installation methods. To verify the predictions, a simulated classroom was built and several common types of equipment including a single-zone air handling unit, a high-efficiency water-source heat pump, and a packaged rooftop unit were tested. The selected products were standard catalog offerings, without any special attenuation features, that were operated at typical airflow and static pressures. This paper describes those tests, the conclusion, and the resulting recommendations.

Chicken Swarm Optimization with Deep Learning Based Packaged Rooftop Units Fault Diagnosis Model

Rooftop units (RTUs) were commonly employed in small commercial buildings that represent that can frequently do not take the higher level maintenance that chillers receive. Fault detection and diagnosis (FDD) tools can be employed for RTU methods to ensure essential faults are addressed promptly. In this aspect, this article presents an Optimal Deep Belief Network based Fault Detection and Classification on Packaged Rooftop Units (ODBNFDC-PRTU) model. The ODBNFDC-PRTU technique considers fault diagnosis as a multi-class classification problem and is handled using DL models. For fault diagnosis in RTUs, the ODBNFDC-PRTU model exploits the deep belief network (DBN) classification model, which identifies seven distinct types of faults. At the same time, the chicken swarm optimization (CSO) algorithm-based hyperparameter tuning technique is utilized for resolving the trial and error hyperparameter selection process, showing the novelty of the work. To illustrate the enhanced performance of the ODBNFDC-PRTU algorithm, a comprehensive set of simulations are applied. The comparison study described the improvement of the ODBNFDC-PRTU method over other recent FDD algorithms with maximum accuracy of 99.30% and TPR of 93.09%.

Energy efficiency package for rooftop unit replacement: laboratory testing and validation of energy savings

Over half of commercial building floor area in the US is served by packaged rooftop units (RTUs). With a typical useful life in the 10-20 years range, RTU replacement is a periodic necessity in commercial buildings and an opportunity for upgrading to higher-efficiency units. We designed a package of energy efficiency measures that can be used in conjunction with an RTU replacement, including advanced controls, high-efficiency low pressure-drop air filters, energy monitoring and, optionally, window films and cool-roofs to reduce loads and size of the replacement RTUs. We conducted side-by-side laboratory testing of the package to evaluate energy savings and thermal comfort relative to an existing building baseline. Daily HVAC energy savings for the RTU replacement package averaged 61% across all seasons. The test data showed that the ASHRAE Guideline 36 controls significantly reduced thermal loads and this was a key contributor to the savings, in addition to the higher efficiency of the RTU itself. The laboratory testing also revealed some challenges with installation and commissioning of Guideline 36. Overall, the results demonstrate that significant savings can be realized in existing office buildings by incorporating relatively low-risk, proven measures in conjunction with an RTU replacement.

Impacts of Air-Conditioning Equipment Sizing on Energy Performance of US Office Buildings

Abstract The paper summarizes the results of systematic and comprehensive analysis to investigate the impacts of inadequate sizing air-conditioning (AC) systems on the overall energy consumption of medium and large office buildings located in representative US cities. The effects of proper sizing on the overall and disaggregated AC systems are evaluated in terms of energy consumption, peak demand, equipment run-time, and indoor thermal comfort. The presented analysis covers the performance of a wide range of AC equipment components that serve US office buildings including packaged rooftop units as well as central cooling plants. The analysis results indicate that oversizing penalties can be significant on the annual energy consumption and electrical peak demand as well as capital costs for both medium and large office buildings. In particular, the reliance on simplified calculation methods and rules-of-thumb to determine equipment capacities can lead to significantly oversizing AC systems for office buildings in the vast majority of US climates. The analysis results indicate that 50% oversizing of AC systems can result in increases in annual energy consumption of up to 91% for large offices and 39% for medium office. Moreover, oversizing increases capital costs required for air-conditioning office buildings and extends cycling periods and associated structural stresses and failures of AC equipment.

Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods

This paper proposes and demonstrates a data-driven fault detection and diagnosis strategy for packaged rooftop units using statistical machine learning classification methods. The fault detection and diagnosis task is formulated as a multi-class classification problem. Seven typical rooftop unit faults are discriminated against one another as well as the normal condition. Since experimental data for rooftop units is rare and difficult to obtain, a simulated data library of model faults at steady state operation is used for training and validating the classifications models. Synthetic minority over-sampling technique is used to generate new artificial samples of minority class in order to balance the dataset. Nine well-known classification methods are applied to our dataset, and their performance is compared. The results show that support vector machine, with an overall accuracy rate of 96.2%, is the best classifier, and linear discriminant analysis, with an overall accuracy rate of 76.2%, is the worst classifier. The performance of the classification methods for individual faults is also characterized using true positive rate and false positive rate statistical measures. The relative importance of input variables is also discussed. The high accuracy of the classification methods shows the potential of a data-driven strategy in detecting and diagnosing the rooftop unit faults.

Development and evaluation of HVAC operation schedule detection algorithm

Small- and medium-sized (<5,000 m2 or <50,000 sf) commercial buildings (SMBs) represent over 94% of the U.S. commercial building stock and consume over 1018 kJ (∼1015 Btus) of total site energy. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability and no monitoring or failure management. Therefore, many of the systems in these buildings are operated inefficiently and consume excess energy. SMBs typically use packaged rooftop units (RTUs) that are controlled by an individual thermostat. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy/cost savings. Because there is no monitoring infrastructure in these buildings, deviation of actual heating, ventilation and air conditioning (HVAC) set points and schedules from expected cannot be easily inferred or detected. In this paper, a new methodology is proposed to detect HVAC operation schedules using a symbolic aggregate approximation (SAX) technique. This new method only requires a single temperature measurement (zone temperature) to infer the HVAC schedule. The method was tested and validated using field data from a number of RTUs from six buildings in different climate locations. The confusion metric was used to evaluate how well the method is able to identify the operating schedule of RTUs. Overall, the schedule detection method was successful in detecting the daily operating schedule of RTUs under summer (hot) and winter (cold) weather conditions when energy use is highest or when the RTUs are cycling ON/OFF often. The method can also distinguish different schedules and generate clusters of HVAC operating schedules.

An innovative fault impact analysis framework for enhancing building operations

The primary objective of this paper is to rank building faults based on their impacts on the building energy penalty and occupant thermal comfort penalty considering multiple faults and fault occurrence rates. A fault impact analysis framework is created by incorporating the fault model library with the whole building energy performance simulation (e.g., EnergyPlus used in this study). The fault occurrence rate is introduced as a “meta” parameter in the simulation. This framework involves three essential aspects of conducting a fault impact analysis: fault constructing, fault simulation, and fault impact analysis.A parametric sensitivity analysis was used to determine and rank the criticality of the faults considering the fault concurrence frequency, by using the deep-learning based response surface model (i.e., the multi-layer perceptron regression). The proposed fault analysis framework with ranking was tested and demonstrated for the DOE's prototype medium-sized office in four different climate zones (i.e., Atlanta, Chicago, Miami, and San Francisco) with 12,000 EnergyPlus fault simulations. A total of 129 fault modes from 41 groups of fault models were simulated for the medium-sized office case.The results demonstrate the proposed framework is robust and scalable for the fault impact analysis. The top critical fault for the medium-sized office is the fault of HVAC-Left-ON for the packaged rooftop unit, regarding the site energy, source energy, and HVAC energy. Excluding the fault of HVAC-Left-ON, the top critical faults vary significantly among the four climate zones.

Laboratory-based assessment of HVAC equipment for power grid frequency regulation: Methods, regulation performance, economics, indoor comfort and energy efficiency

Real-time power supply and demand balances are critical to ensure stable power frequency and quality power services. However, the growing integration of renewable energy increasingly challenges the power infrastructure because most of the renewable resources, e.g., solar and wind energy, are intermittent and difficult to predict. To meet stringent power frequency requirements, more fast reacting frequency regulation resources are being brought online among which grid-level batteries are the dominant ones. Buildings consume more than 73% of the electricity in the US, offering significant regulation reserve for the power grid. Variable-speed air-conditioning and heat pump systems are taking an increasing share of the market due to higher efficiency requirements imposed by federal agencies. In addition to efficiency benefits, variable-speed cooling/heating systems are also perfectly suited to provide frequency regulation service since these units can modulate their power continuously over a wide range.This paper presents a methodology and case study results for laboratory-based assessments of power frequency regulation service performance for variable-speed HVAC cooling equipment. The assessment methodology involves the use of both open- and closed-loop testing in psychrometric chambers. The open-loop testing quantifies the maximum possible performance of regulation services that is building independent, whereas the closed-loop testing provides representative performance for regulation services when the equipment must maintain comfort conditions for typical “virtual” buildings. Two air-conditioning units, a variable-speed packaged rooftop unit (RTU) and a split heat pump with an after-market variable-speed retrofit, were utilized for regulation tests performed in psychrometric chambers. Both open-loop and closed-loop regulation control strategies were developed and tested for this equipment. Open-loop test results showed that variable-speed HVAC equipment can provide high quality regulation service with performance scores of up to 0.98. With PJM historical wholesale energy and regulation prices, the estimated regulation credit can offset up to 48% of the electricity price. The closed-loop regulation control was shown to have negligible impact on indoor comfort with temperature fluctuations smaller than 0.1 °C. Power regulation also did not cause any efficiency degradation for the closed-loop tests. In fact, for more humid climate conditions, the electrical input for cooling can even be reduced with regulation control due to reduced moisture removal.

Energy performance evaluation of variable-speed packaged rooftop units using field measurements and building energy simulation

Packaged air-conditioning units are widely used in commercial buildings. Most packaged units in the field are equipped with constant-speed supply fans and constant-speed compressors. Many efforts have been made to increase the operational efficiency of packaged air conditioners by retrofitting existing units with improved controls or developing innovative products with advanced designs. This paper evaluates the energy performance of a packaged unit with all variable-speed components, including the supply fan, the compressor, and the condenser fan. Both a field test and energy simulation are used to quantify the potential of energy savings from the variable-speed unit (VSU) against reference units. In the field test, the reference is a six-year-old existing unit. It was determined through monitoring for one year that the VSU had a 31% higher cooling efficiency than the reference unit. In the simulation, a retail building model was used to compare the performance between the VSU and two reference units—one representing existing units in the field and the other representing units meeting the latest energy code requirements by ASHRAE Standard 90.1. Simulation results showed that depending on the climate, the VSU can save electricity consumption (including both cooling and fan) by 45%–54% relative to the first reference and by 22%–29% relative to the second reference. These findings are valuable for building designers and contractors in selecting packaged rooftop units for both new constructions and retrofits.

Assessments of variable-speed equipment for packaged rooftop units (RTUs) in the United States

Packaged air-conditioning systems, such as rooftop units (RTUs), are widely used in small commercial buildings in the United States. Although new regulations and standards require new air-conditioning equipment to have multi-speed supply fan operation, most of the installed units employ fixed-speed supply fans. The electrical energy usage associated with constant volumetric air delivery fan systems is on the order of half the total RTU electrical energy usage because supply fans operate continuously during the occupied periods in commercial buildings for ventilation. In addition, compressors within RTUs typically feature single- or two-stage capacity controls leading to frequent unit cycling and thus, deteriorated performance in energy efficiency. This paper presents relatively comprehensive assessment results for three RTU variable-speed retrofit options: 1) retrofits of fixed-speed supply fans with variable-speed fans for single-compressor-speed units, 2) replacement of existing fixed-speed RTUs with RTUs having two-speed compressors with variable-speed supply fans and 3) retrofits of RTUs with new RTUs that incorporate variable-speed compressors and fans (indoor and outdoor). The assessment study involves parametric simulations across different building types and locations in the U.S. to understand the performance variation with respect to location and building use. Performance improvements in terms of energy efficiency, operation cost and indoor comfort delivery for the various retrofit options are demonstrated compared to conventional single-stage RTUs with constant speed supply fans. The assessment results show that all three retrofit options offer significant fan energy savings, but much smaller savings potential for compressor and condenser fan electricity consumption. The variable-speed-unit retrofit provides the highest cost savings potentials, ranging from 40 to 250 $ per rated cooling ton per year depending on the building type and location. In addition to efficiency and economic benefits, the two-stage-unit and variable-speed-unit retrofits also lead to improved indoor humidity regulation performance due to reduced unit cycling.