Browsing by Subject "HVAC"

Now showing 1 - 8 of 8
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    A Bi-Level Data-Driven Framework for Fault-Detection and Diagnosis of HVAC Systems feature explainability
    (Elsevier, 2022-07) Movahed, Paria; Taheri, Saman; Razban, Ali; Mechanical Engineering, School of Engineering and Technology
    Machine learning methods have lately received considerable interest for fault detection diagnostic (FDD) analysis of heating, ventilation, and air conditioning (HVAC) systems due to their high detection accuracy. Meanwhile, HVAC malfunctions are regarded as rare occurrences, hence normal operating data samples are much more accessible than data samples in faulty and malfunctioning conditions. The dominating frequency of normal operation in HVAC datasets have also led to heavily biased classification algorithms within the literature. Moreover, the focus of previous literature has been on increasing accuracy of the models while this leads to a high number of false positives (misleading alarms) in the system. To enhance the performance of diagnostic procedures and fill the mentioned gaps, this study proposes a novel data-driven framework. A bi-level machine learning framework is developed for diagnosing faults in air handling units and rooftop units based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single zone AHU, 85% precision for RTU, and 79% for multi-zone AHU.
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    A Case Study of Safe and Cost-Effective Hospital HVAC Strategies
    (2022-08-02) Caesar, Jeffrey; Ray, Matthew Veto; Koo, Dan; Dalir, Hamid
    The pressures of healthcare facilities to keep patients safe while also maintaining financial viability have been felt in recent years amongst industry leaders. The impacts COVID has had on patient safety and planning has in any way fast-tracked patient safety progress, but certainly at a financial cost. As hospital leaders and facility leaders attempt to grapple with these realities, a facility's operating strategy that addresses both safety and cost should be employed. The below study aims to solve two issues facing hospital facility leadership in regards to the facilities’ HVAC system. The first issue is how to decrease energy consumption and operating expenses in light of industry pressures to improve the financial outlook and secondly, how to increase patient safety as a direct result of COVID-19 realities. Increasing safety and ultimately flexibility can many times increase costs, so utilizing the most appropriate and tested techniques that follow patient safety protocols will be necessary. The importance of this study cannot be understated. As with any healthcare system, improving patient outcomes are at the heart of the industry and especially in light of our recent pandemic. The fundamental question as to how facilities can keep patients safer while simultaneously reducing energy consumption is a tough question to answer, but manageable due to both recent industry experience and up-to-date research on the topic. The methodology will be to conduct a straightforward cost benefit analysis that takes into account both patient safety and energy consumption. The first step will be to gather baseline data for Lutheran Hospital’s HVAC system to gauge current system performance vs. benchmarked performance. Next, the data will inform us as to what strategies to implement to both curb costs and increase patient safety. The third step will be to implement those strategies where possible and measure their benefits. Lastly, a conclusion will be made as to what long-term solutions will be most useful to both this hospital and the other hospitals within Lutheran Health Network.
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    Data-driven Demand Control Ventilation Using Machine Learning CO2 Occupancy Detection Method
    (2020-07) Momeni, Mehdi; Wu, Da-Chun; Razban, Ali; Chen, Jie; Mechanical and Energy Engineering, School of Engineering and Technology
    Heating, ventilation, and air-conditioning (HVAC) system accounts for approximately 40% of total building energy consumption in the United States. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a room excessively and result in a waste of energy. Previous studies show that CO2-based demand-controlled ventilation methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this manuscript, a data-driven control strategy was developed to optimize the energy consumption of supply fans by feed-forward neural network to predict real-time occupancy as an active constraint. As for the validation, the experiment was carried out in an auditorium located on a university campus. The result shows, after utilizing feed-forward neural network to enhance the occupancy estimation, the new primary fan schedule can reduce the daily ventilation energy by 75% when compared to the current on/off control.
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    Embedded System for Sensor Communication and Security
    (2010) An, Feng; Rizkalla, Maher; Li, Lingxi; Du, Yingzi; Salama, Paul; Knieser, Michael
    In this work, inter-integrated circuit mode (I2C) software was used to communicate between sensors and the embedded control system, utilizing PIC182585 MPLAB hardware. These sensors were built as part of a system on board that includes the sensors, microcontroller, and interface circuitry. The hardware includes the PIC18 processor, FPGA chip, and peripherals. A FPGA chip was used to interface the processor with the peripherals in order to operate at the same clock speed. This hardware design features high level of integration, reliability, high precision, and high speed communications. The software was first designed to operate each sensor separately, then the sensor system was integrated (to combine all sensors, microcontroller, and interfacing circuitries), and the software was updated to provide various actions if triggered by the sensors. Actions taken by the processor may include alarming signals that are based on threshold values received from the sensors, and inquiring temperature and CO2 readings. The system was designed for HVAC (heating, ventilating and air conditioning) applications and industrial settings. The overall system incorporating temperature and CO2 sensors was implemented and successfully tested. The response of the multi-sensor system was agreeable with the design parameters. The system may be expanded to include other sensors such as light senor, pressure sensor, etc. Monitoring the threshold values should add to the security features of the integrated communication system. This design features low power consumption (utilizing the sleeping mode of the processors), high speed communications, security, and flexibility to expansion.
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    Energy Optimization of Heating, Ventilation, and Air Conditioning Systems
    (2024-05) Taheri, Saman; Razban, Ali; Chen, Jie; Du, Xiaoping; Chien, Stanley Yung-Ping
    The energy consumption in the building sector is responsible for over 36% of the total energy consumption across the globe. Of all the energy-consumer devices within a building, heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy consumed. This makes HVAC systems a source of preventable and unexplored energy waste that can be tackled by incorporating intelligent operations. Since its inception, model predictive control (MPC) has been one of the prospective solutions for HVAC management systems to reduce both costs and energy usage. Additionally, MPC is becoming increasingly practical as the processing capacity of building automation systems increases and a large quantity of monitored building data becomes available. MPC also provides the potential to improve the energy efficiency of HVAC systems via its capacity to consider limitations, to predict disruptions, and to factor in multiple competing goals such as interior thermal comfort and building energy consumption. In this regard, the opening chapter delves into the evolving landscape of the HVAC industry. It explores how rapid advancements in technology, growing concerns about climate change, and the ever-present need for energy efficiency are driving innovation. The chapter highlights the shift from static to dynamic HVAC systems, where buildings become sensor rich networks enabling advanced control strategies like Model Predictive Control (MPC) and Fault Detection and Diagnosis (FDD). we first provide a comprehensive review of the literature concerning the application of MPC in HVAC systems. Detailed discussions of modeling approaches and optimization algorithms are included. Numerous design aspects such as prediction horizon, time step, and cost function, that impact MPC performance are discussed in detail. The technical characteristics, advantages, and disadvantages of various types of modeling software are discussed. Next, a thorough, real-world case study for the design and implementation of a generalized data-collection and control architecture for HVAC systems in an educational building is proposed. The proposed MPC method adds a supervisory control layer on top of the current BMS by delivering temperature setpoints to the legacy controller. This means that the technique may be used to a variety of current HVAC systems in different commercial buildings. In addition, the utilization of remote web services to host the cloud-based architecture significantly minimizes the amount of technical expertise generally necessary to create such systems. In addition, we provide significant lessons learned from the installation process and we list indicative prices, therefore minimizing uncertainty for other researchers and promoting the use of comparable solutions. Chapter two focuses on Fault Detection and Diagnosis (FDD), a critical component of maintaining optimal HVAC performance and minimizing energy waste. HVAC systems are susceptible to malfunctions over time, leading to increased energy consumption and higher maintenance costs. FDD techniques play a vital role in identifying and diagnosing these faults early on, allowing for timely repairs and preventing further deterioration. This chapter introduces a novel bi-level machine learning framework for diagnosing faults in air handling units. This framework addresses key challenges associated with FDD. A bi-level machine learning framework is developed for diagnosing faults in air handling units (AHUs) and rooftop units (RTUs) based on principal component analysis (PCA), time series anomaly detection, and random forest (RF). By proposing this framework, we address three persistent challenges in this field: (I) minimizing false positives; (II) accounting for data imbalance; and (III) normal condition monitoring of equipment. It is shown that PCA can reduce the dataset dimension with one principal component accounting for 95% of data variance. Also, the random forest could classify the faults with 89% precision for single-zone AHU, 85% precision for RTU, and 79% for multi-zone AHU. Chapter three tackles the practical implementation of Model Predictive Control (MPC) in a real-world commercial building setting. It details the development, implementation, and cost analysis of a universally applicable cloud-based MPC framework for HVAC control systems. This chapter offers valuable insights into the feasibility and effectiveness of MPC in achieving energy efficiency goals while maintaining occupant comfort. The chapter delves into the hardware and software components used for data acquisition and MPC implemen tation. It emphasizes the use of cloud-based microservices to ensure seamless integration with existing building management systems, promoting wider adoption of this advanced control strategy. Three innovative control strategies are presented and evaluated in this chapter. The chapter presents compelling evidence for the effectiveness of these strategies, showcasing significant energy savings of up to 19.21%. Chapter four focuses on Occupancy-based Demand Controlled Ventilation (DCV) as a means to optimize indoor air quality (IAQ) while minimizing energy consumption. This chapter highlights the growing importance of IAQ in the wake of the COVID-19 pandemic and its impact on occupant health and well-being. Current ventilation standards often rely on static occupancy assumptions, which can lead to over-ventilation during unoccupied pe riods and wasted energy. This chapter proposes a dynamic occupant behavior model using machine learning algorithms to predict CO2 concentrations within buildings. The chapter investigates the performance of various machine learning algorithms, ultimately identify ing a Multilayer Perceptron (MLP) as the most effective in predicting CO2 levels under dynamic occupancy conditions. This model allows for real-time modulation of ventilation rates, ensuring adequate IAQ while minimizing energy consumption. The concluding chapter presents experimental findings on the effectiveness of adaptive Variable Frequency Drive (VFD) control strategies in optimizing HVAC energy consump tion. Variable Frequency Drives allow for adjusting the speed of electric motors, including those powering HVAC fans. This chapter explores the potential of using real-time occu pancy predictions to optimize VFD operation. The proposed control strategy demonstrates impressive energy savings, achieving a 51.4% reduction in HVAC fan energy consumption while adhering to ASHRAE IAQ standards. This chapter paves the way for occupant-centric ventilation strategies that prioritize both human health and energy efficiency. These results underscore the potential of predictive control systems to transform building operations to ward greater sustainability and efficiency. The chapter acknowledges the need for further validation through extended monitoring and analysis. In summary, this thesis contributes significantly to the advancement of smart building technologies by proposing practical frameworks for implementing advanced control strategies in HVAC systems. The findings presented here offer valuable insights for building designers, engineers, facility managers, and policymakers interested in creating sustainable, energy efficient, and occupant-centric buildings. The developed frameworks have the potential to be applied across a wide range of building types and climatic conditions, promoting broader adoption of smart building technologies and contributing to a more sustainable built envi ronment.
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    Feed-Forward Neural Network (FFNN) Based Optimization Of Air Handling Units: A State-Of-The-Art Data-Driven Demand-Controlled Ventilation Strategy
    (2020-08) Momeni, Mehdi; Razban, Ali; Chen, Jie; Adams, Eric
    Heating, ventilation and air conditioning systems (HVAC) are the single largest consumer of energy in commercial and residential sectors. Minimizing its energy consumption without compromising indoor air quality (IAQ) and thermal comfort would result in environmental and financial benefits. Currently, most buildings still utilize constant air volume (CAV) systems with on/off control to meet the thermal loads. Such systems, without any consideration of occupancy, may ventilate a zone excessively and result in energy waste. Previous studies showed that CO2-based demand-controlled ventilation (DCV) methods are the most widely used strategies to determine the optimal level of supply air volume. However, conventional CO2 mass balanced models do not yield an optimal estimation accuracy. In this study, feed-forward neural network algorithm (FFNN) was proposed to estimate the zone occupancy using CO2 concentrations, observed occupancy data and the zone schedule. The occupancy prediction result was then utilized to optimize supply fan operation of the air handling unit (AHU) associated with the zone. IAQ and thermal comfort standards were also taken into consideration as the active constraints of this optimization. As for the validation, the experiment was carried out in an auditorium located on a university campus. The results revealed that utilizing neural network occupancy estimation model can reduce the daily ventilation energy by 74.2% when compared to the current on/off control.
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    Identification of Key Parameters Affecting Energy Consumption of an Air Handling Unit
    (ASME, 2016-11) Goodman, David; Chen, Jie; Razban, Ali; Li, Jing; Engineering Technology, School of Engineering and Technology
    Air handling unit system (AHU) is one of the series of mechanical systems that regulate and circulate the air through the ducts inside the buildings. In a commercial setting, air handling units accounted for more than 50% of the total energy cost of the building in 2013. The energy efficiency of the system depends on multiple factors. The set points of discharge air temperature and supply air static pressure are important ones. ASHRAE Standard 90.1-2010 requires multi-zone HVAC systems to implement supply air temperature reset. Energy is wasted if the set points are set constant. However, the waste has never been quantified. The objectives of this study were to (1) develop and validate a mathematical model, which can be used to predict the system performance in response to various controls, specifically the set-point control strategies, and associated energy consumption, and (2) to recommend measures for optimizing the AHU performance by optimizing the setting schedules. In this research, a gray box model was established to evaluate the performance of an AHU. Individual components were modeled using energy and mass balance governing equations that represent the inherent physical processes and interactions with other components. Engineering Equation Solver (EES) was selected for system simulation due to its capabilities of finding the solutions of a large set of complicated equations. The model was validated using two sets of sub hourly real time data. The model performance was evaluated employing Mean Absolute Percentage Error (MAPE) and Root Mean Square Deviation (RMSD). The model was used to create the baseline of energy consumption with constant set points and predict the energy savings using two different reset schedules. The AHU, which serves the entire basement of a campus building on IUPUI campus, was used for this study. It normally has constant set points of discharge air temperature and supply air static pressure. The AHU was monitored using sensors. The data were filtered and transferred to a Building Automation system. Operation information and design specifications of the AHU were collected. Two reset schedules were investigated to determine the better control strategy to minimize energy consumption of the AHU. Discharge air temperature was reset based on return air temperature (RA-T) with a linear reset schedule from March 4 to March 7. Static pressure of the supply air was reset based on the widest open Variable Air Volume (VAV) box damper position from March 20 to March 23. Additionally, uncertainty propagation method was used to identify the dominant parameters affecting the energy consumption. Results indicated that 17% energy savings was achieved using discharge air temperature reset while the energy consumption reduced by 7% using static pressure reset. The results also indicated that outside air temperature, supply airflow rate and return air temperature were the key parameters that impact the overall energy consumption.
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    Spatial Effects in Energy-Efficient Residential HVAC Technology Adoption
    (2013-05) Noonan, Douglas S.; Hsieh, Lin-Han Chiang; Matisoff, Daniel C.
    If your neighborhood adopts greener, energy-efficient residential heating, ventilating, and air conditioning (HVAC) systems, will your proenvironmental behavior become contagious, spilling over into adjacent neighborhoods’ HVAC adoptions? Objective data on more than 300,000 detailed single-family house sale records in the Greater Chicago area from 1992 to 2004 are aggregated to census block-group neighborhoods to answer that question. Spatial lag regression models show that spatial dependence or “contagion” exists for neighborhood adoption of energy-efficient HVACs. Specifically, if 625 of 726 homes in a demonstration neighborhood upgraded to green HVAC, data of this study predict that at least 98 upgrades would occur in adjacent neighborhoods, more than doubling their baseline adoption rates. This spatial multiplier substantially magnifies the effects of factors affecting adoption rates. These results have important policy implications, especially in the context of new standards for neighborhood development, such as Leadership in Energy and Environmental Design (LEED) or Low-Impact Development standards.