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Energy Optimization of Heating, Vent...

Taheri, Saman,

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Energy Optimization of Heating, Ventilation, and Air Conditioning Systems /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Energy Optimization of Heating, Ventilation, and Air Conditioning Systems // Saman Taheri.
Author:	Taheri, Saman,
Description:	1 electronic resource (223 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
Contained By:	Dissertations Abstracts International86-04B.
Subject:	Indoor air quality. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31606817
ISBN:	9798342142953

Energy Optimization of Heating, Ventilation, and Air Conditioning Systems /
Taheri, Saman,

Energy Optimization of Heating, Ventilation, and Air Conditioning Systems /Saman Taheri. - 1 electronic resource (223 pages)

Source: Dissertations Abstracts International, Volume: 86-04, Section: B.

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).

English

ISBN: 9798342142953Subjects--Topical Terms:

2179141
Indoor air quality.

Energy Optimization of Heating, Ventilation, and Air Conditioning Systems /
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245 1 0 $a Energy Optimization of Heating, Ventilation, and Air Conditioning Systems / $c Saman Taheri.
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300 $a 1 electronic resource (223 pages)
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500 $a Source: Dissertations Abstracts International, Volume: 86-04, Section: B.
500 $a Advisors: Razban, Ali; Chen, Jie; Du Chen, Xiaoping; Chien, Stanley Yung-Ping.
502 $b Ph.D. $c Purdue University $d 2024.
520 $a 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).
546 $a English
590 $a School code: 0183
650 4 $a Indoor air quality. $3 2179141
650 4 $a Software. $2 gtt. $3 619355
650 4 $a Fourier transforms. $3 3545926
650 4 $a Forecasting. $3 547120
650 4 $a Air conditioning. $3 660362
650 4 $a Building automation. $3 3686353
650 4 $a Support vector machines. $3 2058743
650 4 $a Energy efficiency. $3 3555643
650 4 $a Breakdowns. $3 3682712
650 4 $a HVAC. $3 3555644
650 4 $a Building management systems. $3 3683912
650 4 $a Energy consumption. $3 631630
650 4 $a Ventilation. $3 776587
650 4 $a Atmospheric sciences. $3 3168354
650 4 $a Energy. $3 876794
650 4 $a Mathematics. $3 515831
650 4 $a Sustainability. $3 1029978
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710 2 $a Purdue University. $e degree granting institution. $3 3759105
720 1 $a Razban, Ali $e degree supervisor.
720 1 $a Chen, Jie $e degree supervisor.
720 1 $a Du Chen, Xiaoping $e degree supervisor.
720 1 $a Chien, Stanley Yung-Ping $e degree supervisor.
773 0 $t Dissertations Abstracts International $g 86-04B.
790 $a 0183
791 $a Ph.D.
792 $a 2024
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31606817