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Energy Analytics for Infrastructure : = An Application to Institutional Buildings.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Energy Analytics for Infrastructure :/
Reminder of title:	An Application to Institutional Buildings.
Author:	Naganathan, Hariharan.
Description:	1 online resource (137 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
Subject:	Engineering. -
Online resource:	click for full text (PQDT)
ISBN:	9780355161380

Energy Analytics for Infrastructure : = An Application to Institutional Buildings.
Naganathan, Hariharan.

Energy Analytics for Infrastructure :An Application to Institutional Buildings. - 1 online resource (137 pages)

Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.

Thesis (Ph.D.)--Arizona State University, 2017.

Includes bibliographical references

Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the 'Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355161380Subjects--Topical Terms:

561152
Engineering.
Index Terms--Genre/Form:

554714
Electronic books.

Energy Analytics for Infrastructure : = An Application to Institutional Buildings.
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245 1 0 $a Energy Analytics for Infrastructure : $b An Application to Institutional Buildings.
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500 $a Source: Dissertation Abstracts International, Volume: 79-01(E), Section: B.
500 $a Adviser: Oswald W. Chong.
502 $a Thesis (Ph.D.)--Arizona State University, 2017.
504 $a Includes bibliographical references
520 $a Commercial buildings in the United States account for 19% of the total energy consumption annually. Commercial Building Energy Consumption Survey (CBECS), which serves as the benchmark for all the commercial buildings provides critical input for EnergyStar models. Smart energy management technologies, sensors, innovative demand response programs, and updated versions of certification programs elevate the opportunity to mitigate energy-related problems (blackouts and overproduction) and guides energy managers to optimize the consumption characteristics. With increasing advancements in technologies relying on the 'Big Data,' codes and certification programs such as the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE), and the Leadership in Energy and Environmental Design (LEED) evaluates during the pre-construction phase. It is mostly carried out with the assumed quantitative and qualitative values calculated from energy models such as Energy Plus and E-quest. However, the energy consumption analysis through Knowledge Discovery in Databases (KDD) is not commonly used by energy managers to perform complete implementation, causing the need for better energy analytic framework.
520 $a The dissertation utilizes Interval Data (ID) and establishes three different frameworks to identify electricity losses, predict electricity consumption and detect anomalies using data mining, deep learning, and mathematical models. The process of energy analytics integrates with the computational science and contributes to several objectives which are to.
520 $a 1. Develop a framework to identify both technical and non-technical losses using clustering and semi-supervised learning techniques.
520 $a 2. Develop an integrated framework to predict electricity consumption using wavelet based data transformation model and deep learning algorithms.
520 $a 3. Develop a framework to detect anomalies using ensemble empirical mode decomposition and isolation forest algorithms.
520 $a With a thorough research background, the first phase details on performing data analytics on the demand-supply database to determine the potential energy loss reduction potentials. Data preprocessing and electricity prediction framework in the second phase integrates mathematical models and deep learning algorithms to accurately predict consumption. The third phase employs data decomposition model and data mining techniques to detect the anomalies of institutional buildings.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Engineering. $3 561152
650 4 $a Energy. $3 784773
650 4 $a Sustainability. $3 793436
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710 2 $a Arizona State University. $b Civil, Environmental and Sustainable Engineering. $3 1179779
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10617238 $z click for full text (PQDT)