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Big data analytic techniques: Predic...

Tannahill, Barnabas K.

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Big data analytic techniques: Predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms.

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Big data analytic techniques: Predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms./
Author:	Tannahill, Barnabas K.
Description:	72 p.
Notes:	Source: Masters Abstracts International, Volume: 53-01.
Contained By:	Masters Abstracts International53-01(E).
Subject:	Engineering, Electronics and Electrical. -
Online resource:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=1556608
ISBN:	9781303921049

Big data analytic techniques: Predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms.
Tannahill, Barnabas K.

Big data analytic techniques: Predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms. - 72 p.

Source: Masters Abstracts International, Volume: 53-01.

Thesis (M.S.)--The University of Texas at San Antonio, 2014.

Large data has been accumulating in all aspects of our lives for quite some time. Advances in sensor technology, the Internet, wireless communication, and inexpensive memory have all contributed to an explosion of "Big Data". System of Systems (SoS) integrate independently operating, non-homogeneous systems to achieve a higher goal than the sum of the parts. Today's SoS are also contributing to the existence of unmanageable "Big Data". Recent efforts have developed a promising approach, called "Data Analytics", which uses statistical and computational intelligence (CI) tools such as principal component analysis (PCA), clustering, fuzzy logic, neuro-computing, evolutionary computation (such as genetic algorithms), Bayesian networks, etc. to reduce the size of "Big Data" to a manageable size and apply these tools to a) extract information, b) build a knowledge base using the derived data, and c) eventually develop a non-parametric model for the "Big Data". This thesis demonstrates how to construct a bridge between SoS and Data Analytics to develop reliable models for such systems. This thesis uses data analytics to generate models to forecast produced photovoltaic and wind energy to assist in the optimization of a micro grid SoS. Tools like fuzzy interference, neural networks, PCA, and genetic algorithms are used.

ISBN: 9781303921049Subjects--Topical Terms:

626636
Engineering, Electronics and Electrical.

Big data analytic techniques: Predicting renewable energy capacity to facilitate the optimization of power plant energy trading and control algorithms.
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500 $a Adviser: Mo Jamshidi.
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