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Applied data analysis and modeling f...

Reddy, T. Agami.

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Applied data analysis and modeling for energy engineers and scientists

Record Type:	Electronic resources : Monograph/item
Title/Author:	Applied data analysis and modeling for energy engineers and scientists/ by T. Agami Reddy, Gregor P. Henze.
Author:	Reddy, T. Agami.
other author:	Henze, Gregor P.
Published:	Cham :Springer International Publishing : : 2023.,
Description:	xxi, 609 p. :ill., digital ;24 cm.
[NT 15003449]:	Mathematical Models and Data Analysis -- Probability Concepts and Probability Distributions -- Data Collection and Preliminary Data Analysis -- Making Statistical Inferences from Samples -- Linear Regression Analysis Using Least Squares -- Design of Physical and Simulation Experiments -- Optimization Methods -- Analysis of Time Series Data -- Parametric and Non-Parametric Regression Methods -- Inverse Methods for Mechanistic Models -- Statistical Learning Through Data Analytics -- Decision-Making and Sustainability Assessments.
Contained By:	Springer Nature eBook
Subject:	Engineering - Data processing. -
Online resource:	https://doi.org/10.1007/978-3-031-34869-3
ISBN:	9783031348693

Applied data analysis and modeling for energy engineers and scientists
Reddy, T. Agami.

Applied data analysis and modeling for energy engineers and scientists[electronic resource] /by T. Agami Reddy, Gregor P. Henze. - Second edition. - Cham :Springer International Publishing :2023. - xxi, 609 p. :ill., digital ;24 cm.

Mathematical Models and Data Analysis -- Probability Concepts and Probability Distributions -- Data Collection and Preliminary Data Analysis -- Making Statistical Inferences from Samples -- Linear Regression Analysis Using Least Squares -- Design of Physical and Simulation Experiments -- Optimization Methods -- Analysis of Time Series Data -- Parametric and Non-Parametric Regression Methods -- Inverse Methods for Mechanistic Models -- Statistical Learning Through Data Analytics -- Decision-Making and Sustainability Assessments.

Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling approaches are complemented by data analytic algorithmic models such as machine learning and data mining are also discussed. The important societal issues related to the sustainability of energy systems are presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature. Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. Applies statistical and modeling concepts and methods learned in disparate courses to energy processes and systems; Provides a broad and integrative structure meant to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems; Includes practical examples, end-of-chapter problems, case studies, and RStudio code.

ISBN: 9783031348693

Standard No.: 10.1007/978-3-031-34869-3doiSubjects--Topical Terms:

570384
Engineering
--Data processing.

LC Class. No.: TA345 / .R43 2023

Dewey Class. No.: 620.00285

Applied data analysis and modeling for energy engineers and scientists
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245 1 0 $a Applied data analysis and modeling for energy engineers and scientists $h [electronic resource] / $c by T. Agami Reddy, Gregor P. Henze.
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260 $a Cham : $b Springer International Publishing : $b Imprint: Springer, $c 2023.
300 $a xxi, 609 p. : $b ill., digital ; $c 24 cm.
505 0 $a Mathematical Models and Data Analysis -- Probability Concepts and Probability Distributions -- Data Collection and Preliminary Data Analysis -- Making Statistical Inferences from Samples -- Linear Regression Analysis Using Least Squares -- Design of Physical and Simulation Experiments -- Optimization Methods -- Analysis of Time Series Data -- Parametric and Non-Parametric Regression Methods -- Inverse Methods for Mechanistic Models -- Statistical Learning Through Data Analytics -- Decision-Making and Sustainability Assessments.
520 $a Now in a thoroughly revised and expanded second edition, this classroom-tested text demonstrates and illustrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability, statistics, experimental design, regression, optimization, parameter estimation, inverse modeling, risk analysis, decision-making, and sustainability assessment methods to energy processes and systems. It provides a formal structure that offers a broad and integrative perspective to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems. This new edition also reflects recent trends and advances in statistical modeling as applied to energy and building processes and systems. It includes numerous examples from recently published technical papers to nurture and stimulate a more research-focused mindset. How the traditional stochastic data modeling approaches are complemented by data analytic algorithmic models such as machine learning and data mining are also discussed. The important societal issues related to the sustainability of energy systems are presented, and a formal structure is proposed meant to classify the various assessment methods found in the literature. Applied Data Analysis and Modeling for Energy Engineers and Scientists is designed for senior-level undergraduate and graduate instruction in energy engineering and mathematical modeling, for continuing education professional courses, and as a self-study reference book for working professionals. In order for readers to have exposure and proficiency with performing hands-on analysis, the open-source Python and R programming languages have been adopted in the form of Jupyter notebooks and R markdown files, and numerous data sets and sample computer code reflective of real-world problems are available online. Applies statistical and modeling concepts and methods learned in disparate courses to energy processes and systems; Provides a broad and integrative structure meant to enhance knowledge, skills, and confidence to work in applied data analysis and modeling problems; Includes practical examples, end-of-chapter problems, case studies, and RStudio code.
650 0 $a Engineering $x Data processing. $3 570384
650 0 $a Engineering mathematics. $3 516847
650 0 $a Heat engineering. $3 668199
650 1 4 $a Energy Policy, Economics and Management. $3 1532761
650 2 4 $a Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. $3 3591853
650 2 4 $a Data Analysis and Big Data. $3 3538537
650 2 4 $a Electrical Power Engineering. $3 3592498
650 2 4 $a Mathematical Modeling and Industrial Mathematics. $3 891089
700 1 $a Henze, Gregor P. $3 3667835
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-3-031-34869-3
950 $a Energy (SpringerNature-40367)