Applied data analysis and modeling for energy engineers and scientists / T. Agami Reddy, Gregor P. Henze.
2023
TA345 .R43 2023
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Details
Title
Applied data analysis and modeling for energy engineers and scientists / T. Agami Reddy, Gregor P. Henze.
Author
Reddy, T. Agami, author.
Edition
Second edition.
ISBN
9783031348693 (electronic bk.)
3031348699 (electronic bk.)
9783031348686
3031348680
3031348699 (electronic bk.)
9783031348686
3031348680
Published
Cham, Switzerland : Springer, [2023]
Language
English
Description
1 online resource (xxi, 609 pages) : illustrations (black and white, and colour).
Other Standard Identifiers
10.1007/978-3-031-34869-3 doi
Call Number
TA345 .R43 2023
Dewey Decimal Classification
620.00285
Summary
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.
Bibliography, etc. Note
Includes bibliographical references and index.
Access Note
Access limited to authorized users.
Source of Description
Description based on print version record.
Added Author
Henze, Gregor P., author.
Available in Other Form
Applied data analysis and modeling for energy engineers and scientists.
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Table of Contents
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.
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.