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001482656 001__ 1482656
001482656 003__ OCoLC
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001482656 020__ $$a9783031348693$$q(electronic bk.)
001482656 020__ $$a3031348699$$q(electronic bk.)
001482656 020__ $$z9783031348686
001482656 020__ $$z3031348680
001482656 0247_ $$a10.1007/978-3-031-34869-3$$2doi
001482656 035__ $$aSP(OCoLC)1405972716
001482656 040__ $$aGW5XE$$beng$$erda$$epn$$cGW5XE$$dEBLCP$$dYDX$$dOCLCO$$dOCLCF
001482656 049__ $$aISEA
001482656 050_4 $$aTA345$$b.R43 2023
001482656 08204 $$a620.00285$$223/eng/20231025
001482656 1001_ $$aReddy, T. Agami,$$eauthor.$$1https://isni.org/isni/0000000384730088
001482656 24510 $$aApplied data analysis and modeling for energy engineers and scientists /$$cT. Agami Reddy, Gregor P. Henze.
001482656 250__ $$aSecond edition.
001482656 264_1 $$aCham, Switzerland :$$bSpringer,$$c[2023]
001482656 300__ $$a1 online resource (xxi, 609 pages) :$$billustrations (black and white, and colour).
001482656 336__ $$atext$$btxt$$2rdacontent
001482656 337__ $$acomputer$$bc$$2rdamedia
001482656 338__ $$aonline resource$$bcr$$2rdacarrier
001482656 504__ $$aIncludes bibliographical references and index.
001482656 5050_ $$aMathematical Models and Data Analysis -- Probability Concepts and Probability Distributions -- Data Collection and Preliminary Data Analysis -- Making Statistical Inferences from Samples -- Linear Regression Analysis Using Least Squares -- Design of Physical and Simulation Experiments -- Optimization Methods -- Analysis of Time Series Data -- Parametric and Non-Parametric Regression Methods -- Inverse Methods for Mechanistic Models -- Statistical Learning Through Data Analytics -- Decision-Making and Sustainability Assessments.
001482656 506__ $$aAccess limited to authorized users.
001482656 520__ $$aNow 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.
001482656 588__ $$aDescription based on print version record.
001482656 650_6 $$aIngénierie$$xInformatique.
001482656 650_6 $$aMathématiques de l'ingénieur.
001482656 650_6 $$aThermique.
001482656 650_0 $$aEngineering$$xData processing.
001482656 650_0 $$aEngineering mathematics.$$xMathematics$$0(DLC)sh2009118797
001482656 650_0 $$aHeat engineering.$$0(DLC)sh 85059775
001482656 655_0 $$aElectronic books.
001482656 7001_ $$aHenze, Gregor P.,$$eauthor.
001482656 77608 $$iPrint version:$$aReddy, T. Agami.$$tApplied data analysis and modeling for energy engineers and scientists.$$bSecond edition.$$dCham : Springer, 2023$$z9783031348686$$w(OCoLC)1388641698
001482656 852__ $$bebk
001482656 85640 $$3Springer Nature$$uhttps://univsouthin.idm.oclc.org/login?url=https://link.springer.com/10.1007/978-3-031-34869-3$$zOnline Access$$91397441.1
001482656 909CO $$ooai:library.usi.edu:1482656$$pGLOBAL_SET
001482656 980__ $$aBIB
001482656 980__ $$aEBOOK
001482656 982__ $$aEbook
001482656 983__ $$aOnline
001482656 994__ $$a92$$bISE