LEADER 05107nam 22006975 450 001 9910754089103321 005 20231018121958.0 010 $a3-031-34869-9 024 7 $a10.1007/978-3-031-34869-3 035 $a(MiAaPQ)EBC30800278 035 $a(CKB)28530619900041 035 $a(Au-PeEL)EBL30800278 035 $a(DE-He213)978-3-031-34869-3 035 $a(PPN)272919128 035 $a(EXLCZ)9928530619900041 100 $a20231018d2023 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aApplied Data Analysis and Modeling for Energy Engineers and Scientists /$fby T. Agami Reddy, Gregor P. Henze 205 $a2nd ed. 2023. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (622 pages) 311 $a9783031348686 320 $aIncludes bibliographical references and index. 327 $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. 330 $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. 606 $aEnergy policy 606 $aEnergy and state 606 $aStatistics 606 $aQuantitative research 606 $aElectric power production 606 $aMathematical models 606 $aEnergy Policy, Economics and Management 606 $aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences 606 $aData Analysis and Big Data 606 $aElectrical Power Engineering 606 $aMathematical Modeling and Industrial Mathematics 615 0$aEnergy policy. 615 0$aEnergy and state. 615 0$aStatistics. 615 0$aQuantitative research. 615 0$aElectric power production. 615 0$aMathematical models. 615 14$aEnergy Policy, Economics and Management. 615 24$aStatistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 615 24$aData Analysis and Big Data. 615 24$aElectrical Power Engineering. 615 24$aMathematical Modeling and Industrial Mathematics. 676 $a620.00285 700 $aReddy$b T. Agami$01434312 702 $aHenze$b Gregor P. 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910754089103321 996 $aApplied Data Analysis and Modeling for Energy Engineers and Scientists$93587962 997 $aUNINA