Applied Data Analysis and Modeling for Energy Engineers and Scientists
Springer International Publishing (Verlag)
978-3-031-34868-6 (ISBN)
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 methods complement data analytic algorithmic approaches such as machine learning and data mining is also discussed. The important societal issue related to the sustainability of energy systems is 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.T Agami Reddy, PhD, is Professor Emeritus of Energy and Environment at Arizona State University. He is a licensed mechanical engineer with about 40 years of teaching and research experience in solar energy system simulation and testing, building energy inverse data analysis and modeling, energy efficient and green building technologies, and sustainable/resilience as applied to energy infrastructure systems. He has over 190 journal publications (three of which received best paper awards), edited four conference proceedings or special journal issues, contributed to seven handbook chapters and written three textbooks: The Design and Sizing of Active Solar Systems (Oxford Univ Press, 1987), Applied Data Analysis and Modeling for Energy Engineers and Scientists (Springer, 2011), and Heating and Cooling of Buildings (3rd ed., CRC, 2016). He has completed about 50 research projects with funding from organizations such as NSF, USDOE, EPA, NIST, Homeland Security, ASHRAE and several building control companies. He is Fellow of ASME (past chair of Solar Energy Division, past Associate Editor of ASME Journal of Solar Energy Engineering, and ASME Journal of Sustainable Buildings and Cities) and Fellow of ASHRAE (past Chair of Research Advisory Committee, and past member of Research Advisory Panel). He received his PhD and MS from the Laboratoire de Thermodynamique et d'Energetique, University of Perpignan, France. Gregor P Henze, PhD, PE, is Professor and Charles V Schelke Chair of Architectural Engineering at the University of Colorado Boulder, where his teaching focuses on building and district energy systems, i.e., high-performance building design, building control and automation systems, data science for energy applications, and sustainable building design. His research emphasizes advanced building control approaches, fault detection and diagnosis, characterization of building occupant behavior, human presence detection, sensor fusion systems, as well as the integration of building energy system operations with the electric grid system. He is the primary author of more than 170 research articles, four of which have received best paper awards, and received three patents. He received the 2011 Colorado Cleantech Industry Association's Research and Commercialization Award. Prof. Henze is a professional mechanical engineer, high-performance building design professional, editorial board member for Journal Building Performance Simulation, Fellow of the Renewable and Sustainable Energy Institute, joint professor at the National Renewable Energy Laboratory, as well as co-founder and chief scientist of QCoefficient, Inc., an engineering firm developing real-time optimal control solutions for grid-interactive efficient buildings. He received his Diplom-Ingenieur from the Technical University of Berlin, Germany, his MS from Oregon State University, and his PhD from the University of Colorado Boulder.
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.
| Erscheinungsdatum | 20.10.2023 |
|---|---|
| Zusatzinfo | XXI, 609 p. 400 illus., 194 illus. in color. |
| Verlagsort | Cham |
| Sprache | englisch |
| Maße | 210 x 279 mm |
| Gewicht | 1770 g |
| Themenwelt | Mathematik / Informatik ► Mathematik |
| Naturwissenschaften ► Biologie ► Ökologie / Naturschutz | |
| Technik ► Elektrotechnik / Energietechnik | |
| Schlagworte | Algorithmic modeling • Applied Data Analysis • Decision Analysis • Deep learning • Linear modeling • Mathematical Models • Modeling methods • Probability concepts • Thermal Systems • Time Series Analysis |
| ISBN-10 | 3-031-34868-0 / 3031348680 |
| ISBN-13 | 978-3-031-34868-6 / 9783031348686 |
| Zustand | Neuware |
| Informationen gemäß Produktsicherheitsverordnung (GPSR) | |
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