From the Back Cover
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.
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.
