Applied Data Analysis and Modeling for Energy Engineers and Scientists

• Κύριος συγγραφέας: Reddy, T. Agami (Συγγραφέας)
• Συγγραφή απο Οργανισμό/Αρχή: SpringerLink (Online service)
• Μορφή: Ηλεκτρονική πηγή Ηλ. βιβλίο
• Γλώσσα: English
• Έκδοση: Boston, MA : Springer US : Imprint: Springer, 2011.
• Θέματα: Energy.; Energy efficiency.; Probabilities.; Statistics.; Thermodynamics.; Heat engineering.; Heat transfer.; Mass transfer.; Energy Efficiency (incl. Buildings).; Engineering Thermodynamics, Heat and Mass Transfer.; Probability Theory and Stochastic Processes.; Statistics for Engineering, Physics, Computer Science, Chemistry and Earth Sciences.

 Applied Data Analysis and Modeling for Energy Engineers and Scientists fills an identified gap in engineering and science education and practice for both students and practitioners. It demonstrates how to apply concepts and methods learned in disparate courses such as mathematical modeling, probability,statistics, experimental design, regression, model building, optimization, risk analysis and decision-making to actual engineering processes and systems. The text provides a formal structure that offers a basic, broad and unified perspective,while imparting the knowledge, skills and confidence to work in data analysis and modeling. This volume uses numerous solved examples, published case studies from the author’s own research, and well-conceived problems in order to enhance comprehension levels among readers and their understanding of the “processes”along with the tools. Applied Data Analysis and Modeling for Energy Engineers and Scientists is an ideal volume for researchers, practitioners, and senior level or graduate students working in energy engineering, mathematical modeling and other related areas.  . 

 Models, data analysis and decision making -- Probability concepts and probability distributions -- Data collection and preliminary data analysis -- Making statistical inferences from samples -- Estimation of linear model parameters using least squares -- Designed experiments and analysis of non-intrusive data -- Time series models -- Topics in optimization, parameter estimation and clustering methods -- Inverse problems and illustrative examples -- Decision analysis and risk modeling.