Applied Data Analysis and Modeling for Energy Engineers and Scientists

2011
ISBN: 978-1-4899-8636-8
Verlag: Springer US

Buch, Englisch, 430 Seiten, Previously published in hardcover, Format (B × H): 210 mm x 279 mm, Gewicht: 1093 g

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.

Zielgruppe

Research

Autoren/Hrsg.

Reddy, T. Agami

Fachgebiete

• Technische Wissenschaften Technik Allgemein Mathematik für Ingenieure
• Interdisziplinäres Wissenschaften Wissenschaften: Forschung und Information Datenanalyse, Datenverarbeitung
• Technische Wissenschaften Energietechnik | Elektrotechnik Energietechnik & Elektrotechnik
• Mathematik | Informatik Mathematik Numerik und Wissenschaftliches Rechnen Angewandte Mathematik, Mathematische Modelle
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Automatische Datenerfassung, Datenanalyse

Inhaltsverzeichnis

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.