Springer -- Applied Data Analysis and Modeling for Energy Engineers and Scientists PDF

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.

The book also:

• Offers descriptions of numerous data analysis techniques, including but not limited to exploratory data analysis, estimation and model building, inferential methods and data compaction
• Utilizes an effective combination of classical methods with the more recently developed machine learning and automated tools which have become more prevalent in recent years
• Discusses data analysis and modeling issues that are relevant to thermal energy systems, building energy systems, renewable energy systems, energy efficiency, indoor air quality, and environmental engineering