Titlebook: Applied Data Analysis and Modeling for Energy Engineers and Scientists; T. Agami Reddy,Gregor P. Henze Textbook 2023Latest edition The Edi

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Applied Data Analysis and Modeling for Energy Engineers and Scientists978-3-031-34869-3

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Mathematical Models and Data Analysis, Next, the distinction between simulation or forward (or well-defined or well-specified) problems, and inverse (or data-driven or ill-defined) problems is highlighted. This chapter introduces analysis approaches relevant to the latter which include calibrated forward models and statistical models id

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Linear Regression Analysis Using Least Squares,ches, such as stepwise regression to automatically select the appropriate subset of regressors, called . or . are described. Subsequently, the inherent assumptions/conditions under which OLS is an optimal estimator are discussed. This is followed by an in-depth treatment of how model . can provide d

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Design of Physical and Simulation Experiments,ns and executing the test sequence where one variable is varied at a time, analyzing the data collected to verify (or refute) statistical hypotheses, and then drawing meaningful conclusions. Selected experimental design methods are discussed such as full and fractional factorial designs, and complet

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Analysis of Time Series Data,rns using . with the time variable appearing as a regressor. Its strength lies in its ability to model the deterministic or structural components of the data in a relatively simple manner, and to provide reasonably accurate forecasts along with their confidence intervals. The . modeling approach is