Download e-book for iPad: Analytics for Smart Energy Management: Tools and by Seog-Chan Oh, Alfred J. Hildreth

By Seog-Chan Oh, Alfred J. Hildreth

ISBN-10: 3319327283

ISBN-13: 9783319327280

ISBN-10: 3319327291

ISBN-13: 9783319327297

This ebook introduces the problems and difficulties that come up while imposing clever strength administration for sustainable production within the automobile production and the analytical instruments and functions to house them. It makes use of a few illustrative examples to give an explanation for strength administration in automobile production, which includes so much different types of production expertise and numerous degrees of power intake.

It demonstrates how analytical instruments may help increase power administration approaches, together with forecasting, intake, and function research, rising new expertise identity in addition to funding judgements for developing shrewdpermanent strength intake practices.

It additionally information useful strength administration structures, making it a worthwhile source for execs taken with genuine power administration procedures, and permitting readers to enforce the techniques and purposes presented.

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Extra resources for Analytics for Smart Energy Management: Tools and Applications for Sustainable Manufacturing

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Appendix B provides a short instruction to Excel Solver by illustrating three examples: (1) SFA parameters estimation (2) DEA LP problem and (3) traveling compressed air expert problem, with an attempt to help readers learn and use GRG method, Simplex LP method and evolutionary method, respectively. Note that this chapter expands on previous researches (Oh and Al 2014) by adding detailed procedures of deriving the log likelihood function and first-order partial derivatives for cost frontier model and a short instruction to Excel Solver.

EN is: loguðe1 ; . ; eN Þ ¼ N X i¼1 log ue ðei Þ   N  p 1 X 1 kei 1 XN 2 2 ¼ À N log logU pffiffiffiffiffiffi À 2 e À N log r þ i¼1 i 2 2 2 2r r2 i¼1 To emphasize that the error term ε depends on the parameter (vector) β, the log likelihood function can be expressed alternatively as:  p À Á 1 l b; r2 ; k ¼ À N log 2 2   N X 1 kðyi À f ðxi ; bÞÞ pffiffiffiffiffiffi À N log r2 þ log U ð2:3Þ 2 r2 i¼1 N 1 X À 2 ðyi À f ðxi ; bÞÞ2 : 2r i¼1 The function lðb; r2 ; kÞ is the log-likelihood function, which depends on parameters to be estimated (in this case β, r2 and λ) and on the data ðx1 ; y1 Þ; .

For example, plants are assumed to use electricity-powered chiller, solvent-borne paint system, gas-fired direct heating system, and air conditioning in place. This chapter uses a commercially available spreadsheet package, Excel, to build the SFA and DEA models. Excel provides an add-on tool called Solver with different solving method options such as Simplex or GRG (Generalized Reduced Gradient). Using the GRG solver method facilitates the maximum likelihood estimation of subset parameters of the proposed SFA models.

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Analytics for Smart Energy Management: Tools and Applications for Sustainable Manufacturing by Seog-Chan Oh, Alfred J. Hildreth


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