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001 ocn163572859;

003 OCoLC;

005 20141212010419.0;

006 m d f ;

007 cr mn|||||||||;

008 070808s1980 coua st f000 0 eng d;

035 a| (Sirsi) o163572859;

035 a| (OCoLC)163572859;

049 a| ERE#;

074 a| 0429-T-04 (online);

086 0 a| E 1.28:SERI/TR-353-459;

088 a| SERI/TR-353-459;

100 1 a| Krawiec, Frank.=| ^A789396;

245 13 a| An investigation of learning and experience curves /c| Frank Krawiec, John Thornton, Michael Edesess.;

256 a| Electronic data (1 PDF file ; 10 Mb);

260 a| Golden, Colo. :b| Solar Energy Research Institute,c| [1980];

300 a| xix, 170 pages :b| digital, PDF file.;

336 a| text2| rdacontent;

337 a| computer2| rdamedia;

338 a| online resource2| rdacarrier;

500 a| Title from title screen (viewed Apr. 7, 2008).;

500 a| "April 1980.";

500 a| "SERI/TR-353-459.";

520 3 a| This study assesses the applicability of learning and experience curves for predicting future costs of solar technologies, and the major test case is the production economics of heliostats. Alternative methods for estimating cost reductions in systems manufacture are discussed, and procedures for using learning and experience curves to predict costs are outlined. Because adequate production data often do not exist, production histories of analogous products/processes are analyzed and learning and aggregated cost curves for these surrogates estimated. If the surrogate learning curves apply, they can be used to estimate solar technology costs. The steps involved in generating these cost estimates are given. Second-generation glass-steel and inflated-bubble heliostat design concepts, developed by MDAC and GE, respectively, are described; a costing scenario for 25,000 units/yr is detailed; surrogates for cost analysis are chosen; learning and aggregate cost curves are estimated; and aggregate cost curves for the GE and MDAC designs are estimated. However, an approach that combines a neoclassical production function with a learning-by-doing hypothesis is needed to yield a cost relation compatible with the historical learning curve and the traditional cost function of economic theory.;

530 a| Full text available via Internet in .pdf format. Adobe Acrobat Reader required.;

538 a| Mode of access: Internet from the National Renewable Energy Laboratory web site. Address as of 4/7/08: http://www.nrel.gov/docs/legosti/old/459.pdf ; current access available via PURL.;

650 0 a| Solar energyx| Costs.=| ^A61952;

650 0 a| Economic forecasting.=| ^A5068;

700 1 a| Thornton, John P.q| (John Preston),d| 1940-=| ^A789397;

700 1 a| Edesess, Michael.=| ^A789398;

710 2 a| Solar Energy Research Institute.=| ^A117649;

776 0 a| Krawiec, Frankt| Investigation of learning and experience curvesh| xix, 190 p.w| (OCoLC)7167180;

856 40 u| https://purl.fdlp.gov/GPO/LPS92772;

994 a| C0b| ERE;

998 a| 1435807;

998 a| u1435807;

596 a| 1;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| MQ2713493l| JNETm| JOYNERr| Ys| Yt| JNE1DOu| 11/10/2008x| EDOCUMENTz| JERESOURCEo| .STAFF. 0;

An investigation of learning and experience curves / Frank Krawiec, John Thornton, Michael Edesess.

Author/creator	Krawiec, Frank
Other author	Thornton, John P. (John Preston), 1940-
Other author	Edesess, Michael.
Other author	Solar Energy Research Institute.
Format	Electronic
Publication Info	Golden, Colo. : Solar Energy Research Institute, [1980]
Description	xix, 170 pages : digital, PDF file.
Supplemental Content	https://purl.fdlp.gov/GPO/LPS92772
Subjects	Solar energy--Costs. Economic forecasting.

Data characteristics	Electronic data (1 PDF file ; 10 Mb)
Summary	This study assesses the applicability of learning and experience curves for predicting future costs of solar technologies, and the major test case is the production economics of heliostats. Alternative methods for estimating cost reductions in systems manufacture are discussed, and procedures for using learning and experience curves to predict costs are outlined. Because adequate production data often do not exist, production histories of analogous products/processes are analyzed and learning and aggregated cost curves for these surrogates estimated. If the surrogate learning curves apply, they can be used to estimate solar technology costs. The steps involved in generating these cost estimates are given. Second-generation glass-steel and inflated-bubble heliostat design concepts, developed by MDAC and GE, respectively, are described; a costing scenario for 25,000 units/yr is detailed; surrogates for cost analysis are chosen; learning and aggregate cost curves are estimated; and aggregate cost curves for the GE and MDAC designs are estimated. However, an approach that combines a neoclassical production function with a learning-by-doing hypothesis is needed to yield a cost relation compatible with the historical learning curve and the traditional cost function of economic theory.
General note	Title from title screen (viewed Apr. 7, 2008).
General note	"April 1980."
General note	"SERI/TR-353-459."
Other forms	Full text available via Internet in .pdf format. Adobe Acrobat Reader required.
Technical details	Mode of access: Internet from the National Renewable Energy Laboratory web site. Address as of 4/7/08: http://www.nrel.gov/docs/legosti/old/459.pdf ; current access available via PURL.
Issued in other form	Krawiec, Frank Investigation of learning and experience curves xix, 190 p.
Technical rpt number	SERI/TR-353-459
GPO item number	0429-T-04 (online)
Govt. docs number	E 1.28:SERI/TR-353-459

An investigation of learning and experience curves / Frank Krawiec, John Thornton, Michael Edesess.

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