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049 a| ERE#;

074 a| 0430-P-17 (online);

086 0 a| E 9.28:NREL/PO-2 C 00-76856;

100 1 a| Duplyakin, Dmitry,e| author.?| UNAUTHORIZED;

245 10 a| Using machine learning to predict future temperature outputs in geothermal systems /c| Dmitry Duplyakin [and 4 others].;

264 1 a| [Golden, Colorado] :b| National Renewable Energy Laboratory,c| 2020.;

300 a| 1 online resource (6 pages) :b| color illustrations.;

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337 a| computerb| c2| rdamedia;

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500 a| "GRC 2020 Virtual Annual Meeting and Expo, October 18-23, 2020.";

500 a| "This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Geothermal Technologies Office"--Page 6.;

500 a| This is a publication issued by the United States federal government or produced with federal funds.;

500 a| In scope of the U.S. Government Publishing Office Cataloging and Indexing Program (C&I) and Federal Depository Library Program (FDLP) distribution.;

504 a| Includes bibliographical references (page 6).;

536 a| Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Geothermal Technologies Officeb| DE-AC36-08GO28308;

588 a| Description based on online resource; title from PDF title page (NLR, viewed March 10, 2026).;

650 0 a| Geothermal power plantsz| United States.=| ^A835246;

650 0 a| Machine learning.0| http://id.loc.gov/authorities/subjects/sh85079324=| ^A145211;

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Using machine learning to predict future temperature outputs in geothermal systems / Dmitry Duplyakin [and 4 others].

Author/creator	Duplyakin, Dmitry author.
Other author	National Renewable Energy Laboratory (U.S.), author.
Format	Electronic
Publication	[Golden, Colorado] : National Renewable Energy Laboratory, 2020.
Description	1 online resource (6 pages) : color illustrations.
Supplemental Content	https://purl.fdlp.gov/GPO/gpo253806
Supplemental Content	Address at time of PURL creation
Subjects	Geothermal power plants--United States. Machine learning.

Series	NREL/PO ; 2C00-76856 NREL/PO ; 2C00-76856. http://id.loc.gov/authorities/names/no2008020017 ^A782221
General note	"GRC 2020 Virtual Annual Meeting and Expo, October 18-23, 2020."
General note	"This work was authored in part by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Geothermal Technologies Office"--Page 6.
General note	This is a publication issued by the United States federal government or produced with federal funds.
General note	In scope of the U.S. Government Publishing Office Cataloging and Indexing Program (C&I) and Federal Depository Library Program (FDLP) distribution.
Bibliography note	Includes bibliographical references (page 6).
Funding information	Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Geothermal Technologies Office DE-AC36-08GO28308
Source of description	Description based on online resource; title from PDF title page (NLR, viewed March 10, 2026).
Genre/form	Conference papers and proceedings.
GPO item number	0430-P-17 (online)
Govt. docs number	E 9.28:NREL/PO-2 C 00-76856

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Using machine learning to predict future temperature outputs in geothermal systems / Dmitry Duplyakin [and 4 others].

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