文献
J-GLOBAL ID:202202231753297132
整理番号:22A0950538
エネルギーシステムモデリングにおける複雑性を低減するための深層学習の可能性【JST・京大機械翻訳】
The potential of deep learning to reduce complexity in energy system modeling
著者 (8件):
Kohnen Clara Sophie
(Chair for Energy System Economics, Institute for Future Energy Consumer, Needs and Behavior (FCN), E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany)
,
Priesmann Jan
(Chair for Energy System Economics, Institute for Future Energy Consumer, Needs and Behavior (FCN), E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany)
,
Nolting Lars
(Chair for Energy System Economics, Institute for Future Energy Consumer, Needs and Behavior (FCN), E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany)
,
Kotzur Leander
(Techno-economic Systems Analysis (IEK-3), Forschungszentrum Juelich, Institute of Energy and Climate Research, Juelich, Germany)
,
Kotzur Leander
(JARA-ENERGY, Juelich Aachen Research Alliance, Juelich, Germany)
,
Robinius Martin
(Umlaut Energy GmbH, Aachen, Germany)
,
Praktiknjo Aaron
(Chair for Energy System Economics, Institute for Future Energy Consumer, Needs and Behavior (FCN), E.ON Energy Research Center, RWTH Aachen University, Aachen, Germany)
,
Praktiknjo Aaron
(JARA-ENERGY, Juelich Aachen Research Alliance, Aachen, Germany)
資料名:
International Journal of Energy Research
(International Journal of Energy Research)
巻:
46
号:
4
ページ:
4550-4571
発行年:
2022年
JST資料番号:
A0249B
ISSN:
0363-907X
CODEN:
IJERDN
資料種別:
逐次刊行物 (A)
記事区分:
原著論文
発行国:
イギリス (GBR)
言語:
英語 (EN)