文献
J-GLOBAL ID:202202274822455114
整理番号:22A0837305
異なる天候/汚染条件下での日全体および拡散日射を予測するための機械学習モデルの評価【JST・京大機械翻訳】
Evaluation of machine learning models for predicting daily global and diffuse solar radiation under different weather/pollution conditions
著者 (7件):
Jia Dongyu
(College of Urban Environment, Lanzhou City University, Lanzhou, 730070, China)
,
Yang Liwei
(Key Laboratory of Desert and Desertification/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China)
,
Yang Liwei
(Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China)
,
Lv Tao
(Huangshan Meteorological Office, Huangshan, 245800, China)
,
Liu Weiping
(Lanzhou Regional Climate Center, Lanzhou, 730000, China)
,
Gao Xiaoqing
(Key Laboratory of Land Surface Process and Climate Change in Cold and Arid Regions/Northwest Institute of Eco-Environment and Resources, Chinese Academy of Sciences, Lanzhou, 730000, China)
,
Zhou Jiaxin
(College of Urban Environment, Lanzhou City University, Lanzhou, 730070, China)
資料名:
Renewable Energy
(Renewable Energy)
巻:
187
ページ:
896-906
発行年:
2022年
JST資料番号:
A0124C
ISSN:
0960-1481
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)