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中国科学院数学与系统科学研究院机构知识库
KMS Of Academy of mathematics and systems sciences, CAS
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Short-term load forecasting with an improved dynamic decomposition-reconstruction-ensemble approach
期刊论文
ENERGY, 2023, 卷号: 263, 页码: 16
作者:
Yang, Dongchuan
;
Guo, Ju-e
;
Li, Yanzhao
;
Sun, Shaolong
;
Wang, Shouyang
收藏
  |  
浏览/下载:57/0
  |  
提交时间:2023/02/07
Short -term load forecasting
Time series modeling
Dynamic decomposition-reconstruction tech
nique
Neural networks
A novel two-stage seasonal grey model for residential electricity consumption forecasting
期刊论文
ENERGY, 2022, 卷号: 258, 页码: 18
作者:
Du, Pei
;
Guo, Ju'e
;
Sun, Shaolong
;
Wang, Shouyang
;
Wu, Jing
收藏
  |  
浏览/下载:65/0
  |  
提交时间:2023/02/07
Electricity consumption forecasting
Grey model
Seasonal factor
Error correction strategy
Understanding the linkage-dependence structure between oil and gas markets: A new perspective
期刊论文
ENERGY, 2022, 卷号: 257, 页码: 18
作者:
Wei, Zhaohao
;
Chai, Jian
;
Dong, Jichang
;
Lu, Quanying
收藏
  |  
浏览/下载:60/0
  |  
提交时间:2023/02/07
Oil and gas markets
Characteristic evolution
Intrinsic mechanism quantification
Non-parametric test
Multivariate GARCH-Copula
CEEMDAN-JADE-RT
Conformalized temporal convolutional quantile regression networks for wind power interval forecasting
期刊论文
ENERGY, 2022, 卷号: 248, 页码: 16
作者:
Hu, Jianming
;
Luo, Qingxi
;
Tang, Jingwei
;
Heng, Jiani
;
Deng, Yuwen
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  |  
浏览/下载:55/0
  |  
提交时间:2023/02/07
Wind power interval prediction
Temporal convolutional network
Conformalized quantile regression
Influential study of novel microorganism and nanoparticles during heat and mass transport in Homann flow of visco-elastic materials
期刊论文
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2022, 卷号: 131, 页码: 12
作者:
Ahmad, Latif
;
Irfan, Muhammad
;
Javed, Saleem
;
Khan, M. Ijaz
;
Khan, M. Riaz
;
Niazi, Usama Muhammad
;
Alqarni, Ali O.
;
El-Zahar, Essam Roshdy
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  |  
浏览/下载:57/0
  |  
提交时间:2023/02/07
Homann flow
Visco-elastic fluid
Nanoparticles
Microorganism
Convective boundary conditions
Newtonian heating
Numerical analysis of heat transfer and friction drag relating to the effect of Joule heating, viscous dissipation and heat generation/absorption in aligned MHD slip flow of a nanofluid
期刊论文
INTERNATIONAL COMMUNICATIONS IN HEAT AND MASS TRANSFER, 2022, 卷号: 131, 页码: 9
作者:
Khan, M. Riaz
;
Mao, Shipeng
;
Deebani, Wejdan
;
Elsiddieg, Awatif M. A.
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  |  
浏览/下载:73/0
  |  
提交时间:2023/02/07
Heat transfer
Friction drag
Joule heating
Aligned magnetic field
Viscous dissipation
Convective condition
Transport properties of mixed convective nano-material flow considering the generalized fourier law and a vertical surface: Concept of caputo-time fractional derivative
期刊论文
PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART A-JOURNAL OF POWER AND ENERGY, 2022, 页码: 11
作者:
Raza, Ali
;
Khan, Sami Ullah
;
Farid, Saadia
;
Khan, Muhammad Ijaz
;
Khan, M. Riaz
;
Ul Haq, Absar
;
Elsiddieg, Awatif M. A.
;
Malik, M. Y.
;
Alsallami, Shami A. M.
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  |  
浏览/下载:62/0
  |  
提交时间:2023/02/07
Newtonian
slip
convective flow
fourier's law
fractional derivatives
The impact of China's low-carbon transition on economy, society and energy in 2030 based on CO2 emissions drivers
期刊论文
ENERGY, 2022, 卷号: 239, 页码: 12
作者:
Shi, Huiting
;
Chai, Jian
;
Lu, Quanying
;
Zheng, Jiali
;
Wang, Shouyang
收藏
  |  
浏览/下载:125/0
  |  
提交时间:2022/04/02
CO2 emissions
Low-carbon economy
Transition impact
Bayesian network
A novel machine learning-based electricity price forecasting model based on optimal model selection strategy
期刊论文
ENERGY, 2022, 卷号: 238, 页码: 14
作者:
Yang, Wendong
;
Sun, Shaolong
;
Hao, Yan
;
Wang, Shouyang
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  |  
浏览/下载:158/0
  |  
提交时间:2022/04/02
Electricity price
Forecasting
Hybrid model
Model selection
Kernel-based extreme learning machine
Multi-step-ahead wind speed forecasting based on a hybrid decomposition method and temporal convolutional networks
期刊论文
ENERGY, 2022, 卷号: 238, 页码: 22
作者:
Li, Dan
;
Jiang, Fuxin
;
Chen, Min
;
Qian, Tao
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  |  
浏览/下载:137/0
  |  
提交时间:2022/04/02
Wind speed forecasting
Ensemble patch transform
Complete ensemble empirical mode
decomposition
Temporal convolutional network
Hybrid method