Yang Zhou

Personal Details

First Name:	Yang
Middle Name:
Last Name:	Zhou
Suffix:
RePEc Short-ID:	pzh802
	[This author has chosen not to make the email address public]

Affiliation

复旦大学大数据研究院

https://ibd.fudan.edu.cn/main.htm
China, Shanghai

Citations

Many of the citations below have been collected in an experimental project, CitEc, where a more detailed citation analysis can be found. These are citations from works listed in RePEc that could be analyzed mechanically. So far, only a minority of all works could be analyzed. See under "Corrections" how you can help improve the citation analysis.

Articles

1. Zhou, Yang & Zhang, Shuaishuai & Wu, Libo & Tian, Yingjie, 2019. "Predicting sectoral electricity consumption based on complex network analysis," Applied Energy, Elsevier, vol. 255(C).

   Cited by:

   1. Haoqi Qian & Zhengyu Shi & Libo Wu, 2021. "Inferring Economic Condition Uncertainty from Electricity Big Data," Papers 2107.11593, arXiv.org, revised May 2023.
   2. Chen, Ying & Koch, Thorsten & Zakiyeva, Nazgul & Zhu, Bangzhu, 2020. "Modeling and forecasting the dynamics of the natural gas transmission network in Germany with the demand and supply balance constraint," Applied Energy, Elsevier, vol. 278(C).
   3. Du, Yuxian & Lin, Xi & Pan, Ye & Chen, Zhaoxin & Xia, Huan & Luo, Qian, 2023. "Identifying influential airports in airline network based on failure risk factors with TOPSIS," Chaos, Solitons & Fractals, Elsevier, vol. 169(C).
   4. Zhang, Shuaishuai & Wu, Libo & Zhou, Yang, 2020. "The impact of negative list policy on sectoral structure: Based on complex network and DID analysis," Applied Energy, Elsevier, vol. 278(C).
   5. Haben, Stephen & Arora, Siddharth & Giasemidis, Georgios & Voss, Marcus & Vukadinović Greetham, Danica, 2021. "Review of low voltage load forecasting: Methods, applications, and recommendations," Applied Energy, Elsevier, vol. 304(C).

2. Zhou, Yang & Shi, Zhixiong & Shi, Zhengyu & Gao, Qing & Wu, Libo, 2019. "Disaggregating power consumption of commercial buildings based on the finite mixture model," Applied Energy, Elsevier, vol. 243(C), pages 35-46.

   Cited by:

   1. Pamulapati, Trinadh & Mallipeddi, Rammohan & Lee, Minho, 2020. "Multi-objective home appliance scheduling with implicit and interactive user satisfaction modelling," Applied Energy, Elsevier, vol. 267(C).
   2. Shi, Zhengyu & Wu, Libo & Zhou, Yang, 2023. "Predicting household energy consumption in an aging society," Applied Energy, Elsevier, vol. 352(C).
   3. Khalilnejad, Arash & French, Roger H. & Abramson, Alexis R., 2020. "Data-driven evaluation of HVAC operation and savings in commercial buildings," Applied Energy, Elsevier, vol. 278(C).
   4. Jia, Mengshuo & Huang, Shaowei & Wang, Zhiwen & Shen, Chen, 2021. "Privacy-preserving distributed parameter estimation for probability distribution of wind power forecast error," Renewable Energy, Elsevier, vol. 163(C), pages 1318-1332.
   5. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Robust event-based non-intrusive appliance recognition using multi-scale wavelet packet tree and ensemble bagging tree," Applied Energy, Elsevier, vol. 267(C).
   6. Seuk Yen Phoong & Shi Ling Khek & Seuk Wai Phoong, 2022. "The Bibliometric Analysis on Finite Mixture Model," SAGE Open, , vol. 12(2), pages 21582440221, May.
   7. Himeur, Yassine & Alsalemi, Abdullah & Bensaali, Faycal & Amira, Abbes, 2020. "Effective non-intrusive load monitoring of buildings based on a novel multi-descriptor fusion with dimensionality reduction," Applied Energy, Elsevier, vol. 279(C).

3. Zhou, Yang & Ma, Rong & Su, Yun & Wu, Libo, 2019. "Too big to change: How heterogeneous firms respond to time-of-use electricity price," China Economic Review, Elsevier, vol. 58(C).

   Cited by:

   1. Wang, Jinda & Sun, Chunhua & Qi, Chengying & Zhou, Zhigang & Zhao, Jianing & Zheng, Jinfu, 2021. "Promoting the performance of district heating from waste heat recovery in China: A general solving framework based on the two-stage branch evaluation method," Energy, Elsevier, vol. 220(C).
   2. Silvana Tiedemann & Raffaele Sgarlato & Lion Hirth, 2023. "Price elasticity of electricity demand: Using instrumental variable regressions to address endogeneity and autocorrelation of high-frequency time series," Papers 2306.12863, arXiv.org.
   3. Jia, Zhijie & Lin, Boqiang & Wen, Shiyan, 2022. "Electricity market Reform: The perspective of price regulation and carbon neutrality," Applied Energy, Elsevier, vol. 328(C).

More information

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Statistics

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Co-authorship network on CollEc

Corrections

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