Adaptive Optimal Greedy Clustering-Based Monthly Electricity Consumption Forecasting Method-Reference-Cited by-同舟云学术

Adaptive Optimal Greedy Clustering-Based Monthly Electricity Consumption Forecasting Method

Published:2022-11 Issue:6 Volume:58 Page:7881-7891
ISSN:0093-9994
Container-title:IEEE Transactions on Industry Applications
language:
Short-container-title:IEEE Trans. on Ind. Applicat.

Author:

Wang Yuqing¹,Fu Zhiyang²,Wang Fei¹^ORCID,Li Kangping³^ORCID,Li Zhenghui⁴,Zhen Zhao¹^ORCID,Dehghanian Payman⁵^ORCID,Fotuhi-Firuzabad Mahmud⁶^ORCID,Catalao Joao P. S.⁷^ORCID

Affiliation:

1. Department of Electrical Engineering, North China Electric Power University, Baoding, China

2. Department of Mathematics and Physics, North China Electric Power University, Baoding, China

3. College of Smart Energy, Shanghai Jiao Tong University, Shanghai, China

4. State Grid Anhui Electric Power Company, Bengbu Power Supply Company, Bengbu, China

5. Department of Electrical and Computer Engineering, The George Washington University, Washington, DC, USA

6. Center of Excellence in Power System Management and Control, Department of Electrical Engineering, Sharif University of Technology, Tehran, Iran

7. Institute for Systems and Computer Engineering, Technology and Science, Faculty of Engineering of the University of Porto, Porto, Portugal

Funder

National Key R&D Program of China

Science and Technology Project of State Grid Hebei Electric Power Co., Ltd.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Industrial and Manufacturing Engineering,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/28/9956958/09864078.pdf?arnumber=9864078

Reference30 articles.

1. An interactive two-stage retail electricity market for microgrids with peer-to-peer flexibility trading

2. Auto-encoder Neural Network-Based Monthly Electricity Consumption Forecasting Method Using Hourly Data

3. Capacity and output power estimation approach of individual behind-the-meter distributed photovoltaic system for demand response baseline estimation

4. Global energy forecasting competition 2017: Hierarchical probabilistic load forecasting

5. Effective energy consumption forecasting using empirical wavelet transform and long short-term memory

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