Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

OpenAlex is a bibliographic catalogue of scientific papers, authors and institutions accessible in open access mode, named after the Library of Alexandria. It's citation coverage is excellent and I hope you will find utility in this listing of citing articles!

If you click the article title, you'll navigate to the article, as listed in CrossRef. If you click the Open Access links, you'll navigate to the "best Open Access location". Clicking the citation count will open this listing for that article. Lastly at the bottom of the page, you'll find basic pagination options.

Requested Article:

An integrated federated learning algorithm for short-term load forecasting
Yang Yang, Zijin Wang, Shangrui Zhao, et al.
Electric Power Systems Research (2022) Vol. 214, pp. 108830-108830
Open Access | Times Cited: 35

Showing 1-25 of 35 citing articles:

Smart meter-based energy consumption forecasting for smart cities using adaptive federated learning
Nawaf Abdulla, Mehmet Demirci, Suat Özdemır
Sustainable Energy Grids and Networks (2024) Vol. 38, pp. 101342-101342
Closed Access | Times Cited: 19
A Secure Federated Learning Framework for Residential Short-Term Load Forecasting
Muhammad Akbar Husnoo, Adnan Anwar, Nasser Hosseinzadeh, et al.
IEEE Transactions on Smart Grid (2023) Vol. 15, Iss. 2, pp. 2044-2055
Open Access | Times Cited: 27
Short term power load forecasting based on BES-VMD and CNN-Bi-LSTM method with error correction
Nier Wang, Zhanming Li
Frontiers in Energy Research (2023) Vol. 10
Open Access | Times Cited: 20
Consumers profiling based federated learning approach for energy load forecasting
Atharvan Dogra, Ashima Anand, Jatin Bedi
Sustainable Cities and Society (2023) Vol. 98, pp. 104815-104815
Closed Access | Times Cited: 20
Federated deep learning for smart city edge-based applications
Youcef Djenouri, Tomasz Michalak, Jerry Chun‐Wei Lin
Future Generation Computer Systems (2023) Vol. 147, pp. 350-359
Closed Access | Times Cited: 17
Generative probabilistic prediction of precipitation induced landslide deformation with variational autoencoder and gated recurrent unit
Wencheng Cai, Fuan Lan, Xianhao Huang, et al.
Frontiers in Earth Science (2024) Vol. 12
Open Access | Times Cited: 6
Bootstrap aggregation with Christiano–Fitzgerald random walk filter for fault prediction in power systems
Nathielle Waldrigues Branco, Mariana Santos Matos Cavalca, Raúl García Ovejero
Electrical Engineering (2024) Vol. 106, Iss. 3, pp. 3657-3670
Closed Access | Times Cited: 5
Graph Convolutional Networks based short-term load forecasting: Leveraging spatial information for improved accuracy
Haris Mansoor, Muhammad Shuzub Gull, Huzaifa Rauf, et al.
Electric Power Systems Research (2024) Vol. 230, pp. 110263-110263
Closed Access | Times Cited: 5
EXtreme gradient boosting Mix-Fit-Standalone: A high-performance gradient boosting tree framework for Vertical federated learning
Shimao Xie, Yun Che, Niannian Chen, et al.
Neurocomputing (2025), pp. 129461-129461
Closed Access
A federated and transfer learning based approach for households load forecasting
Gurjot Singh, Jatin Bedi
Knowledge-Based Systems (2024) Vol. 299, pp. 111967-111967
Closed Access | Times Cited: 4
Multiscale-integrated deep learning approaches for short-term load forecasting
Yang Yang, Yuchao Gao, Zijin Wang, et al.
International Journal of Machine Learning and Cybernetics (2024) Vol. 15, Iss. 12, pp. 6061-6076
Open Access | Times Cited: 4
FedDiSC: A computation-efficient federated learning framework for power systems disturbance and cyber attack discrimination
Muhammad Akbar Husnoo, Adnan Anwar, Haftu Tasew Reda, et al.
Energy and AI (2023) Vol. 14, pp. 100271-100271
Open Access | Times Cited: 11
Optimal placement of distributed generation in power distribution system and evaluating the losses and voltage using machine learning algorithms
Akanksha Jain, S. C. Gupta
Frontiers in Energy Research (2024) Vol. 12
Open Access | Times Cited: 3
Mutual Knowledge Distillation Based Federated Learning for Short-term Forecasting in Electric IoT Systems
Cheng Tong, Linghua Zhang, Yin Ding, et al.
IEEE Internet of Things Journal (2024) Vol. 11, Iss. 19, pp. 31190-31205
Closed Access | Times Cited: 3
Personalized federated learning for buildings energy consumption forecasting
Rui Wang, Ling Bai, Rakiba Rayhana, et al.
Energy and Buildings (2024), pp. 114762-114762
Closed Access | Times Cited: 3
Forecasting energy power consumption using federated learning in edge computing devices
Eduardo Montagner de Moraes Sarmento, Iran F. Ribeiro, Pablo Rafael Neves Marciano, et al.
Internet of Things (2023) Vol. 25, pp. 101050-101050
Closed Access | Times Cited: 7
FedWindT: Federated learning assisted transformer architecture for collaborative and secure wind power forecasting in diverse conditions
Qumrish Arooj
Energy (2024) Vol. 309, pp. 133072-133072
Closed Access | Times Cited: 2
A reinforcement learning-based online learning strategy for real-time short-term load forecasting
Xinlin Wang, Hao Wang, Shengping Li, et al.
Energy (2024) Vol. 305, pp. 132344-132344
Open Access | Times Cited: 1
Multiple Load Forecasting of Integrated Renewable Energy System Based on TCN-FECAM-Informer
Mingxiang Li, Tianyi Zhang, Haizhu Yang, et al.
Energies (2024) Vol. 17, Iss. 20, pp. 5181-5181
Open Access | Times Cited: 1
Advancing Electric Load Forecasting: Leveraging Federated Learning for Distributed, Non-Stationary, and Discontinuous Time Series
Lucas Richter, Steve Lenk, Peter Bretschneider
Smart Cities (2024) Vol. 7, Iss. 4, pp. 2065-2093
Open Access | Times Cited: 1
Robust autoregressive bidirectional gated recurrent units model for short-term power forecasting
Yang Yang, Zijin Wang, Shangrui Zhao, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 138, pp. 109453-109453
Open Access | Times Cited: 1
Robust load feature extraction based secondary VMD novel short-term load demand forecasting framework
Miao Zhang, Guowei Xiao, Jianhang Lu, et al.
Electric Power Systems Research (2024) Vol. 239, pp. 111198-111198
Closed Access | Times Cited: 1
A Short-Term Power Load Forecasting Method of Based on the CEEMDAN-MVO-GRU
Taorong Jia, Yao Lixiao, Guoqing Yang, et al.
Sustainability (2022) Vol. 14, Iss. 24, pp. 16460-16460
Open Access | Times Cited: 5
Feature Transfer and Rapid Adaptation for Few-Shot Solar Power Forecasting
Xin Ren, Yimei Wang, Zhi Cao, et al.
Energies (2023) Vol. 16, Iss. 17, pp. 6211-6211
Open Access | Times Cited: 2
Two-Stage Forecasting of Tcn-Gru Short-Term Load Considering Error Compensation and Real-Time Decomposition
Yongsheng Ye, Yang Li, Yanlong Xu, et al.
(2024)
Open Access
Page 1 - Next Page

Scroll to top