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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:

Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra-short-term wind farm cluster power forecasting method
Fei Wang, Peng Chen, Zhao Zhen, et al.
Applied Energy (2022) Vol. 323, pp. 119579-119579
Closed Access | Times Cited: 61

Showing 1-25 of 61 citing articles:

Spatio-temporal wind speed forecasting using graph networks and novel Transformer architectures
Lars Ødegaard Bentsen, Narada Dilp Warakagoda, Roy Stenbro, et al.
Applied Energy (2022) Vol. 333, pp. 120565-120565
Open Access | Times Cited: 83
A spatio-temporal forecasting model using optimally weighted graph convolutional network and gated recurrent unit for wind speed of different sites distributed in an offshore wind farm
Xuefang Xu, Shiting Hu, Huaishuang Shao, et al.
Energy (2023) Vol. 284, pp. 128565-128565
Closed Access | Times Cited: 48
Sustainable urban energy solutions: Forecasting energy production for hybrid solar-wind systems
Ali Javaid, Muhammad Sajid, Emad Uddin, et al.
Energy Conversion and Management (2024) Vol. 302, pp. 118120-118120
Closed Access | Times Cited: 21
Ultra-Short-Term Distributed Photovoltaic Power Probabilistic Forecasting Method Based on Federated Learning and Joint Probability Distribution Modeling
Yübo Wang, Chao Huo, Fei Xu, et al.
Energies (2025) Vol. 18, Iss. 1, pp. 197-197
Open Access | Times Cited: 2
Safe Deep Reinforcement Learning for Microgrid Energy Management in Distribution Networks With Leveraged Spatial–Temporal Perception
Yujian Ye, Hongru Wang, Peiling Chen, et al.
IEEE Transactions on Smart Grid (2023) Vol. 14, Iss. 5, pp. 3759-3775
Closed Access | Times Cited: 29
Interpretable wind speed forecasting with meteorological feature exploring and two-stage decomposition
Binrong Wu, Sihao Yu, Lu Peng, et al.
Energy (2024) Vol. 294, pp. 130782-130782
Closed Access | Times Cited: 14
GGNet: A novel graph structure for power forecasting in renewable power plants considering temporal lead-lag correlations
Nanyang Zhu, Ying Wang, Kun Yuan, et al.
Applied Energy (2024) Vol. 364, pp. 123194-123194
Closed Access | Times Cited: 13
Short-Term Power Forecasting of Wind Farm Cluster Based on Global Information Adaptive Perceptual Graph Convolution Network
Mao Yang, Chaoyi Ju, Y. Huang, et al.
IEEE Transactions on Sustainable Energy (2024) Vol. 15, Iss. 3, pp. 2063-2076
Closed Access | Times Cited: 12
Spatial correlation learning based on graph neural network for medium-term wind power forecasting
Beizhen Zhao, Xin He, Shaolin Ran, et al.
Energy (2024) Vol. 296, pp. 131164-131164
Closed Access | Times Cited: 10
A short-term power prediction method based on numerical weather prediction correction and the fusion of adaptive spatiotemporal graph feature information for wind farm cluster
Mao Yang, Chao Han, Wei Zhang, et al.
Expert Systems with Applications (2025) Vol. 274, pp. 126979-126979
Closed Access | Times Cited: 1
Fluctuation pattern recognition based ultra-short-term wind power probabilistic forecasting method
Huijing Fan, Zhao Zhen, Nian Liu, et al.
Energy (2022) Vol. 266, pp. 126420-126420
Closed Access | Times Cited: 35
Mixformer: Mixture transformer with hierarchical context for spatio-temporal wind speed forecasting
Tangjie Wu, Qiang Ling
Energy Conversion and Management (2023) Vol. 299, pp. 117896-117896
Closed Access | Times Cited: 20
Renewable energy forecasting: A self-supervised learning-based transformer variant
Jiarui Liu, Yuchen Fu
Energy (2023) Vol. 284, pp. 128730-128730
Closed Access | Times Cited: 16
An ultra-short-term wind power prediction method based on spatial-temporal attention graph convolutional model
Yunlong Lv, Qin Hu, Hang Xu, et al.
Energy (2024) Vol. 293, pp. 130751-130751
Closed Access | Times Cited: 6
Interpretable multi-graph convolution network integrating spatial-temporal attention and dynamic combination for wind power forecasting
Yongning Zhao, Haohan Liao, Shiji Pan, et al.
Expert Systems with Applications (2024) Vol. 255, pp. 124766-124766
Closed Access | Times Cited: 6
Spatio-Temporal Graph Neural Network and Pattern Prediction Based Ultra-Short-Term Power Forecasting of Wind Farm Cluster
Xiaoyan Liu, Yiran Zhang, Zhao Zhen, et al.
IEEE Transactions on Industry Applications (2023) Vol. 60, Iss. 1, pp. 1794-1803
Closed Access | Times Cited: 14
Short-term load forecasting using spatial-temporal embedding graph neural network
Chuyuan Wei, Dechang Pi, Mingtian Ping, et al.
Electric Power Systems Research (2023) Vol. 225, pp. 109873-109873
Closed Access | Times Cited: 13
Short-term wind farm cluster power prediction based on dual feature extraction and quadratic decomposition aggregation
Zhijian Qu, Xinxing Hou, Jian Li, et al.
Energy (2023) Vol. 290, pp. 130155-130155
Closed Access | Times Cited: 13
Non-crossing quantile probabilistic forecasting of cluster wind power considering spatio-temporal correlation
Yuejiang Chen, Jiang‐Wen Xiao, Yan‐Wu Wang, et al.
Applied Energy (2024) Vol. 377, pp. 124356-124356
Closed Access | Times Cited: 5
Day-ahead wind farm cluster power prediction based on trend categorization and spatial information integration model
Mao Yang, Yang Jiang, Chuanyu Xu, et al.
Applied Energy (2025) Vol. 388, pp. 125580-125580
Closed Access
Bayesian averaging-enabled transfer learning method for probabilistic wind power forecasting of newly built wind farms
Jiaxiang Hu, Weihao Hu, Di Cao, et al.
Applied Energy (2023) Vol. 355, pp. 122185-122185
Closed Access | Times Cited: 11
Interpretable wind power forecasting combing seasonal-trend representations learning with temporal fusion transformers architecture
Zhewen Niu, Xiaoqing Han, Dongxia Zhang, et al.
Energy (2024) Vol. 306, pp. 132482-132482
Closed Access | Times Cited: 4
Deep Belief Network with Swarm Spider Optimization Method for Renewable Energy Power Forecasting
Yuan Wei, Huanchang Zhang, Jiahui Dai, et al.
Processes (2023) Vol. 11, Iss. 4, pp. 1001-1001
Open Access | Times Cited: 10
A long-term water quality prediction model for marine ranch based on time-graph convolutional neural network
Dashe Li, Weijie Zhao, Jingzhe Hu, et al.
Ecological Indicators (2023) Vol. 154, pp. 110782-110782
Open Access | Times Cited: 10
Wind Power Forecasting Based on a Spatial–Temporal Graph Convolution Network With Limited Engineering Knowledge
Luo Yang, Fugee Tsung, Kaibo Wang, et al.
IEEE Transactions on Instrumentation and Measurement (2024) Vol. 73, pp. 1-13
Closed Access | Times Cited: 3
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