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OpenAlex Citation Counts

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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.

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Showing 1-25 of 110 citing articles:

A review of deep learning for renewable energy forecasting
Huaizhi Wang, Zhenxing Lei, Xian Zhang, et al.
Energy Conversion and Management (2019) Vol. 198, pp. 111799-111799
Closed Access | Times Cited: 869
A review of wind speed and wind power forecasting with deep neural networks
Yun Wang, Runmin Zou, Fang Liu, et al.
Applied Energy (2021) Vol. 304, pp. 117766-117766
Closed Access | Times Cited: 553
A review on renewable energy and electricity requirement forecasting models for smart grid and buildings
Tanveer Ahmad, Hongcai Zhang, Biao Yan
Sustainable Cities and Society (2020) Vol. 55, pp. 102052-102052
Closed Access | Times Cited: 370
Photovoltaic power forecasting based LSTM-Convolutional Network
Kejun Wang, Xiaoxia Qi, Hongda Liu
Energy (2019) Vol. 189, pp. 116225-116225
Closed Access | Times Cited: 345
Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression
Mahdi Sharifzadeh, Alexandra Sikinioti-Lock, Nilay Shah
Renewable and Sustainable Energy Reviews (2019) Vol. 108, pp. 513-538
Closed Access | Times Cited: 294
Taxonomy research of artificial intelligence for deterministic solar power forecasting
Huaizhi Wang, Yangyang Liu, Bin Zhou, et al.
Energy Conversion and Management (2020) Vol. 214, pp. 112909-112909
Closed Access | Times Cited: 266
Wind power forecasting using attention-based gated recurrent unit network
Zhewen Niu, Zeyuan Yu, Wenhu Tang, et al.
Energy (2020) Vol. 196, pp. 117081-117081
Closed Access | Times Cited: 253
Combined electricity-heat-cooling-gas load forecasting model for integrated energy system based on multi-task learning and least square support vector machine
Zhongfu Tan, Gejirifu De, Menglu Li, et al.
Journal of Cleaner Production (2019) Vol. 248, pp. 119252-119252
Closed Access | Times Cited: 207
A review and taxonomy of wind and solar energy forecasting methods based on deep learning
Ghadah Alkhayat, Rashid Mehmood
Energy and AI (2021) Vol. 4, pp. 100060-100060
Open Access | Times Cited: 204
Short-term wind speed prediction based on LMD and improved FA optimized combined kernel function LSSVM
Zhongda Tian
Engineering Applications of Artificial Intelligence (2020) Vol. 91, pp. 103573-103573
Closed Access | Times Cited: 159
Coupling a hybrid CNN-LSTM deep learning model with a Boundary Corrected Maximal Overlap Discrete Wavelet Transform for multiscale Lake water level forecasting
Rahim Barzegar, Mohammad Taghi Aalami, Jan Adamowski
Journal of Hydrology (2021) Vol. 598, pp. 126196-126196
Closed Access | Times Cited: 155
Review of meta-heuristic algorithms for wind power prediction: Methodologies, applications and challenges
Peng Lu, Lin Ye, Yongning Zhao, et al.
Applied Energy (2021) Vol. 301, pp. 117446-117446
Closed Access | Times Cited: 133
A hybrid attention-based deep learning approach for wind power prediction
Zhengjing Ma, Gang Mei
Applied Energy (2022) Vol. 323, pp. 119608-119608
Closed Access | Times Cited: 98
Enhancing wind speed forecasting through synergy of machine learning, singular spectral analysis, and variational mode decomposition
Sinvaldo Rodrigues Moreno, Laio Oriel Seman, Stéfano Frizzo Stefenon, et al.
Energy (2024) Vol. 292, pp. 130493-130493
Closed Access | Times Cited: 46
Wind power forecast based on improved Long Short Term Memory network
Li Han, Huitian Jing, Rongchang Zhang, et al.
Energy (2019) Vol. 189, pp. 116300-116300
Closed Access | Times Cited: 134
Wind power forecasting based on singular spectrum analysis and a new hybrid Laguerre neural network
Cong Wang, Hongli Zhang, Ping Ma
Applied Energy (2019) Vol. 259, pp. 114139-114139
Closed Access | Times Cited: 133
Short-Term Wind Power Forecasting Based on VMD Decomposition, ConvLSTM Networks and Error Analysis
Zexian Sun, Mingyu Zhao
IEEE Access (2020) Vol. 8, pp. 134422-134434
Open Access | Times Cited: 116
Electrical load forecasting: A deep learning approach based on K-nearest neighbors
Yunxuan Dong, Xuejiao Ma, Tonglin Fu
Applied Soft Computing (2020) Vol. 99, pp. 106900-106900
Closed Access | Times Cited: 111
A review of applications of artificial intelligent algorithms in wind farms
Yirui Wang, Yang Yu, Shuyang Cao, et al.
Artificial Intelligence Review (2019) Vol. 53, Iss. 5, pp. 3447-3500
Closed Access | Times Cited: 106
Multi-agent microgrid energy management based on deep learning forecaster
Mousa Afrasiabi, Mohammad Mohammadi, Mohammad Rastegar, et al.
Energy (2019) Vol. 186, pp. 115873-115873
Closed Access | Times Cited: 104
Ultra-short term wind prediction with wavelet transform, deep belief network and ensemble learning
Jiajun He, Chuanjin Yu, Yongle Li, et al.
Energy Conversion and Management (2019) Vol. 205, pp. 112418-112418
Closed Access | Times Cited: 103
Hybrid forecasting method for wind power integrating spatial correlation and corrected numerical weather prediction
Shuai Hu, Yue Xiang, Hongcai Zhang, et al.
Applied Energy (2021) Vol. 293, pp. 116951-116951
Open Access | Times Cited: 102
Nature-inspired metaheuristic ensemble model for forecasting energy consumption in residential buildings
Duc-Hoc Tran, Duc-Long Luong, Jui‐Sheng Chou
Energy (2019) Vol. 191, pp. 116552-116552
Closed Access | Times Cited: 99
A hybrid approach for multi-step wind speed forecasting based on two-layer decomposition, improved hybrid DE-HHO optimization and KELM
Wenlong Fu, Kai Zhang, Kai Wang, et al.
Renewable Energy (2020) Vol. 164, pp. 211-229
Closed Access | Times Cited: 86
Wind speed prediction based on singular spectrum analysis and neural network structural learning
Xiwei Mi, Shuo Zhao
Energy Conversion and Management (2020) Vol. 216, pp. 112956-112956
Closed Access | Times Cited: 82
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