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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.

Requested Article:

A novel hybrid methodology for short-term wind power forecasting based on adaptive neuro-fuzzy inference system
Jinqiang Liu, Xiaoru Wang, Yun Lu
Renewable Energy (2016) Vol. 103, pp. 620-629
Open Access | Times Cited: 120

Showing 1-25 of 120 citing articles:

A survey of artificial neural network in wind energy systems
Alberto Pliego Marugán, Fausto Pedro Garcı́a Márquez, Jesús María Pinar-Pérez, et al.
Applied Energy (2018) Vol. 228, pp. 1822-1836
Open Access | Times Cited: 417
Renewable energy: Present research and future scope of Artificial Intelligence
Sunil Kumar Jha, Jasmin Bilalovic, Anju Jha, et al.
Renewable and Sustainable Energy Reviews (2017) Vol. 77, pp. 297-317
Closed Access | Times Cited: 289
A Critical Review of Wind Power Forecasting Methods—Past, Present and Future
Shahram Hanifi, Xiaolei Liu, Zi Lin, et al.
Energies (2020) Vol. 13, Iss. 15, pp. 3764-3764
Open Access | Times Cited: 289
Methods and applications for Artificial Intelligence, Big Data, Internet of Things, and Blockchain in smart energy management
Joey H. Li, Münür Sacit Herdem, Jatin Nathwani, et al.
Energy and AI (2022) Vol. 11, pp. 100208-100208
Open Access | Times Cited: 238
Effective tourist volume forecasting supported by PCA and improved BPNN using Baidu index
Shaowen Li, Tao Chen, Lin Wang, et al.
Tourism Management (2018) Vol. 68, pp. 116-126
Closed Access | Times Cited: 217
Wind power forecasting of an offshore wind turbine based on high-frequency SCADA data and deep learning neural network
Zi Lin, Xiaolei Liu
Energy (2020) Vol. 201, pp. 117693-117693
Open Access | Times Cited: 202
Optimal hybrid PV/wind energy system sizing: Application of cuckoo search algorithm for Algerian dairy farms
O. Nadjemi, T. Nacer, A. Hamidat, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 70, pp. 1352-1365
Closed Access | Times Cited: 197
Ensemble empirical mode decomposition based adaptive wavelet neural network method for wind speed prediction
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Energy Conversion and Management (2018) Vol. 168, pp. 482-493
Closed Access | Times Cited: 189
Prediction interval of wind power using parameter optimized Beta distribution based LSTM model
Xiaohui Yuan, Chen Chen, Min Jiang, et al.
Applied Soft Computing (2019) Vol. 82, pp. 105550-105550
Closed Access | Times Cited: 169
Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain
Tatiane Carolyne Carneiro, Paulo Alexandre Costa Rocha, Paulo César Marques de Carvalho, et al.
Applied Energy (2022) Vol. 314, pp. 118936-118936
Closed Access | Times Cited: 84
Ultra-short-term wind power forecasting based on deep Bayesian model with uncertainty
Lei Liu, Jicheng Liu, Yu Ye, et al.
Renewable Energy (2023) Vol. 205, pp. 598-607
Closed Access | Times Cited: 51
Research and application of a combined model based on variable weight for short term wind speed forecasting
Hongmin Li, Jianzhou Wang, Haiyan Lu, et al.
Renewable Energy (2017) Vol. 116, pp. 669-684
Open Access | Times Cited: 140
Multi‐step wind power forecast based on VMD‐LSTM
Li Han, Rongchang Zhang, Xuesong Wang, et al.
IET Renewable Power Generation (2019) Vol. 13, Iss. 10, pp. 1690-1700
Closed Access | Times Cited: 137
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: 133
Wind speed and wind direction forecasting using echo state network with nonlinear functions
Mohammad Amin Chitsazan, M. Sami Fadali, A.M. Trzynadlowski
Renewable Energy (2018) Vol. 131, pp. 879-889
Closed Access | Times Cited: 124
An overview of performance evaluation metrics for short-term statistical wind power forecasting
Juan Manuel González Sopeña, Vikram Pakrashi, Bidisha Ghosh
Renewable and Sustainable Energy Reviews (2020) Vol. 138, pp. 110515-110515
Open Access | Times Cited: 123
Current advances and approaches in wind speed and wind power forecasting for improved renewable energy integration: A review
Santhosh Madasthu, Chintham Venkaiah, D. M. Vinod Kumar
Engineering Reports (2020) Vol. 2, Iss. 6
Open Access | Times Cited: 120
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: 115
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
A hybrid wind power forecasting approach based on Bayesian model averaging and ensemble learning
Gang Wang, Ru Jia, Jinhai Liu, et al.
Renewable Energy (2019) Vol. 145, pp. 2426-2434
Closed Access | Times Cited: 100
Wind Speed Ensemble Forecasting Based on Deep Learning Using Adaptive Dynamic Optimization Algorithm
Abdelhameed Ibrahim‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬‬, Seyedali Mirjalili, M. El-Said, et al.
IEEE Access (2021) Vol. 9, pp. 125787-125804
Open Access | Times Cited: 92
An advanced approach for optimal wind power generation prediction intervals by using self-adaptive evolutionary extreme learning machine
Tawfek Mahmoud, Zhao Yang Dong, Jin Ma
Renewable Energy (2018) Vol. 126, pp. 254-269
Closed Access | Times Cited: 86
Efficient artificial intelligence forecasting models for COVID-19 outbreak in Russia and Brazil
Mohammed A. A. Al‐qaness, Amal I. Saba, Ammar H. Elsheikh, et al.
Process Safety and Environmental Protection (2020) Vol. 149, pp. 399-409
Open Access | Times Cited: 83
Neuro-fuzzy resource forecast in site suitability assessment for wind and solar energy: A mini review
Paul A. Adedeji, Stephen A. Akinlabi, Nkosinathi Madushele, et al.
Journal of Cleaner Production (2020) Vol. 269, pp. 122104-122104
Closed Access | Times Cited: 80
Transfer learning based multi-layer extreme learning machine for probabilistic wind power forecasting
Yanli Liu, Junyi Wang
Applied Energy (2022) Vol. 312, pp. 118729-118729
Closed Access | Times Cited: 63
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