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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:

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

Showing 1-25 of 124 citing articles:

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: 368
Data processing strategies in wind energy forecasting models and applications: A comprehensive review
Hui Liu, Chao Chen
Applied Energy (2019) Vol. 249, pp. 392-408
Closed Access | Times Cited: 296
A combined forecasting model for time series: Application to short-term wind speed forecasting
Zhenkun Liu, Ping Jiang, Lifang Zhang, et al.
Applied Energy (2019) Vol. 259, pp. 114137-114137
Closed Access | Times Cited: 259
Deterministic wind energy forecasting: A review of intelligent predictors and auxiliary methods
Hui Liu, Chao Chen, Xinwei Lv, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 328-345
Closed Access | Times Cited: 250
A novel hybrid model based on VMD-WT and PCA-BP-RBF neural network for short-term wind speed forecasting
Yagang Zhang, Bing Chen, Guifang Pan, et al.
Energy Conversion and Management (2019) Vol. 195, pp. 180-197
Closed Access | Times Cited: 222
Multi-step wind speed forecasting based on hybrid multi-stage decomposition model and long short-term memory neural network
Sinvaldo Rodrigues Moreno, Ramon Gomes da Silva, Viviana Cocco Mariani, et al.
Energy Conversion and Management (2020) Vol. 213, pp. 112869-112869
Closed Access | Times Cited: 185
Wind speed prediction using a hybrid model of the multi-layer perceptron and whale optimization algorithm
Saeed Samadianfard, Sajjad Hashemi, Katayoun Kargar, et al.
Energy Reports (2020) Vol. 6, pp. 1147-1159
Open Access | Times Cited: 179
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
A long-term prediction approach based on long short-term memory neural networks with automatic parameter optimization by Tree-structured Parzen Estimator and applied to time-series data of NPP steam generators
Hoang-Phuong Nguyen, Jie Liu, Enrico Zio
Applied Soft Computing (2020) Vol. 89, pp. 106116-106116
Open Access | Times Cited: 142
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: 131
Short-Term Nacelle Orientation Forecasting Using Bilinear Transformation and ICEEMDAN Framework
Huajin Li, Jiahao Deng, Peng Feng, et al.
Frontiers in Energy Research (2021) Vol. 9
Open Access | Times Cited: 131
Two-phase deep learning model for short-term wind direction forecasting
Zhenhao Tang, Gengnan Zhao, Tinghui Ouyang
Renewable Energy (2021) Vol. 173, pp. 1005-1016
Closed Access | Times Cited: 103
A comprehensive review on deep learning approaches in wind forecasting applications
Zhou Wu, Gan Luo, Zhile Yang, et al.
CAAI Transactions on Intelligence Technology (2022) Vol. 7, Iss. 2, pp. 129-143
Open Access | Times Cited: 93
Multi-step short-term wind speed forecasting based on multi-stage decomposition coupled with stacking-ensemble learning approach
Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, Matheus Henrique Dal Molin Ribeiro, et al.
International Journal of Electrical Power & Energy Systems (2022) Vol. 143, pp. 108504-108504
Closed Access | Times Cited: 71
A novel hybrid model based on Empirical Mode Decomposition and Echo State Network for wind power forecasting
Uğur Yüzgeç, Emrah Dokur, MEHMET EMİN BALCI
Energy (2024) Vol. 300, pp. 131546-131546
Closed Access | Times Cited: 17
Forecasting energy consumption and wind power generation using deep echo state network
Huanling Hu, Lin Wang, Sheng-Xiang Lv
Renewable Energy (2020) Vol. 154, pp. 598-613
Closed Access | Times Cited: 126
Smart wind speed forecasting approach using various boosting algorithms, big multi-step forecasting strategy
Yanfei Li, Huipeng Shi, Feng-ze Han, et al.
Renewable Energy (2018) Vol. 135, pp. 540-553
Closed Access | Times Cited: 106
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
Developing optimal energy management of energy hub in the presence of stochastic renewable energy resources
Elnaz Shahrabi, Seyed Mehdi Hakimi, Arezoo Hasankhani, et al.
Sustainable Energy Grids and Networks (2021) Vol. 26, pp. 100428-100428
Closed Access | Times Cited: 94
Effective passenger flow forecasting using STL and ESN based on two improvement strategies
Lan Qin, Weide Li, Shijia Li
Neurocomputing (2019) Vol. 356, pp. 244-256
Closed Access | Times Cited: 93
Modes decomposition forecasting approach for ultra-short-term wind speed
Zhongda Tian
Applied Soft Computing (2021) Vol. 105, pp. 107303-107303
Closed Access | Times Cited: 77
A combination forecasting model of wind speed based on decomposition
Zhongda Tian, Hao Li, Feihong Li
Energy Reports (2021) Vol. 7, pp. 1217-1233
Open Access | Times Cited: 77
Hybrid multi-stage decomposition with parametric model applied to wind speed forecasting in Brazilian Northeast
Sinvaldo Rodrigues Moreno, Viviana Cocco Mariani, Leandro dos Santos Coelho
Renewable Energy (2020) Vol. 164, pp. 1508-1526
Closed Access | Times Cited: 73
Randomization-based machine learning in renewable energy prediction problems: Critical literature review, new results and perspectives
Javier Del Ser, David Casillas-Pérez, L. Cornejo-Bueno, et al.
Applied Soft Computing (2022) Vol. 118, pp. 108526-108526
Open Access | Times Cited: 50
An overview of deterministic and probabilistic forecasting methods of wind energy
Yuying Xie, Chaoshun Li, Mengying Li, et al.
iScience (2022) Vol. 26, Iss. 1, pp. 105804-105804
Open Access | Times Cited: 42
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