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:

Multi-step forecasting for wind speed using a modified EMD-based artificial neural network model
Zhenhai Guo, Weigang Zhao, Haiyan Lu, et al.
Renewable Energy (2011) Vol. 37, Iss. 1, pp. 241-249
Closed Access | Times Cited: 503

Showing 101-125 of 503 citing articles:

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
Artificial Intelligence in Wind Speed Forecasting: A Review
Sandra Minerva Valdivia-Bautista, José A. Domínguez‐Navarro, Marco Pérez‐Cisneros, et al.
Energies (2023) Vol. 16, Iss. 5, pp. 2457-2457
Open Access | Times Cited: 40
Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field
Zhen Shao, Jun Han, Wei Zhao, et al.
Energy Conversion and Management (2022) Vol. 269, pp. 116138-116138
Closed Access | Times Cited: 39
Forecasting energy prices using a novel hybrid model with variational mode decomposition
Yu Lin, Qin Lu, Bin Tan, et al.
Energy (2022) Vol. 246, pp. 123366-123366
Closed Access | Times Cited: 38
A compound framework incorporating improved outlier detection and correction, VMD, weight-based stacked generalization with enhanced DESMA for multi-step short-term wind speed forecasting
Wenlong Fu, Yuchen Fu, Bailing Li, et al.
Applied Energy (2023) Vol. 348, pp. 121587-121587
Closed Access | Times Cited: 32
Inception-embedded attention memory fully-connected network for short-term wind power prediction
Linfei Yin, Mingshan Zhao
Applied Soft Computing (2023) Vol. 141, pp. 110279-110279
Closed Access | Times Cited: 22
Highly fluctuating short-term load forecasting based on improved secondary decomposition and optimized VMD
Yan Wen, Su Pan, Xinxin Li, et al.
Sustainable Energy Grids and Networks (2024) Vol. 37, pp. 101270-101270
Closed Access | Times Cited: 12
WGformer: A Weibull-Gaussian Informer based model for wind speed prediction
Ziyi Shi, Li Jia, Zheyuan Jiang, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 131, pp. 107891-107891
Closed Access | Times Cited: 12
A multifactor hybrid model for carbon price interval prediction based on decomposition-integration framework
Guozhong Zheng, Kang Li, Xuhui Yue, et al.
Journal of Environmental Management (2024) Vol. 363, pp. 121273-121273
Closed Access | Times Cited: 9
A Novel Multiscale Ensemble Carbon Price Prediction Model Integrating Empirical Mode Decomposition, Genetic Algorithm and Artificial Neural Network
Bangzhu Zhu
Energies (2012) Vol. 5, Iss. 2, pp. 355-370
Open Access | Times Cited: 102
An experimental investigation of two Wavelet-MLP hybrid frameworks for wind speed prediction using GA and PSO optimization
Hui Liu, Hong-qi Tian, Chao Chen, et al.
International Journal of Electrical Power & Energy Systems (2013) Vol. 52, pp. 161-173
Closed Access | Times Cited: 93
Pattern-Based Wind Speed Prediction Based on Generalized Principal Component Analysis
Qinghua Hu, Pengyu Su, Daren Yu, et al.
IEEE Transactions on Sustainable Energy (2014) Vol. 5, Iss. 3, pp. 866-874
Closed Access | Times Cited: 86
Extreme learning machine for the displacement prediction of landslide under rainfall and reservoir level
Cheng Lian, Zhigang Zeng, Wei Yao, et al.
Stochastic Environmental Research and Risk Assessment (2014) Vol. 28, Iss. 8, pp. 1957-1972
Closed Access | Times Cited: 85
Combining forecasts of electricity consumption in China with time-varying weights updated by a high-order Markov chain model
Weigang Zhao, Jianzhou Wang, Haiyan Lu
Omega (2014) Vol. 45, pp. 80-91
Closed Access | Times Cited: 85
One day ahead wind speed forecasting: A resampling-based approach
Weigang Zhao, Yi‐Ming Wei, Zhongyue Su
Applied Energy (2016) Vol. 178, pp. 886-901
Closed Access | Times Cited: 85
Application of Hilbert-Huang Transform in Structural Health Monitoring: A State-of-the-Art Review
Bo Chen, Sheng-lin Zhao, Peng-yun Li
Mathematical Problems in Engineering (2014) Vol. 2014, pp. 1-22
Open Access | Times Cited: 81
Hourly Day-Ahead Wind Power Prediction Using the Hybrid Model of Variational Model Decomposition and Long Short-Term Memory
Xiaoyu Shi, Xuewen Lei, Qiang Huang, et al.
Energies (2018) Vol. 11, Iss. 11, pp. 3227-3227
Open Access | Times Cited: 81
A novel wind speed prediction method: Hybrid of correlation-aided DWT, LSSVM and GARCH
Yan Jiang, Guoqing Huang, Xinyan Peng, et al.
Journal of Wind Engineering and Industrial Aerodynamics (2017) Vol. 174, pp. 28-38
Closed Access | Times Cited: 78
A hybrid application algorithm based on the support vector machine and artificial intelligence: An example of electric load forecasting
Yanhua Chen, Yi Yang, Chaoqun Liu, et al.
Applied Mathematical Modelling (2014) Vol. 39, Iss. 9, pp. 2617-2632
Open Access | Times Cited: 77
Prediction of time series of NPP operating parameters using dynamic model based on BP neural network
Yong-kuo Liu, Fei Xie, Chunli Xie, et al.
Annals of Nuclear Energy (2015) Vol. 85, pp. 566-575
Closed Access | Times Cited: 73
Prediction of landslide displacement with controlling factors using extreme learning adaptive neuro-fuzzy inference system (ELANFIS)
K. V. Shihabudheen, G. N. Pillai, Bipin Peethambaran
Applied Soft Computing (2017) Vol. 61, pp. 892-904
Closed Access | Times Cited: 70
A review of the decomposition methodology for extracting and identifying the fluctuation characteristics in electricity demand forecasting
Zhen Shao, Chao Fu, Shanlin Yang, et al.
Renewable and Sustainable Energy Reviews (2016) Vol. 75, pp. 123-136
Closed Access | Times Cited: 67
A hybrid approach based on the Gaussian process with t-observation model for short-term wind speed forecasts
Jianming Hu, Jianzhou Wang, Liqun Xiao
Renewable Energy (2017) Vol. 114, pp. 670-685
Closed Access | Times Cited: 64
Day-ahead spatio-temporal forecasting of solar irradiation along a navigation route
Hai Lan, He Yin, Ying‐Yi Hong, et al.
Applied Energy (2017) Vol. 211, pp. 15-27
Closed Access | Times Cited: 62
Wind Speed Interval Prediction Based on Lorenz Disturbance Distribution
Yagang Zhang, Yuan Zhao, Guifang Pan, et al.
IEEE Transactions on Sustainable Energy (2019) Vol. 11, Iss. 2, pp. 807-816
Closed Access | Times Cited: 62

Scroll to top