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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 seasonal decomposition based least squares support vector regression ensemble learning approach for hydropower consumption forecasting in China
Shuai Wang, Lean Yu, Ling Tang, et al.
Energy (2011) Vol. 36, Iss. 11, pp. 6542-6554
Closed Access | Times Cited: 131

Showing 1-25 of 131 citing articles:

Ensemble Classification and Regression-Recent Developments, Applications and Future Directions [Review Article]
Ye Ren, Le Zhang, Ponnuthurai Nagaratnam Suganthan
IEEE Computational Intelligence Magazine (2016) Vol. 11, Iss. 1, pp. 41-53
Closed Access | Times Cited: 593
Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines
Fazıl Kaytez, M. Cengiz Taplamacıoğlu, Ertuğrul Çam, et al.
International Journal of Electrical Power & Energy Systems (2014) Vol. 67, pp. 431-438
Closed Access | Times Cited: 456
Online model-based estimation of state-of-charge and open-circuit voltage of lithium-ion batteries in electric vehicles
Hongwen He, Xiaowei Zhang, Rui Xiong, et al.
Energy (2012) Vol. 39, Iss. 1, pp. 310-318
Closed Access | Times Cited: 451
Machine learning in energy economics and finance: A review
Hamed Ghoddusi, Germán G. Creamer, Nima Rafizadeh
Energy Economics (2019) Vol. 81, pp. 709-727
Closed Access | Times Cited: 340
Forecasting methods in energy planning models
Kumar Biswajit Debnath, Monjur Mourshed
Renewable and Sustainable Energy Reviews (2018) Vol. 88, pp. 297-325
Open Access | Times Cited: 293
A new generation of AI: A review and perspective on machine learning technologies applied to smart energy and electric power systems
Lefeng Cheng, Tao Yu
International Journal of Energy Research (2019) Vol. 43, Iss. 6, pp. 1928-1973
Open Access | Times Cited: 269
Online big data-driven oil consumption forecasting with Google trends
Lean Yu, Yaqing Zhao, Ling Tang, et al.
International Journal of Forecasting (2018) Vol. 35, Iss. 1, pp. 213-223
Closed Access | Times Cited: 210
A novel decomposition ensemble model with extended extreme learning machine for crude oil price forecasting
Lean Yu, Wei Dai, Ling Tang
Engineering Applications of Artificial Intelligence (2015) Vol. 47, pp. 110-121
Closed Access | Times Cited: 191
Intelligent techniques for forecasting electricity consumption of buildings
Khuram Pervez Amber, Rizwan Ahmad, Muhammad Waqar Aslam, et al.
Energy (2018) Vol. 157, pp. 886-893
Closed Access | Times Cited: 185
A decomposition–ensemble model with data-characteristic-driven reconstruction for crude oil price forecasting
Lean Yu, Zishu Wang, Ling Tang
Applied Energy (2015) Vol. 156, pp. 251-267
Closed Access | Times Cited: 182
Machine learning on sustainable energy: A review and outlook on renewable energy systems, catalysis, smart grid and energy storage
Daniel Rangel-Martinez, K.D.P. Nigam, Luis Ricardez‐Sandoval
Process Safety and Environmental Protection (2021) Vol. 174, pp. 414-441
Closed Access | Times Cited: 172
A review on the integrated optimization techniques and machine learning approaches for modeling, prediction, and decision making on integrated energy systems
Tobi Michael Alabi, Emmanuel Imuetinyan Aghimien, Favour David Agbajor, et al.
Renewable Energy (2022) Vol. 194, pp. 822-849
Closed Access | Times Cited: 126
Using multi-output feedforward neural network with empirical mode decomposition based signal filtering for electricity demand forecasting
Ning An, Weigang Zhao, Jianzhou Wang, et al.
Energy (2012) Vol. 49, pp. 279-288
Closed Access | Times Cited: 186
A novel least squares support vector machine ensemble model for NOx emission prediction of a coal-fired boiler
You Lv, Jizhen Liu, Tingting Yang, et al.
Energy (2013) Vol. 55, pp. 319-329
Closed Access | Times Cited: 180
A novel hybrid ensemble learning paradigm for nuclear energy consumption forecasting
Ling Tang, Lean Yu, Shuai Wang, et al.
Applied Energy (2012) Vol. 93, pp. 432-443
Closed Access | Times Cited: 168
Beyond one-step-ahead forecasting: Evaluation of alternative multi-step-ahead forecasting models for crude oil prices
Tao Xiong, Yukun Bao, Zhongyi Hu
Energy Economics (2013) Vol. 40, pp. 405-415
Open Access | Times Cited: 155
Forecasting the demand of the aviation industry using hybrid time series SARIMA-SVR approach
Shuojiang Xu, Hing Kai Chan, Tiantian Zhang
Transportation Research Part E Logistics and Transportation Review (2018) Vol. 122, pp. 169-180
Closed Access | Times Cited: 148
A decomposition-clustering-ensemble learning approach for solar radiation forecasting
Shaolong Sun, Shouyang Wang, Guowei Zhang, et al.
Solar Energy (2018) Vol. 163, pp. 189-199
Closed Access | Times Cited: 144
Empirical mode decomposition–based least squares support vector regression for foreign exchange rate forecasting
Chiun-Sin Lin, Sheng-Hsiung Chiu, Tzu-Yu Lin
Economic Modelling (2012) Vol. 29, Iss. 6, pp. 2583-2590
Closed Access | Times Cited: 141
Recursive wind speed forecasting based on Hammerstein Auto-Regressive model
Othman Ait Maatallah, Ajit Achuthan, Kerop D. Janoyan, et al.
Applied Energy (2015) Vol. 145, pp. 191-197
Closed Access | Times Cited: 137
A Novel CEEMD-Based EELM Ensemble Learning Paradigm for Crude Oil Price Forecasting
Ling Tang, Wei Dai, Lean Yu, et al.
International Journal of Information Technology & Decision Making (2014) Vol. 14, Iss. 01, pp. 141-169
Closed Access | Times Cited: 132
Hydroelectricity consumption forecast for Pakistan using ARIMA modeling and supply-demand analysis for the year 2030
Rehan Jamil
Renewable Energy (2020) Vol. 154, pp. 1-10
Closed Access | Times Cited: 132
A compressed sensing based AI learning paradigm for crude oil price forecasting
Lean Yu, Yang Zhao, Ling Tang
Energy Economics (2014) Vol. 46, pp. 236-245
Closed Access | Times Cited: 121
Complexity testing techniques for time series data: A comprehensive literature review
Ling Tang, Huiling Lv, Fengmei Yang, et al.
Chaos Solitons & Fractals (2015) Vol. 81, pp. 117-135
Closed Access | Times Cited: 118
Annual Electric Load Forecasting by a Least Squares Support Vector Machine with a Fruit Fly Optimization Algorithm
Hongze Li, Sen Guo, Huiru Zhao, et al.
Energies (2012) Vol. 5, Iss. 11, pp. 4430-4445
Open Access | Times Cited: 117
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