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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:

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

Showing 1-25 of 126 citing articles:

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: 538
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: 202
Wind speed forecasting based on variational mode decomposition and improved echo state network
Huanling Hu, Lin Wang, Rui Tao
Renewable Energy (2020) Vol. 164, pp. 729-751
Closed Access | Times Cited: 162
Effective energy consumption forecasting using empirical wavelet transform and long short-term memory
Lu Peng, Lin Wang, De Xia, et al.
Energy (2021) Vol. 238, pp. 121756-121756
Closed Access | Times Cited: 161
A novel decomposition-ensemble learning framework for multi-step ahead wind energy forecasting
Ramon Gomes da Silva, Matheus Henrique Dal Molin Ribeiro, Sinvaldo Rodrigues Moreno, et al.
Energy (2020) Vol. 216, pp. 119174-119174
Closed Access | Times Cited: 154
Interpretable wind speed prediction with multivariate time series and temporal fusion transformers
Binrong Wu, Lin Wang, Yu‐Rong Zeng
Energy (2022) Vol. 252, pp. 123990-123990
Closed Access | Times Cited: 147
Building energy prediction using artificial neural networks: A literature survey
Chujie Lu, Sihui Li, Zhengjun Lu
Energy and Buildings (2021) Vol. 262, pp. 111718-111718
Closed Access | Times Cited: 146
Efficient bootstrap stacking ensemble learning model applied to wind power generation forecasting
Matheus Henrique Dal Molin Ribeiro, Ramon Gomes da Silva, Sinvaldo Rodrigues Moreno, et al.
International Journal of Electrical Power & Energy Systems (2021) Vol. 136, pp. 107712-107712
Closed Access | Times Cited: 119
New developments in wind energy forecasting with artificial intelligence and big data: a scientometric insight
Erlong Zhao, Shaolong Sun, Shouyang Wang
Data Science and Management (2022) Vol. 5, Iss. 2, pp. 84-95
Open Access | Times Cited: 118
Deep learned recurrent type-3 fuzzy system: Application for renewable energy modeling/prediction
Yan Cao, Amir Raise, Ardashir Mohammadzadeh, et al.
Energy Reports (2021) Vol. 7, pp. 8115-8127
Open Access | Times Cited: 106
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
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
Deep learning in electrical utility industry: A comprehensive review of a decade of research
Manohar Mishra, Janmenjoy Nayak, Bighnaraj Naik, et al.
Engineering Applications of Artificial Intelligence (2020) Vol. 96, pp. 104000-104000
Closed Access | Times Cited: 93
A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting
Tanveer Ahmad, Dongdong Zhang
Energy (2021) Vol. 239, pp. 122109-122109
Closed Access | Times Cited: 87
Novel hybrid model based on echo state neural network applied to the prediction of stock price return volatility
Gabriel Trierweiler Ribeiro, André Alves Portela Santos, Viviana Cocco Mariani, et al.
Expert Systems with Applications (2021) Vol. 184, pp. 115490-115490
Open Access | Times Cited: 84
Effective long short-term memory with fruit fly optimization algorithm for time series forecasting
Lu Peng, Qing Zhu, Sheng-Xiang Lv, et al.
Soft Computing (2020) Vol. 24, Iss. 19, pp. 15059-15079
Closed Access | Times Cited: 73
Effective crude oil price forecasting using new text-based and big-data-driven model
Binrong Wu, Lin Wang, Sheng-Xiang Lv, et al.
Measurement (2020) Vol. 168, pp. 108468-108468
Closed Access | Times Cited: 73
Hybrid model with secondary decomposition, randomforest algorithm, clustering analysis and long short memory network principal computing for short-term wind power forecasting on multiple scales
Zexian Sun, Mingyu Zhao, Dong Yan, et al.
Energy (2021) Vol. 221, pp. 119848-119848
Closed Access | Times Cited: 62
Short-term renewable energy consumption and generation forecasting: A case study of Western Australia
Bilal Abu-Salih, Pornpit Wongthongtham, Gregory M. Morrison, et al.
Heliyon (2022) Vol. 8, Iss. 3, pp. e09152-e09152
Open Access | Times Cited: 57
Dynamic ensemble deep echo state network for significant wave height forecasting
Ruobin Gao, Ruilin Li, Minghui Hu, et al.
Applied Energy (2022) Vol. 329, pp. 120261-120261
Closed Access | Times Cited: 56
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
Deep Learning for Time Series Forecasting: Advances and Open Problems
Angelo Casolaro, Vincenzo Capone, Gennaro Iannuzzo, et al.
Information (2023) Vol. 14, Iss. 11, pp. 598-598
Open Access | Times Cited: 38
Adaptive fuzzy echo state network optimal synchronization control of hybrid–order chaotic systems via reinforcement learning
Mei Zhong, Chengdai Huang, Jinde Cao, et al.
Chaos Solitons & Fractals (2024) Vol. 181, pp. 114665-114665
Closed Access | Times Cited: 9
Short-term wind power forecasting by stacked recurrent neural networks with parametric sine activation function
Xin Liu, Jun Zhou, Huimin Qian
Electric Power Systems Research (2020) Vol. 192, pp. 107011-107011
Closed Access | Times Cited: 67
Sequential Learning-Based Energy Consumption Prediction Model for Residential and Commercial Sectors
Ijaz Ul Haq, Amin Ullah, Samee U. Khan, et al.
Mathematics (2021) Vol. 9, Iss. 6, pp. 605-605
Open Access | Times Cited: 52
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