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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 Hybrid Seasonal Mechanism with a Chaotic Cuckoo Search Algorithm with a Support Vector Regression Model for Electric Load Forecasting
Yongquan Dong, Zichen Zhang, Wei‐Chiang Hong
Energies (2018) Vol. 11, Iss. 4, pp. 1009-1009
Open Access | Times Cited: 128

Showing 1-25 of 128 citing articles:

Application of variational mode decomposition and chaotic grey wolf optimizer with support vector regression for forecasting electric loads
Zichen Zhang, Wei‐Chiang Hong
Knowledge-Based Systems (2021) Vol. 228, pp. 107297-107297
Closed Access | Times Cited: 183
Electricity Price and Load Forecasting using Enhanced Convolutional Neural Network and Enhanced Support Vector Regression in Smart Grids
Maheen Zahid, Fahad Ahmed, Nadeem Javaid, et al.
Electronics (2019) Vol. 8, Iss. 2, pp. 122-122
Open Access | Times Cited: 152
Smart home energy management systems: Research challenges and survey
Ali Raza, LI Jing-zhao, Yazeed Yasin Ghadi, et al.
Alexandria Engineering Journal (2024) Vol. 92, pp. 117-170
Open Access | Times Cited: 27
Application of the Weighted K-Nearest Neighbor Algorithm for Short-Term Load Forecasting
Guo‐Feng Fan, Yanhui Guo, Jia‐Mei Zheng, et al.
Energies (2019) Vol. 12, Iss. 5, pp. 916-916
Open Access | Times Cited: 133
Electric Load Forecasting by Hybrid Self-Recurrent Support Vector Regression Model With Variational Mode Decomposition and Improved Cuckoo Search Algorithm
Zichen Zhang, Wei‐Chiang Hong, Junchi Li
IEEE Access (2020) Vol. 8, pp. 14642-14658
Open Access | Times Cited: 122
Economic-environmental dispatch of microgrid based on improved quantum particle swarm optimization
Zhao Xin-gang, Zhang Ze-qi, Xie Yi-min, et al.
Energy (2020) Vol. 195, pp. 117014-117014
Closed Access | Times Cited: 107
A support vector regression model hybridized with chaotic krill herd algorithm and empirical mode decomposition for regression task
Zichen Zhang, Shifei Ding, Yuting Sun
Neurocomputing (2020) Vol. 410, pp. 185-201
Closed Access | Times Cited: 106
Using a self-adaptive grey fractional weighted model to forecast Jiangsu’s electricity consumption in China
Xiaoyue Zhu, Yaoguo Dang, Song Ding
Energy (2019) Vol. 190, pp. 116417-116417
Closed Access | Times Cited: 90
Impact of FDI and R&D on China's industrial CO2 emissions reduction and trend prediction
Yang Yu, Wei Xu
Atmospheric Pollution Research (2019) Vol. 10, Iss. 5, pp. 1627-1635
Closed Access | Times Cited: 88
Association mining based deep learning approach for financial time-series forecasting
Tanya Srivastava, Ishita Mullick, Jatin Bedi
Applied Soft Computing (2024) Vol. 155, pp. 111469-111469
Closed Access | Times Cited: 9
A hybrid method based on neural network and improved environmental adaptation method using Controlled Gaussian Mutation with real parameter for short-term load forecasting
Priyanka Singh, Pragya Dwivedi, Vibhor Kant
Energy (2019) Vol. 174, pp. 460-477
Closed Access | Times Cited: 73
Hybrid Empirical Mode Decomposition with Support Vector Regression Model for Short Term Load Forecasting
Wei‐Chiang Hong, Guo‐Feng Fan
Energies (2019) Vol. 12, Iss. 6, pp. 1093-1093
Open Access | Times Cited: 59
A novel triple power cycle featuring a gas turbine cycle with supercritical carbon dioxide and organic Rankine cycles: Thermoeconomic analysis and optimization
Kasra Mohammadi, Kevin Ellingwood, Kody M. Powell
Energy Conversion and Management (2020) Vol. 220, pp. 113123-113123
Open Access | Times Cited: 58
Image surface texture analysis and classification using deep learning
Akarsh Aggarwal, Manoj Kumar
Multimedia Tools and Applications (2020) Vol. 80, Iss. 1, pp. 1289-1309
Closed Access | Times Cited: 57
Deep Learning-Based Short-Term Load Forecasting for Supporting Demand Response Program in Hybrid Energy System
Sholeh Hadi Pramono, Mahdin Rohmatillah, Eka Maulana, et al.
Energies (2019) Vol. 12, Iss. 17, pp. 3359-3359
Open Access | Times Cited: 55
Self adaptive cuckoo search: Analysis and experimentation
Rohit Salgotra, Urvinder Singh, Sriparna Saha, et al.
Swarm and Evolutionary Computation (2020) Vol. 60, pp. 100751-100751
Closed Access | Times Cited: 55
Insights into TripAdvisor's online reviews: The case of Tehran's hotels
Ramina Khorsand, Majid Rafiee, Vahid Kayvanfar
Tourism Management Perspectives (2020) Vol. 34, pp. 100673-100673
Closed Access | Times Cited: 51
An effective dimensionality reduction approach for short-term load forecasting
Yang Yang, Zijin Wang, Yuchao Gao, et al.
Electric Power Systems Research (2022) Vol. 210, pp. 108150-108150
Closed Access | Times Cited: 36
Forecasting of Electrical Energy Consumption in Slovakia
Michal Pavličko, Mária Vojteková, Oľga Blažeková
Mathematics (2022) Vol. 10, Iss. 4, pp. 577-577
Open Access | Times Cited: 29
A new intelligent hybrid forecasting method for power load considering uncertainty
Guo‐Feng Fan, Ying-Ying Han, Jingjing Wang, et al.
Knowledge-Based Systems (2023) Vol. 280, pp. 111034-111034
Closed Access | Times Cited: 20
Long-term solar radiation forecasting in India using EMD, EEMD, and advanced machine learning algorithms
T. Rajasundrapandiyanleebanon, N. S. Sakthivel Murugan, K. Kumaresan, et al.
Environmental Monitoring and Assessment (2025) Vol. 197, Iss. 3
Open Access
A comparative study of statistical and soft computing techniques for reliability prediction of automotive manufacturing
Hamzeh Soltanali, Abbas Rohani, Mohammad Hossein Abbaspour‐Fard, et al.
Applied Soft Computing (2020) Vol. 98, pp. 106738-106738
Closed Access | Times Cited: 44
PLS-CNN-BiLSTM: An End-to-End Algorithm-Based Savitzky–Golay Smoothing and Evolution Strategy for Load Forecasting
Mohamed Massaoudi, Shady S. Refaat, Haitham Abu‐Rub, et al.
Energies (2020) Vol. 13, Iss. 20, pp. 5464-5464
Open Access | Times Cited: 44
A generalized regression model based on hybrid empirical mode decomposition and support vector regression with back‐propagation neural network for mid‐short‐term load forecasting
Guo‐Feng Fan, Yanhui Guo, Jia‐Mei Zheng, et al.
Journal of Forecasting (2020) Vol. 39, Iss. 5, pp. 737-756
Closed Access | Times Cited: 43
Towards Electric Price and Load Forecasting Using CNN-Based Ensembler in Smart Grid
Shahzad Aslam, Nasir Ayub, Umer Farooq, et al.
Sustainability (2021) Vol. 13, Iss. 22, pp. 12653-12653
Open Access | Times Cited: 36
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