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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:

Energy Load Forecasting Using Empirical Mode Decomposition and Support Vector Regression
Luca Ghelardoni, Alessandro Ghio, Davide Anguita
IEEE Transactions on Smart Grid (2013) Vol. 4, Iss. 1, pp. 549-556
Closed Access | Times Cited: 209

Showing 1-25 of 209 citing articles:

Building energy load forecasting using Deep Neural Networks
Daniel Marino, Kasun Amarasinghe, Milos Manic
(2016), pp. 7046-7051
Open Access | Times Cited: 541
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: 366
Deep neural networks for energy load forecasting
Kasun Amarasinghe, Daniel Marino, Milos Manic
(2017), pp. 1483-1488
Closed Access | Times Cited: 312
A Comparative Study of Empirical Mode Decomposition-Based Short-Term Wind Speed Forecasting Methods
Ye Ren, Ponnuthurai Nagaratnam Suganthan, Narasimalu Srikanth
IEEE Transactions on Sustainable Energy (2014) Vol. 6, Iss. 1, pp. 236-244
Closed Access | Times Cited: 303
A review of non-probabilistic machine learning-based state of health estimation techniques for Lithium-ion battery
Xin Sui, Shan He, Søren Byg Vilsen, et al.
Applied Energy (2021) Vol. 300, pp. 117346-117346
Open Access | Times Cited: 276
Random vector functional link network for short-term electricity load demand forecasting
Ye Ren, Ponnuthurai Nagaratnam Suganthan, Narasimalu Srikanth, et al.
Information Sciences (2016) Vol. 367-368, pp. 1078-1093
Closed Access | Times Cited: 270
Energy Big Data Analytics and Security: Challenges and Opportunities
Jiankun Hu, Athanasios V. Vasilakos
IEEE Transactions on Smart Grid (2016) Vol. 7, Iss. 5, pp. 2423-2436
Closed Access | Times Cited: 205
A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting
Ashraf Ul Haque, M.H. Nehrir, Paras Mandal
IEEE Transactions on Power Systems (2014) Vol. 29, Iss. 4, pp. 1663-1672
Closed Access | Times Cited: 198
A novel framework for wind speed prediction based on recurrent neural networks and support vector machine
Chuanjin Yu, Yongle Li, Yulong Bao, et al.
Energy Conversion and Management (2018) Vol. 178, pp. 137-145
Closed Access | Times Cited: 165
Random vector functional link neural network based ensemble deep learning for short-term load forecasting
Ruobin Gao, Liang Du, Ponnuthurai Nagaratnam Suganthan, et al.
Expert Systems with Applications (2022) Vol. 206, pp. 117784-117784
Open Access | Times Cited: 71
Load Forecasting Models in Smart Grid Using Smart Meter Information: A Review
Fanidhar Dewangan, Almoataz Y. Abdelaziz, Monalisa Biswal
Energies (2023) Vol. 16, Iss. 3, pp. 1404-1404
Open Access | Times Cited: 71
Privacy-Preserving and Communication-Efficient Energy Prediction Scheme Based on Federated Learning for Smart Grids
Mahmoud M. Badr, Mohamed Mahmoud, Yuguang Fang, et al.
IEEE Internet of Things Journal (2023) Vol. 10, Iss. 9, pp. 7719-7736
Closed Access | Times Cited: 54
Probabilistic-based electricity demand forecasting with hybrid convolutional neural network-extreme learning machine model
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, et al.
Engineering Applications of Artificial Intelligence (2024) Vol. 132, pp. 107918-107918
Closed Access | Times Cited: 14
Vessels fuel consumption forecast and trim optimisation: A data analytics perspective
Andrea Coraddu, Luca Oneto, Francesco Baldi, et al.
Ocean Engineering (2016) Vol. 130, pp. 351-370
Closed Access | Times Cited: 162
Probabilistic Load Forecasting Using an Improved Wavelet Neural Network Trained by Generalized Extreme Learning Machine
Mehdi Rafiei, Taher Niknam, Jamshid Aghaei, et al.
IEEE Transactions on Smart Grid (2018) Vol. 9, Iss. 6, pp. 6961-6971
Open Access | Times Cited: 159
Long-term electrical energy consumption formulating and forecasting via optimized gene expression programming
S. Hr. Aghay Kaboli, Alireza Fallahpour, Jeyraj Selvaraj, et al.
Energy (2017) Vol. 126, pp. 144-164
Closed Access | Times Cited: 151
An improved Wavelet Transform using Singular Spectrum Analysis for wind speed forecasting based on Elman Neural Network
Chuanjin Yu, Yongle Li, Mingjin Zhang
Energy Conversion and Management (2017) Vol. 148, pp. 895-904
Closed Access | Times Cited: 151
Long-term electric energy consumption forecasting via artificial cooperative search algorithm
S. Hr. Aghay Kaboli, Jeyraj Selvaraj, Nasrudin Abd Rahim
Energy (2016) Vol. 115, pp. 857-871
Closed Access | Times Cited: 144
A novel fuzzy-based ensemble model for load forecasting using hybrid deep neural networks
George Sideratos, A. Ikonomopoulos, Nikos Hatziargyriou
Electric Power Systems Research (2019) Vol. 178, pp. 106025-106025
Closed Access | Times Cited: 142
Empirical mode decomposition based denoising method with support vector regression for time series prediction: A case study for electricity load forecasting
Yusuf Yaslan, Bahadır Bican
Measurement (2017) Vol. 103, pp. 52-61
Closed Access | Times Cited: 140
Condition Based Maintenance in Railway Transportation Systems Based on Big Data Streaming Analysis
Emanuele Fumeo, Luca Oneto, Davide Anguita
Procedia Computer Science (2015) Vol. 53, pp. 437-446
Open Access | Times Cited: 131
Mid-term interval load forecasting using multi-output support vector regression with a memetic algorithm for feature selection
Zhongyi Hu, Yukun Bao, Raymond Chiong, et al.
Energy (2015) Vol. 84, pp. 419-431
Closed Access | Times Cited: 115
On practical challenges of decomposition-based hybrid forecasting algorithms for wind speed and solar irradiation
Yamin Wang, Lei Wu
Energy (2016) Vol. 112, pp. 208-220
Open Access | Times Cited: 109
Comparative study on three new hybrid models using Elman Neural Network and Empirical Mode Decomposition based technologies improved by Singular Spectrum Analysis for hour-ahead wind speed forecasting
Chuanjin Yu, Yongle Li, Mingjin Zhang
Energy Conversion and Management (2017) Vol. 147, pp. 75-85
Closed Access | Times Cited: 108
Evolutionary Deep Learning-Based Energy Consumption Prediction for Buildings
Abdulaziz Almalaq, Jun Jason Zhang
IEEE Access (2018) Vol. 7, pp. 1520-1531
Open Access | Times Cited: 104
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