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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 High Precision Artificial Neural Networks Model for Short-Term Energy Load Forecasting
Ping‐Huan Kuo, Chiou‐Jye Huang
Energies (2018) Vol. 11, Iss. 1, pp. 213-213
Open Access | Times Cited: 300

Showing 1-25 of 300 citing articles:

A Deep Neural Network Model for Short-Term Load Forecast Based on Long Short-Term Memory Network and Convolutional Neural Network
Chujie Tian, Jian Ma, Chunhong Zhang, et al.
Energies (2018) Vol. 11, Iss. 12, pp. 3493-3493
Open Access | Times Cited: 335
A Short-Term Load Forecasting Method Using Integrated CNN and LSTM Network
Shafiul Hasan Rafi, Nahid‐Al Masood, Shohana Rahman Deeba, et al.
IEEE Access (2021) Vol. 9, pp. 32436-32448
Open Access | Times Cited: 302
A Comprehensive Review of the Load Forecasting Techniques Using Single and Hybrid Predictive Models
Abdullah Al Mamun, Md. Sohel, Naeem Mohammad, et al.
IEEE Access (2020) Vol. 8, pp. 134911-134939
Open Access | Times Cited: 252
Machine Learning and Deep Learning in smart manufacturing: The Smart Grid paradigm
Thanasis Kotsiopoulos, Panagiotis Sarigiannidis, Dimosthenis Ioannidis, et al.
Computer Science Review (2021) Vol. 40, pp. 100341-100341
Closed Access | Times Cited: 230
Electricity Price Forecasting Using Recurrent Neural Networks
Umut Uğurlu, İlkay Öksüz, Oktay Taş
Energies (2018) Vol. 11, Iss. 5, pp. 1255-1255
Open Access | Times Cited: 196
On Short-Term Load Forecasting Using Machine Learning Techniques and a Novel Parallel Deep LSTM-CNN Approach
Behnam Farsi, Manar Amayri, Nizar Bouguila, et al.
IEEE Access (2021) Vol. 9, pp. 31191-31212
Open Access | Times Cited: 194
Temporal Convolutional Networks Applied to Energy-Related Time Series Forecasting
Pedro Lara-Benítez, Manuel Carranza-García, José María Luna-Romera, et al.
Applied Sciences (2020) Vol. 10, Iss. 7, pp. 2322-2322
Open Access | Times Cited: 185
Recurrent inception convolution neural network for multi short-term load forecasting
Junhong Kim, Jihoon Moon, Eenjun Hwang, et al.
Energy and Buildings (2019) Vol. 194, pp. 328-341
Closed Access | Times Cited: 182
Deep learning methods and applications for electrical power systems: A comprehensive review
Asiye Kaymaz Özcanlı, Fatma Yaprakdal, Mustafa Baysal
International Journal of Energy Research (2020) Vol. 44, Iss. 9, pp. 7136-7157
Open Access | Times Cited: 179
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
Deep learning for time series forecasting: The electric load case
Alberto Gasparin, Slobodan Luković, Cesare Alippi
CAAI Transactions on Intelligence Technology (2021) Vol. 7, Iss. 1, pp. 1-25
Open Access | Times Cited: 149
A Short-Term Load Forecasting Method Based on GRU-CNN Hybrid Neural Network Model
Lizhen Wu, Kong Chun, Xiaohong Hao, et al.
Mathematical Problems in Engineering (2020) Vol. 2020, pp. 1-10
Open Access | Times Cited: 147
Long Short-Term Memory Network-Based Metaheuristic for Effective Electric Energy Consumption Prediction
Simran Kaur Hora, P Rachana, Rocío Pérez de Prado, et al.
Applied Sciences (2021) Vol. 11, Iss. 23, pp. 11263-11263
Open Access | Times Cited: 136
Load Forecasting Techniques for Power System: Research Challenges and Survey
Naqash Ahmad, Yazeed Yasin Ghadi, Muhammad Adnan, et al.
IEEE Access (2022) Vol. 10, pp. 71054-71090
Open Access | Times Cited: 132
A comparative assessment of SARIMA, LSTM RNN and Fb Prophet models to forecast total and peak monthly energy demand for India
Shobhit Chaturvedi, E. Rajasekar, Sukumar Natarajan, et al.
Energy Policy (2022) Vol. 168, pp. 113097-113097
Closed Access | Times Cited: 100
A hybrid RF-LSTM based on CEEMDAN for improving the accuracy of building energy consumption prediction
Irene Karijadi, Shuo‐Yan Chou
Energy and Buildings (2022) Vol. 259, pp. 111908-111908
Closed Access | Times Cited: 94
Energy Management Model for a Standalone Hybrid Microgrid through a Particle Swarm Optimization and Artificial Neural Networks Approach
Jesús Águila-León, Carlos Vargas‐Salgado, Cristian Chiñas‐Palacios, et al.
Energy Conversion and Management (2022) Vol. 267, pp. 115920-115920
Open Access | Times Cited: 89
Deep learning for renewable energy forecasting: A taxonomy, and systematic literature review
Changtian Ying, Weiqing Wang, Jiong Yu, et al.
Journal of Cleaner Production (2022) Vol. 384, pp. 135414-135414
Closed Access | Times Cited: 72
A Comprehensive Review of Artificial Intelligence (AI) Companies in the Power Sector
Vladimir Franki, Darin Majnarić, Alfredo Višković
Energies (2023) Vol. 16, Iss. 3, pp. 1077-1077
Open Access | Times Cited: 48
Energy Forecasting: A Comprehensive Review of Techniques and Technologies
Aristeidis Mystakidis, Paraskevas Koukaras, Nikolaos Tsalikidis, et al.
Energies (2024) Vol. 17, Iss. 7, pp. 1662-1662
Open Access | Times Cited: 27
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
Hybrid Short-Term Load Forecasting Scheme Using Random Forest and Multilayer Perceptron
Jihoon Moon, Yongsung Kim, Minjae Son, et al.
Energies (2018) Vol. 11, Iss. 12, pp. 3283-3283
Open Access | Times Cited: 130
An Optimized Heterogeneous Structure LSTM Network for Electricity Price Forecasting
Siyu Zhou, Lin Zhou, Mingxuan Mao, et al.
IEEE Access (2019) Vol. 7, pp. 108161-108173
Open Access | Times Cited: 116
Carbon futures price forecasting based with ARIMA-CNN-LSTM model
Lei Ji, Yingchao Zou, Kaijian He, et al.
Procedia Computer Science (2019) Vol. 162, pp. 33-38
Open Access | Times Cited: 106
An Experimental Review on Deep Learning Architectures for Time Series Forecasting
Pedro Lara-Benítez, Manuel Carranza-García, José C. Riquelme
International Journal of Neural Systems (2020) Vol. 31, Iss. 03, pp. 2130001-2130001
Open Access | Times Cited: 99
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