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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:

Short-Term Power Load Forecasting Method Based on Improved Exponential Smoothing Grey Model
Jianwei Mi, Libin Fan, Xuechao Duan, et al.
Mathematical Problems in Engineering (2018) Vol. 2018, pp. 1-11
Open Access | Times Cited: 87

Showing 1-25 of 87 citing articles:

Methods and Models for Electric Load Forecasting: A Comprehensive Review
M. Hammad, Borut Jereb, Bojan Rosi, et al.
Logistics Supply Chain Sustainability and Global Challenges (2020) Vol. 11, Iss. 1, pp. 51-76
Open Access | Times Cited: 178
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: 72
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
Forecasting seasonal electricity generation in European countries under Covid-19-induced lockdown using fractional grey prediction models and machine learning methods
Utkucan Şahin, Serkan Ballı, Yan Chen
Applied Energy (2021) Vol. 302, pp. 117540-117540
Open Access | Times Cited: 65
Electrical demand aggregation effects on the performance of deep learning-based short-term load forecasting of a residential building
Ayas Shaqour, Tetsushi Ono, Aya Hagishima, et al.
Energy and AI (2022) Vol. 8, pp. 100141-100141
Open Access | Times Cited: 38
A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting
Shiyan Yi, Haichun Liu, Tao Chen, et al.
IET Generation Transmission & Distribution (2023) Vol. 17, Iss. 7, pp. 1538-1552
Open Access | Times Cited: 28
ISSA-LSTM: A new data-driven method of heat load forecasting for building air conditioning
Mengran Zhou, Ling Wang, Feng Hu, et al.
Energy and Buildings (2024) Vol. 321, pp. 114698-114698
Closed Access | Times Cited: 11
Data-driven short term load forecasting with deep neural networks: Unlocking insights for sustainable energy management
Waqar Waheed, Qingshan Xu
Electric Power Systems Research (2024) Vol. 232, pp. 110376-110376
Closed Access | Times Cited: 10
A hybrid approach for measuring the vibrational trend of hydroelectric unit with enhanced multi-scale chaotic series analysis and optimized least squares support vector machine
Wenlong Fu, Kai Wang, Chu Zhang, et al.
Transactions of the Institute of Measurement and Control (2019) Vol. 41, Iss. 15, pp. 4436-4449
Closed Access | Times Cited: 61
Deep Forest Regression for Short-Term Load Forecasting of Power Systems
Linfei Yin, Zhixiang Sun, Fang Gao, et al.
IEEE Access (2020) Vol. 8, pp. 49090-49099
Open Access | Times Cited: 60
Design and development of Residential Sector Load Prediction model during COVID-19 Pandemic using LSTM based RNN
A. Ajitha, Maitri Goel, Mohit Assudani, et al.
Electric Power Systems Research (2022) Vol. 212, pp. 108635-108635
Closed Access | Times Cited: 29
An Improved WOA (Whale Optimization Algorithm)-Based CNN-BIGRU-CBAM Model and Its Application to Short-Term Power Load Forecasting
Lei Dai, Haiying Wang
Energies (2024) Vol. 17, Iss. 11, pp. 2559-2559
Open Access | Times Cited: 7
Electric power load forecasting on a 33/11 kV substation using artificial neural networks
Venkataramana Veeramsetty, Ram Deshmukh
SN Applied Sciences (2020) Vol. 2, Iss. 5
Open Access | Times Cited: 47
A review on short‐term load forecasting models for micro‐grid application
V. Y. Kondaiah, B. Saravanan, Sanjeevikumar Padmanaban, et al.
The Journal of Engineering (2022) Vol. 2022, Iss. 7, pp. 665-689
Open Access | Times Cited: 27
Individualized Short-Term Electric Load Forecasting Using Data-Driven Meta-Heuristic Method Based on LSTM Network
Lichao Sun, Hang Qin, Кrzysztof Przystupa, et al.
Sensors (2022) Vol. 22, Iss. 20, pp. 7900-7900
Open Access | Times Cited: 27
Accurate ultra-short-term load forecasting based on load characteristic decomposition and convolutional neural network with bidirectional long short-term memory model
Mingyue Zhang, Yang Han, Amr S. Zalhaf, et al.
Sustainable Energy Grids and Networks (2023) Vol. 35, pp. 101129-101129
Closed Access | Times Cited: 15
The role of age of information in enhancing short-term energy forecasting
Jernej Hribar, Carolina Fortuna, Mihael Mohorčič
Energy (2025), pp. 134704-134704
Closed Access
PAKMamba: Enhancing electricity load forecasting with periodic aggregation and Koopman analysis
Tao Shen, Wenbin Shi, Jingsheng Lei, et al.
Computers & Electrical Engineering (2025) Vol. 123, pp. 110113-110113
Closed Access
Decision Support System Based on Grey Systems and Markov Chain MCGM (1,1) Applied to Improve Forecast for Demand Uncertainty
Francisco Trejo, Rafael Torres-Escobar, Alberto Ochoa
Lecture notes in computer science (2025), pp. 16-27
Closed Access
Research on Subsidence Prediction Method of Water-Conducting Fracture Zone of Overlying Strata in Coal Mine Based on Grey Theory Model
Jinjun Li, Zhihao He, Chunde Piao, et al.
Water (2023) Vol. 15, Iss. 23, pp. 4177-4177
Open Access | Times Cited: 11
Short Term Active Power Load Prediction on A 33/11 kV Substation Using Regression Models
Venkataramana Veeramsetty, Arjun Mohnot, Gaurav Singal, et al.
Energies (2021) Vol. 14, Iss. 11, pp. 2981-2981
Open Access | Times Cited: 26
Short-term power load forecasting using SSA-CNN-LSTM method
Yonggang Wang, Hao Yue, Biying Zhang, et al.
Systems Science & Control Engineering (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 3
Load forecasting method based on CNN and extended LSTM
Chong Wang, Xiang Li, Yan Shi, et al.
Energy Reports (2024) Vol. 12, pp. 2452-2461
Open Access | Times Cited: 3
A Hybrid Deep Learning Framework for Short-Term Load Forecasting with Improved Data Cleansing and Preprocessing Techniques
Muhammad Sajid Iqbal, Muhammad Adnan Khan, Salah Eldeen Gasim Mohamed, et al.
Results in Engineering (2024), pp. 103560-103560
Open Access | Times Cited: 3
Intelligent Machine Learning With Evolutionary Algorithm Based Short Term Load Forecasting in Power Systems
Ibrahim M. Mehedi, Hussain Bassi, Muhyaddin Rawa, et al.
IEEE Access (2021) Vol. 9, pp. 100113-100124
Open Access | Times Cited: 20
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