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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 individual residential load forecasting using an enhanced machine learning-based approach based on a feature engineering framework: A comparative study with deep learning methods
Ali Forootani, Mohammad Rastegar, Ashkan Sami
Electric Power Systems Research (2022) Vol. 210, pp. 108119-108119
Closed Access | Times Cited: 21

Showing 21 citing articles:

Short-term load forecasting based on CEEMDAN and Transformer
Ran Peng, Kun Dong, Xu Liu, et al.
Electric Power Systems Research (2022) Vol. 214, pp. 108885-108885
Closed Access | Times Cited: 73
CO and NOx emissions prediction in gas turbine using a novel modeling pipeline based on the combination of deep forest regressor and feature engineering
Leandro dos Santos Coelho, Helon Vicente Hultmann Ayala, Viviana Cocco Mariani
Fuel (2023) Vol. 355, pp. 129366-129366
Closed Access | Times Cited: 33
Current status, challenges, and prospects of data-driven urban energy modeling: A review of machine learning methods
Prajowal Manandhar, Hasan Rafiq, Edwin Rodríguez-Ubiñas
Energy Reports (2023) Vol. 9, pp. 2757-2776
Open Access | Times Cited: 30
Residential energy consumption forecasting using deep learning models
Paulo Vitor Barbosa Ramos, Saulo Moraes Villela, Walquiria N. Silva, et al.
Applied Energy (2023) Vol. 350, pp. 121705-121705
Closed Access | Times Cited: 24
Boosting short term electric load forecasting of high & medium voltage substations with visibility graphs and graph neural networks
Νικόλαος Γιαμαρέλος, Elias N. Zois
Sustainable Energy Grids and Networks (2024) Vol. 38, pp. 101304-101304
Closed Access | Times Cited: 6
An Ensemble Approach to Predict a Sustainable Energy Plan for London Households
Niraj Buyo, Akbar Sheikh-Akbari, Farrukh Saleem
Sustainability (2025) Vol. 17, Iss. 2, pp. 500-500
Open Access
Power system flexibility analysis using net-load forecasting based on deep learning considering distributed energy sources and electric vehicles
Ehsan Toghiany Rizi, Mohammad Rastegar, Ali Forootani
Computers & Electrical Engineering (2024) Vol. 117, pp. 109305-109305
Closed Access | Times Cited: 4
A federated and transfer learning based approach for households load forecasting
Gurjot Singh, Jatin Bedi
Knowledge-Based Systems (2024) Vol. 299, pp. 111967-111967
Closed Access | Times Cited: 4
Influence of curing system on static and dynamic mechanical properties of fly ash concrete
Yi Xu, Yangjie Chen, Qian Chen, et al.
Construction and Building Materials (2023) Vol. 371, pp. 130714-130714
Closed Access | Times Cited: 12
Transfer Learning-Based Framework Enhanced by Deep Generative Model for Cold-Start Forecasting of Residential EV Charging Behavior
Ali Forootani, Mohammad Rastegar, Hamidreza Zareipour
IEEE Transactions on Intelligent Vehicles (2023) Vol. 9, Iss. 1, pp. 190-198
Closed Access | Times Cited: 10
Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review
Filipe Rodrigues, Carlos Cardeira, J. M. F. Calado, et al.
Energies (2023) Vol. 16, Iss. 10, pp. 4098-4098
Open Access | Times Cited: 8
Short-Term Load Forecasting of Electricity Demand for the Residential Sector Based on Modelling Techniques: A Systematic Review
F. Martins Rodrigues, Carlos Cardeira, J. M. F. Calado, et al.
(2023)
Open Access | Times Cited: 7
Adaptive Façades Strategy: An architect-friendly computational approach based on co-simulation and white-box models for the early design stage
Zichuan Nie, Sisi Chen, Shen Zhang, et al.
Energy and Buildings (2023) Vol. 296, pp. 113320-113320
Closed Access | Times Cited: 7
Mining latent patterns with multi-scale decomposition for electricity demand and price forecasting using modified deep graph convolutional neural networks
Keerti Rawal, Aijaz Ahmad
Sustainable Energy Grids and Networks (2024) Vol. 39, pp. 101436-101436
Closed Access | Times Cited: 2
Feature engineering and selection for prosumer electricity consumption and production forecasting: A comprehensive framework
Qianyun Wen, Yang Liu
Applied Energy (2024) Vol. 381, pp. 125176-125176
Open Access | Times Cited: 2
A residential load forecasting method for multi-attribute adversarial learning considering multi-source uncertainties
Yongxin Su, Qiyao He, Jie Chen, et al.
International Journal of Electrical Power & Energy Systems (2023) Vol. 154, pp. 109421-109421
Open Access | Times Cited: 5
Residential Electricity Consumption Prediction Method Based on Deep Learning and Federated Learning Under Cloud Edge Collaboration Architecture
Wei Wang, Xiaotian Wang, Xiaotian Ma, et al.
International Journal of Gaming and Computer-Mediated Simulations (2024) Vol. 16, Iss. 1, pp. 1-19
Open Access
Multi-energy load forecasting for IES considering meteorological causation and repetition cycles
Lijun Yang, Xiang Li, Y. F. Lv, et al.
Electric Power Systems Research (2024) Vol. 237, pp. 110977-110977
Closed Access
A deep learning deviation-based scheme to defend against false data injection attacks in power distribution systems
Mohammad Reza Dehbozorgi, Mohammad Rastegar, Mohammadreza Fakhari Moghaddam Arani
Electric Power Systems Research (2024) Vol. 238, pp. 111076-111076
Closed Access
Short-Term Load Forecasting Based on Ceemdan and Transformer
Ran Peng, Kun Dong, Xu Liu, et al.
SSRN Electronic Journal (2022)
Closed Access | Times Cited: 1
Effects of Predictors on Power Consumption Estimation for IT Rack in a Data Center: An Experimental Analysis
Mehmet Türker Takcı, Tuba Gözel
Sustainability (2022) Vol. 14, Iss. 21, pp. 14663-14663
Open Access | Times Cited: 1
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