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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:

Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach
Behnam Sadaghat, Sadegh Afzal, Ali Javadzade Khiavi
Expert Systems with Applications (2024) Vol. 251, pp. 123934-123934
Closed Access | Times Cited: 16

Showing 16 citing articles:

Predicting Daily Heating Energy Consumption in Residential Buildings through Integration of Random Forest Model and Meta-Heuristic Algorithms
Weiyan Xu, Jielei Tu, Ning Xu, et al.
Energy (2024) Vol. 301, pp. 131726-131726
Closed Access | Times Cited: 16
Enhancing Building Energy Efficiency: An Integrated Approach to Predicting Heating and Cooling Loads using Machine Learning and Optimization Algorithms
Tianfei Gao, Xu Han, J. Wang, et al.
Journal of Building Engineering (2024), pp. 110759-110759
Closed Access | Times Cited: 5
Developing boosting-based estimation models for the ultimate strength of shear connectors in bridge
Guihai Gao, Jianxu Chen, Xiong Mei, et al.
Mechanics Based Design of Structures and Machines (2025), pp. 1-26
Closed Access
Enhancing residential heating load prediction with advanced machine learning and optimization techniques
Milad Mohebbi, Sadegh Afzal
Journal of Building Engineering (2024) Vol. 95, pp. 110199-110199
Closed Access | Times Cited: 3
An advanced hybrid deep learning model for accurate energy load prediction in smart building
R. Sunder, R Sreeraj, Vince Paul, et al.
Energy Exploration & Exploitation (2024) Vol. 42, Iss. 6, pp. 2241-2269
Open Access | Times Cited: 3
Design of iron-based metal-organic framework (Fe-MOF) and molybdenum telluride (MoTe2) nanohybrids for enhanced energy storage and hydrogen evolution reactions
Hasan B. Albargi, Adnan Abbas, Muhammad Zeeshan, et al.
Inorganic Chemistry Communications (2024), pp. 113791-113791
Closed Access | Times Cited: 2
An Ensemble Model for the Energy Consumption Prediction of Residential Buildings
Ritwik Mohan, Nikhil Pachauri
Energy (2024), pp. 134255-134255
Closed Access | Times Cited: 1
Investigating Cooling Load Estimation via Hybrid Models Based on the Radial Basis Function
Sirui Zhang, Hao Zheng
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 4
Open Access
Applying a multi-layer perceptron model for predicting gasification process outcomes
Mingtong Wei
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 4, pp. 4633-4655
Closed Access
Energy efficiency in cooling systems: integrating machine learning and meta-heuristic algorithms for precise cooling load prediction
Kunming Xu
Chemical Product and Process Modeling (2024) Vol. 19, Iss. 4, pp. 573-603
Closed Access
Harnessing Machine Learning and Meta-Heuristic Algorithms for Accurate Cooling Load Prediction
Yanfang Zhang
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 6
Open Access
The Utilization of a Multi-Layer Perceptron Model for Estimation of the Heating Load
Ken Chen, Wenyao Zhu
International Journal of Advanced Computer Science and Applications (2024) Vol. 15, Iss. 6
Open Access
Accurate cooling load estimation using multi-layer perceptron machine learning models
Jianxin Yang
Signal Image and Video Processing (2024) Vol. 18, Iss. 11, pp. 7711-7727
Closed Access
АНАЛІЗ ЕНЕРГОСПОЖИВАННЯ КОМЕРЦІЙНИХ БУДІВЕЛЬ У ПОМІРНОМУ КЛІМАТІ
Дмитро ШЕПЕЛЄВ, Ольга БОЙКО
Information Technology Computer Science Software Engineering and Cyber Security (2024), Iss. 2, pp. 176-181
Open Access
Applicability of smell agent optimization and Tasmanian devil optimization hybridized with ANFIS and SVR as reliable solutions in estimation of cooling load in buildings
Shaoxu Li
Multiscale and Multidisciplinary Modeling Experiments and Design (2024) Vol. 7, Iss. 6, pp. 5789-5808
Closed Access
Applying machine learning for biomass gasification prediction: enhancing efficiency and sustainability
C. H. Tai, Shasha Xiong
Chemical Product and Process Modeling (2024)
Closed Access
Enhanced Ensemble Learning-Based Uncertainty and Sensitivity Analysis of Ventilation Rate in a Novel Radiative Cooling Building
Majid Mohsenpour, Mohsen Salimi, A. Kermani, et al.
Heliyon (2024) Vol. 11, Iss. 1, pp. e41572-e41572
Open Access
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