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:

Data-Driven Tools for Building Energy Consumption Prediction: A Review
Razak Olu-Ajayi, Hafiz Alaka, Hakeem A. Owolabi, et al.
Energies (2023) Vol. 16, Iss. 6, pp. 2574-2574
Open Access | Times Cited: 30

Showing 1-25 of 30 citing articles:

Load Forecasting with Machine Learning and Deep Learning Methods
Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía, et al.
Applied Sciences (2023) Vol. 13, Iss. 13, pp. 7933-7933
Open Access | Times Cited: 41

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

Building energy performance prediction: A reliability analysis and evaluation of feature selection methods
Razak Olu-Ajayi, Hafiz Alaka, Ismail Sulaimon, et al.
Expert Systems with Applications (2023) Vol. 225, pp. 120109-120109
Open Access | Times Cited: 21

Hybrid feature-based neural network regression method for load profiles forecasting
Aidos Satan, Nurkhat Zhakiyev, Aliya Nugumanova, et al.
Energy Informatics (2025) Vol. 8, Iss. 1
Open Access

Optimizing the sustainable performance of public buildings: A hybrid machine learning algorithm
Wen Xu, Xianguo Wu, Shishu Xiong, et al.
Energy (2025), pp. 135283-135283
Closed Access

Machine learning application in building energy consumption prediction: A comprehensive review
Jingsong Ji, Hao Yu, Xudong Wang, et al.
Journal of Building Engineering (2025), pp. 112295-112295
Closed Access

Advanced computing to support urban climate neutrality
Gregor Papa, Rok Hribar, Gašper Petelin, et al.
Energy Sustainability and Society (2025) Vol. 15, Iss. 1
Open Access

Thermal energy simulation of the building with heating tube embedded in the wall in the presence of different PCM materials
Talal Obaid Alshammari, Sayed Fayaz Ahmad, Mohamad Abou Houran, et al.
Journal of Energy Storage (2023) Vol. 73, pp. 109134-109134
Closed Access | Times Cited: 12

Advanced Energy Performance Modelling: Case Study of an Engineering and Technology Precinct
Faham Tahmasebinia, Lin Lin, Shuo Wu, et al.
Buildings (2024) Vol. 14, Iss. 6, pp. 1774-1774
Open Access | Times Cited: 4

Energy Consumption Prediction Model for Smart Homes via Decentralized Federated Learning With LSTM
Dawid Połap, Gautam Srivastava, Antoni Jaszcz
IEEE Transactions on Consumer Electronics (2023) Vol. 70, Iss. 1, pp. 990-999
Closed Access | Times Cited: 11

Predicting the PCM-incorporated building's performance using optimized linear kernel and tree-based machine learning methods
Kashif Nazir, Shazim Ali Memon, Assemgul Saurbayeva
Journal of Energy Storage (2024) Vol. 94, pp. 112495-112495
Closed Access | Times Cited: 3

A high performance solar-assisted ejector expansion refrigeration cycle for residential air conditioning
Seyedeh Zeinab Sajjadi, Bijan Farhanieh, Hossein Afshin
Applied Thermal Engineering (2024) Vol. 254, pp. 123872-123872
Closed Access | Times Cited: 3

Audit-Based Energy Performance Analysis of Multifamily Buildings in South-East Poland
Piotr Michalak, Krzysztof Szczotka, Jakub Szymiczek
Energies (2023) Vol. 16, Iss. 12, pp. 4828-4828
Open Access | Times Cited: 5

A Novel Ensemble Deep Learning Model for Building Energy Consumption Forecast
Mohammad Khodadadi, ladan riazi, Shahram Yazdani
International journal of engineering. Transactions C: Aspects (2024) Vol. 37, Iss. 6, pp. 1067-1075
Open Access | Times Cited: 1

Statistical and Artificial Intelligence-based Tools for Building Energy Prediction: A Systematic Literature Review
Razak Olu-Ajayi, Hafiz Alaka, Funlade Sunmola, et al.
IEEE Transactions on Engineering Management (2024) Vol. 71, pp. 14733-14753
Closed Access | Times Cited: 1

A novel optimized hybrid machine learning model to enhance the prediction accuracy of hourly building energy consumption
Rajasekar Thota, Nidul Sinha
Energy Sources Part A Recovery Utilization and Environmental Effects (2024) Vol. 46, Iss. 1, pp. 9112-9135
Closed Access | Times Cited: 1

A novel framework for developing a machine learning-based forecasting model using multi-stage sensitivity analysis to predict the energy consumption of PCM-integrated building
Kashif Nazir, Shazim Ali Memon, Assemgul Saurbayeva
Applied Energy (2024) Vol. 376, pp. 124180-124180
Closed Access | Times Cited: 1

Comparing MLR and ANN models for school building electrical energy prediction in Osijek-Baranja County in Croatia
Hana Begić, Hrvoje Krstić
Energy Reports (2024) Vol. 12, pp. 3595-3606
Open Access | Times Cited: 1

Gradient Boosting Approach to Predict Energy-Saving Awareness of Households in Kitakyushu
Nitin Kumar Singh, Takuya Fukushima, Masaaki Nagahara
Energies (2023) Vol. 16, Iss. 16, pp. 5998-5998
Open Access | Times Cited: 4

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