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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:

The role of data sample size and dimensionality in neural network based forecasting of building heating related variables
Martin Macaš, Fabio Moretti, Alessandro Fonti, et al.
Energy and Buildings (2015) Vol. 111, pp. 299-310
Closed Access | Times Cited: 64

Showing 1-25 of 64 citing articles:

An Application of Non-Linear Autoregressive Neural Networks to Predict Energy Consumption in Public Buildings
L. G. B. Ruiz, Manuel Pegalájar Cuéllar, Miguel Delgado Calvo-Flores, et al.
Energies (2016) Vol. 9, Iss. 9, pp. 684-684
Open Access | Times Cited: 218
Forecasting Energy Use in Buildings Using Artificial Neural Networks: A Review
Jason Runge, Radu Zmeureanu
Energies (2019) Vol. 12, Iss. 17, pp. 3254-3254
Open Access | Times Cited: 209
A comprehensive review on the application of artificial neural networks in building energy analysis
Saeed Reza Mohandes, Xueqing Zhang, Amir Mahdiyar
Neurocomputing (2019) Vol. 340, pp. 55-75
Closed Access | Times Cited: 199
Comparison of linear regression and artificial neural networks models to predict heating and cooling energy demand, energy consumption and CO 2 emissions
Rafael Pino‐Mejías, Alexis Pérez-Fargallo, Carlos Rubio-Bellido, et al.
Energy (2016) Vol. 118, pp. 24-36
Closed Access | Times Cited: 148
A hybrid method of dynamic cooling and heating load forecasting for office buildings based on artificial intelligence and regression analysis
Jing Zhao, Xiaojuan Liu
Energy and Buildings (2018) Vol. 174, pp. 293-308
Closed Access | Times Cited: 128
Performance Evaluation of Two Machine Learning Techniques in Heating and Cooling Loads Forecasting of Residential Buildings
Arash Moradzadeh, Amin Mansour‐Saatloo, Behnam Mohammadi‐Ivatloo, et al.
Applied Sciences (2020) Vol. 10, Iss. 11, pp. 3829-3829
Open Access | Times Cited: 112
Real-time prediction model for indoor temperature in a commercial building
Zakia Afroz, Tania Urmee, GM Shafiullah, et al.
Applied Energy (2018) Vol. 231, pp. 29-53
Closed Access | Times Cited: 95
Energy Consumption Prediction Using Machine Learning; A Review
Amir Mosavi, Abdullah Bahmani
(2019)
Open Access | Times Cited: 79
Modeling for indoor temperature prediction based on time-delay and Elman neural network in air conditioning system
Xiuming Li, Zongwei Han, Tianyi Zhao, et al.
Journal of Building Engineering (2020) Vol. 33, pp. 101854-101854
Closed Access | Times Cited: 75
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
Zeyu Wang, Lisha Xia, Hongping Yuan, et al.
Journal of Building Engineering (2022) Vol. 58, pp. 105028-105028
Closed Access | Times Cited: 49
Intelligent Systems for Building Energy and Occupant Comfort Optimization: A State of the Art Review and Recommendations
Abhinandana Boodi, Karim Beddiar, Malek Benamour, et al.
Energies (2018) Vol. 11, Iss. 10, pp. 2604-2604
Open Access | Times Cited: 63
Heating load prediction based on attention long short term memory: A case study of Xingtai
Guixiang Xue, Chengying Qi, Han Li, et al.
Energy (2020) Vol. 203, pp. 117846-117846
Closed Access | Times Cited: 58
Wind speed forecasting using neural networks
Tyler Blanchard, Biswanath Samanta
Wind Engineering (2019) Vol. 44, Iss. 1, pp. 33-48
Open Access | Times Cited: 49
A CNN-LSTM Model for Tailings Dam Risk Prediction
Jun Yang, Jingbin Qu, Qiang Mi, et al.
IEEE Access (2020) Vol. 8, pp. 206491-206502
Open Access | Times Cited: 49
Estimate the daily consumption of natural gas in district heating system based on a hybrid seasonal decomposition and temporal convolutional network model
Jiancai Song, Liyi Zhang, Qingling Jiang, et al.
Applied Energy (2022) Vol. 309, pp. 118444-118444
Closed Access | Times Cited: 22
A GA-stacking ensemble approach for forecasting energy consumption in a smart household: A comparative study of ensemble methods
Mahziyar Dostmohammadi, Mona Zamani Pedram, Siamak Hoseinzadeh, et al.
Journal of Environmental Management (2024) Vol. 364, pp. 121264-121264
Open Access | Times Cited: 4
Modelling on gross domestic product annual growth rate data by using time series, machine learning, and probability models
Ibrahim Elbatal, Md. Taneem Sarwar, Farrukh Jamal, et al.
Journal of Radiation Research and Applied Sciences (2025) Vol. 18, Iss. 2, pp. 101481-101481
Closed Access
Sample data selection method for improving the prediction accuracy of the heating energy consumption
Tianhao Yuan, Neng Zhu, Yunfei Shi, et al.
Energy and Buildings (2017) Vol. 158, pp. 234-243
Closed Access | Times Cited: 44
Predicting non-uniform indoor air quality distribution by using pulsating air supply and SVM model
Xue Tian, Yuchun Zhang, Zhang Lin
Building and Environment (2022) Vol. 219, pp. 109171-109171
Closed Access | Times Cited: 20
Artificial neural networks and linear regression prediction models for social housing allocation: Fuel Poverty Potential Risk Index
Rafael Pino‐Mejías, Alexis Pérez-Fargallo, Carlos Rubio-Bellido, et al.
Energy (2018) Vol. 164, pp. 627-641
Closed Access | Times Cited: 35
Building simulation in adaptive training of machine learning models
Hamed Amini, Kari Alanne, Risto Kosonen
Automation in Construction (2024) Vol. 165, pp. 105564-105564
Open Access | Times Cited: 3
A Review of Research on Building Energy Consumption Prediction Models Based on Artificial Neural Networks
Qing Yin, Chunmiao Han, Ailin Li, et al.
Sustainability (2024) Vol. 16, Iss. 17, pp. 7805-7805
Open Access | Times Cited: 3
Using change-point and Gaussian process models to create baseline energy models in industrial facilities: A comparison
Joseph Carpenter, Keith A. Woodbury, Zheng O’Neill
Applied Energy (2018) Vol. 213, pp. 415-425
Closed Access | Times Cited: 28
Daily residential heat load prediction based on a hybrid model of signal processing, econometric model, and support vector regression
Guixiang Xue, Yahui Zhang, Shi-ang Yu, et al.
Thermal Science and Engineering Progress (2023) Vol. 43, pp. 102005-102005
Closed Access | Times Cited: 8
An intelligent algorithm for final product demand forecasting in pharmaceutical units
Mohsen Sadegh Amalnick, Naser Habibifar, Mahdi Hamid, et al.
International Journal of Systems Assurance Engineering and Management (2019) Vol. 11, Iss. 2, pp. 481-493
Closed Access | Times Cited: 25
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