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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:

Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm
Eiman Tamah Al-Shammari, Afram Keivani, Shahaboddin Shamshirband, et al.
Energy (2015) Vol. 95, pp. 266-273
Closed Access | Times Cited: 117

Showing 1-25 of 117 citing articles:

A review of the-state-of-the-art in data-driven approaches for building energy prediction
Ying Sun, Fariborz Haghighat, Benjamin C. M. Fung
Energy and Buildings (2020) Vol. 221, pp. 110022-110022
Closed Access | Times Cited: 378
Integration of storage and renewable energy into district heating systems: A review of modelling and optimization
Dave Olsthoorn, Fariborz Haghighat, Parham A. Mirzaei
Solar Energy (2016) Vol. 136, pp. 49-64
Closed Access | Times Cited: 213
Machine learning-based thermal response time ahead energy demand prediction for building heating systems
Yabin Guo, Jiangyu Wang, Huanxin Chen, et al.
Applied Energy (2018) Vol. 221, pp. 16-27
Closed Access | Times Cited: 177
Multi-step ahead forecasting of heat load in district heating systems using machine learning algorithms
Puning Xue, Yi Jiang, Zhigang Zhou, et al.
Energy (2019) Vol. 188, pp. 116085-116085
Closed Access | Times Cited: 166
An artificial neural network (ANN) expert system enhanced with the electromagnetism-based firefly algorithm (EFA) for predicting the energy consumption in buildings
Dac-Khuong Bui, Tuan Ngoc Nguyen, Tuan Ngo, et al.
Energy (2019) Vol. 190, pp. 116370-116370
Closed Access | Times Cited: 165
Predicting hourly heating load in a district heating system based on a hybrid CNN-LSTM model
Jiancai Song, Liyi Zhang, Guixiang Xue, et al.
Energy and Buildings (2021) Vol. 243, pp. 110998-110998
Closed Access | Times Cited: 116
Vibration fault diagnosis of wind turbines based on variational mode decomposition and energy entropy
Xuejun Chen, Yongming Yang, Zhixin Cui, et al.
Energy (2019) Vol. 174, pp. 1100-1109
Closed Access | Times Cited: 130
Estimating the Heating Load of Buildings for Smart City Planning Using a Novel Artificial Intelligence Technique PSO-XGBoost
Lê Thị Lệ, Hoang Nguyen, Jian Zhou, et al.
Applied Sciences (2019) Vol. 9, Iss. 13, pp. 2714-2714
Open Access | Times Cited: 123
Fault detection and operation optimization in district heating substations based on data mining techniques
Puning Xue, Zhigang Zhou, Fang Xiu-mu, et al.
Applied Energy (2017) Vol. 205, pp. 926-940
Closed Access | Times Cited: 121
Using self-adaptive evolutionary algorithm to improve the performance of an extreme learning machine for estimating soil temperature
Behnaz Nahvi, Jafar Habibi, Kasra Mohammadi, et al.
Computers and Electronics in Agriculture (2016) Vol. 124, pp. 150-160
Closed Access | Times Cited: 118
Comparative study of machine learning-based multi-objective prediction framework for multiple building energy loads
Xiaojun Luo, Lukumon O. Oyedele, Anuoluwapo Ajayi, et al.
Sustainable Cities and Society (2020) Vol. 61, pp. 102283-102283
Closed Access | Times Cited: 98
Comparison of machine learning models for predicting fluoride contamination in groundwater
Rahim Barzegar, Asghar Asghari Moghaddam, Jan Adamowski, et al.
Stochastic Environmental Research and Risk Assessment (2016) Vol. 31, Iss. 10, pp. 2705-2718
Closed Access | Times Cited: 95
A comparison of models for forecasting the residential natural gas demand of an urban area
Rok Hribar, Primož Potočnik, Jurij Šilc, et al.
Energy (2018) Vol. 167, pp. 511-522
Open Access | Times Cited: 94
GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system
Yakai Lu, Zhe Tian, Peng Peng, et al.
Energy and Buildings (2019) Vol. 190, pp. 49-60
Closed Access | Times Cited: 94
Analysis of hydrogen production from wind energy in the southeast of Iran
Omid Alavi, Ali Mostafaeipour, Mojtaba Qolipour
International Journal of Hydrogen Energy (2016) Vol. 41, Iss. 34, pp. 15158-15171
Closed Access | Times Cited: 88
A firefly algorithm modified support vector machine for the credit risk assessment of supply chain finance
Hao Zhang, Yuxin Shi, Xueran Yang, et al.
Research in International Business and Finance (2021) Vol. 58, pp. 101482-101482
Closed Access | Times Cited: 88
Development of an IoT-based big data platform for day-ahead prediction of building heating and cooling demands
Xiaojun Luo, Lukumon O. Oyedele, Anuoluwapo Ajayi, et al.
Advanced Engineering Informatics (2019) Vol. 41, pp. 100926-100926
Open Access | Times Cited: 85
Gradient boosting machine for predicting return temperature of district heating system: A case study for residential buildings in Tianjin
Mingju Gong, Yin Bai, Juan Qin, et al.
Journal of Building Engineering (2019) Vol. 27, pp. 100950-100950
Closed Access | Times Cited: 79
Hourly Heat Load Prediction Model Based on Temporal Convolutional Neural Network
Jiancai Song, Guixiang Xue, Xuhua Pan, et al.
IEEE Access (2020) Vol. 8, pp. 16726-16741
Open Access | Times Cited: 69
Integration of flexibility potentials of district heating systems into electricity markets: A review
Hessam Golmohamadi, Kim G. Larsen, Peter Gjøl Jensen, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 159, pp. 112200-112200
Closed Access | Times Cited: 41
Accuracy improvement of the load forecasting in the district heating system by the informer-based framework with the optimal step size selection
Ji Zhang, Yuxin Hu, Yonggong Yuan, et al.
Energy (2024) Vol. 291, pp. 130347-130347
Closed Access | Times Cited: 11
Predicting hourly heating load in residential buildings using a hybrid SSA–CNN–SVM approach
Wenhan An, Bo Gao, Jianhua Liu, et al.
Case Studies in Thermal Engineering (2024) Vol. 59, pp. 104516-104516
Open Access | Times Cited: 10
Real operation data analysis on district heating load patterns
Michel Noussan, Matteo Jarre, Alberto Poggio
Energy (2017) Vol. 129, pp. 70-78
Open Access | Times Cited: 80
Medium-term heat load prediction for an existing residential building based on a wireless on-off control system
Jihao Gu, Jin Wang, Chengying Qi, et al.
Energy (2018) Vol. 152, pp. 709-718
Closed Access | Times Cited: 60
A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data
Rochus Niemierko, Jannick Töppel, Timm Tränkler
Applied Energy (2018) Vol. 233-234, pp. 691-708
Closed Access | Times Cited: 60
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