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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:

Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm
Milan Protić, Shahaboddin Shamshirband, Dalibor Petković, et al.
Energy (2015) Vol. 87, pp. 343-351
Closed Access | Times Cited: 101

Showing 1-25 of 101 citing articles:

Evaluation of a multiple linear regression model and SARIMA model in forecasting heat demand for district heating system
Tingting Fang, Risto Lahdelma
Applied Energy (2016) Vol. 179, pp. 544-552
Closed Access | Times Cited: 274
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
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
A comparison of prediction and forecasting artificial intelligence models to estimate the future energy demand in a district heating system
Jason Runge, Étienne Saloux
Energy (2023) Vol. 269, pp. 126661-126661
Closed Access | Times Cited: 47
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
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
Modeling of district load forecasting for distributed energy system
Weiwu Ma, Song Fang, Gang Liu, et al.
Applied Energy (2017) Vol. 204, pp. 181-205
Closed Access | Times Cited: 111
Model input selection for building heating load prediction: A case study for an office building in Tianjin
Yan Ding, Qiang Zhang, Tianhao Yuan, et al.
Energy and Buildings (2017) Vol. 159, pp. 254-270
Closed Access | Times Cited: 111
Effect of input variables on cooling load prediction accuracy of an office building
Yan Ding, Qiang Zhang, Tianhao Yuan, et al.
Applied Thermal Engineering (2017) Vol. 128, pp. 225-234
Closed Access | Times Cited: 105
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
Using an ensemble machine learning methodology-Bagging to predict occupants’ thermal comfort in buildings
Zhibin Wu, Nianping Li, Jinqing Peng, et al.
Energy and Buildings (2018) Vol. 173, pp. 117-127
Closed Access | Times Cited: 96
Multienergy Networks Analytics: Standardized Modeling, Optimization, and Low Carbon Analysis
Wujing Huang, Ning Zhang, Yaohua Cheng, et al.
Proceedings of the IEEE (2020) Vol. 108, Iss. 9, pp. 1411-1436
Closed Access | Times Cited: 96
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
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
Nonlinear evolutionary swarm intelligence of grasshopper optimization algorithm and gray wolf optimization for weight adjustment of neural network
Hossein Moayedi, Hoang Nguyen, Loke Kok Foong
Engineering With Computers (2019) Vol. 37, Iss. 2, pp. 1265-1275
Closed Access | Times Cited: 81
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
Sustainable energies and machine learning: An organized review of recent applications and challenges
Pouya Ifaei, Morteza Nazari‐Heris, Amir Saman Tayerani Charmchi, et al.
Energy (2022) Vol. 266, pp. 126432-126432
Closed Access | Times Cited: 46
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
Carbon dioxide emission prediction using support vector machine
Chairul Saleh, Nur Rachman Dzakiyullah, Jonathan Bayu Nugroho
IOP Conference Series Materials Science and Engineering (2016) Vol. 114, pp. 012148-012148
Open Access | Times Cited: 79
Estimating hourly cooling load in commercial buildings using a thermal network model and electricity submetering data
Ying Ji, Peng Xu, Pengfei Duan, et al.
Applied Energy (2016) Vol. 169, pp. 309-323
Closed Access | Times Cited: 77
A new hybrid method for time series forecasting: AR–ANFIS
Busenur Sarıca, Erol Eǧrioǧlu, Barış Asikgil
Neural Computing and Applications (2016) Vol. 29, Iss. 3, pp. 749-760
Closed Access | Times Cited: 74
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
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