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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:

Implementation of a Long Short-Term Memory Transfer Learning (LSTM-TL)-Based Data-Driven Model for Building Energy Demand Forecasting
Dongsu Kim, Yongjun Lee, Kyungil Chin, et al.
Sustainability (2023) Vol. 15, Iss. 3, pp. 2340-2340
Open Access | Times Cited: 18

Showing 18 citing articles:

Office building energy consumption forecast: Adaptive long short term memory networks driven by improved beluga whale optimization algorithm
Zengxi Feng, Jianhu An, Mingyue Han, et al.
Journal of Building Engineering (2024) Vol. 91, pp. 109612-109612
Closed Access | Times Cited: 15
Transfer learning integrating similarity analysis for short-term and long-term building energy consumption prediction
Zhuoqun Xing, Yiqun Pan, Yiting Yang, et al.
Applied Energy (2024) Vol. 365, pp. 123276-123276
Closed Access | Times Cited: 12
Digital twin modeling for predicting loading resistance of loaders driven by deep transfer learning
Binyun Wu, Liang Hou, Shaojie Wang, et al.
Advanced Engineering Informatics (2025) Vol. 65, pp. 103245-103245
Closed Access | Times Cited: 1
Forecasting building energy demand and on-site power generation for residential buildings using long and short-term memory method with transfer learning
Dongsu Kim, Gu Seomun, Yongjun Lee, et al.
Applied Energy (2024) Vol. 368, pp. 123500-123500
Closed Access | Times Cited: 6
Predicting the Energy Consumption of Commercial Buildings Based on Deep Forest Model and Its Interpretability
Guangfa Zheng, Zao Feng, Mingkai Jiang, et al.
Buildings (2023) Vol. 13, Iss. 9, pp. 2162-2162
Open Access | Times Cited: 12
Analysis and Prediction of Energy Consumption in Office Buildings with Variable Refrigerant Flow Systems: A Case Study
Xiaojie Zhou, Ning Wang, Jixin Zou, et al.
Journal of Building Engineering (2024) Vol. 97, pp. 110936-110936
Closed Access | Times Cited: 4
Enhancing Building Energy Consumption Prediction Using LSTM, Kalman Filter, and Continuous Wavelet Transform
Nasima El Assri, Mohammed Ali Jallal, Samira Chabaa, et al.
Scientific African (2025), pp. e02560-e02560
Open Access
Transfer Learning on Transformers for Building Energy Consumption Forecasting - A Comparative Study
Robert Spencer, Surangika Ranathunga, Mikael Boulic, et al.
Energy and Buildings (2025), pp. 115632-115632
Open Access
Sustainability, emission trading system and carbon leakage: An approach based on neural networks and multicriteria analysis
Idiano D’Adamo, Massimo Gastaldi, Caroline Hachem-Vermette, et al.
Sustainable Operations and Computers (2023) Vol. 4, pp. 147-157
Open Access | Times Cited: 9
Restrictions and alternatives for the development data-based energy prediction models in buildings located in tropical climate: Literature review
Jorge Cárdenas-Rangel, Julián Jaramillo-Ibarra, Germán Osma-Pinto
Building and Environment (2024) Vol. 262, pp. 111786-111786
Closed Access | Times Cited: 1
Exploring artificial intelligence methods for energy prediction in healthcare Facilities: An In-Depth extended systematic review
Marjan FatehiJananloo, Helen Stopps, J.J. McArthur
Energy and Buildings (2024) Vol. 320, pp. 114598-114598
Open Access | Times Cited: 1
CRISP-DM-Based Data-Driven Approach for Building Energy Prediction Utilizing Indoor and Environmental Factors
Moaaz Elkabalawy, Abobakr Al-Sakkaf, Eslam Mohammed Abdelkader, et al.
Sustainability (2024) Vol. 16, Iss. 17, pp. 7249-7249
Open Access | Times Cited: 1
Development and calibration of apartment building energy model based on architectural and energy consumption characteristics
Ruda Lee, Dongsu Kim, Jong-Ho Yoon, et al.
Renewable and Sustainable Energy Reviews (2024) Vol. 206, pp. 114874-114874
Closed Access | Times Cited: 1
A baseline model combining physics and data-driven approach for operation evaluation of district heating substation
Yakai Lu, Xingyu Peng, Conghui Li, et al.
Energy and Buildings (2024) Vol. 321, pp. 114582-114582
Closed Access
Improving LSTM forecasting through ensemble learning: a comparative analysis of various models
Zishan Ahmad, S. Vengadeswaran, Biju, et al.
International Journal of Information Technology (2024) Vol. 16, Iss. 8, pp. 5113-5131
Closed Access
Hourly Building Energy Consumption Prediction Using a Training Sample Selection Method Based on Key Feature Search
Haizhou Fang, Hongwei Tan, Ningfang Dai, et al.
Sustainability (2023) Vol. 15, Iss. 9, pp. 7458-7458
Open Access | Times Cited: 1
A Design of an Energy Regulation Network Framework of Central Air Conditioning System Based on LSTM Prediction Model
Jie Lu, Linze Song, Yuan Yuan, et al.
(2023), pp. 876-880
Closed Access | Times Cited: 1
Multivariate Time Series Forecasting based on Time-Frequency Decomposition
Kehan Li, Yingguang Hao, Hongyu Wang, et al.
(2023), pp. 748-755
Closed Access
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