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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:

Stacking deep transfer learning for short-term cross building energy prediction with different seasonality and occupant schedule
Han-Saem Park, Dong Yoon Park, Byeongjoon Noh, et al.
Building and Environment (2022) Vol. 218, pp. 109060-109060
Closed Access | Times Cited: 21

Showing 21 citing articles:

Privacy-preserving knowledge sharing for few-shot building energy prediction: A federated learning approach
Lingfeng Tang, Haipeng Xie, Xiaoyang Wang, et al.
Applied Energy (2023) Vol. 337, pp. 120860-120860
Closed Access | Times Cited: 31
An improved attention-based deep learning approach for robust cooling load prediction: Public building cases under diverse occupancy schedules
Chujie Lu, Junhua Gu, Weizhuo Lu
Sustainable Cities and Society (2023) Vol. 96, pp. 104679-104679
Open Access | Times Cited: 22
Review of the building energy performance gap from simulation and building lifecycle perspectives: Magnitude, causes and solutions
Zhihang Zheng, Jin Zhou, Zhu Jiaqin, et al.
Developments in the Built Environment (2024) Vol. 17, pp. 100345-100345
Open Access | Times Cited: 9
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
Comprehensive transferability assessment of short-term cross-building-energy prediction using deep adversarial network transfer learning
Guannan Li, Yubei Wu, Sungmin Yoon, et al.
Energy (2024) Vol. 299, pp. 131395-131395
Closed Access | Times Cited: 6
A comprehensive review and future research directions of ensemble learning models for predicting building energy consumption
Zeyu Wang, Yuelan Hong, Luying Huang, et al.
Energy and Buildings (2025), pp. 115589-115589
Closed 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
A district-scale spatial distribution evaluation method of rooftop solar energy potential based on deep learning
Guannan Li, Zixi Wang, Chengliang Xu, et al.
Solar Energy (2023) Vol. 268, pp. 112282-112282
Closed Access | Times Cited: 14
An improved transfer learning strategy for short-term cross-building energy prediction using data incremental
Guannan Li, Yubei Wu, Chengchu Yan, et al.
Building Simulation (2023) Vol. 17, Iss. 1, pp. 165-183
Closed Access | Times Cited: 12
Editorial: AI and IoT applications of smart buildings and smart environment design, construction and maintenance
Ke Yan, Xiaokang Zhou, Bin Yang
Building and Environment (2022) Vol. 229, pp. 109968-109968
Closed Access | Times Cited: 19
Energy Schedule Setting Based on Clustering Algorithm and Pattern Recognition for Non-Residential Buildings Electricity Energy Consumption
Yu Cui, Zishang Zhu, Xudong Zhao, et al.
Sustainability (2023) Vol. 15, Iss. 11, pp. 8750-8750
Open Access | Times Cited: 8
Multi-source domain generalization deep neural network model for predicting energy consumption in multiple office buildings
Ben Jiang, Yu Li, Yacine Rezgui, et al.
Energy (2024) Vol. 299, pp. 131467-131467
Closed Access | Times Cited: 2
Cross-building prediction of natural ventilation rate with small datasets based on a hybrid ensembled transfer learning
Hansaem Park, Dong Yoon Park, Juntae Jake Son, et al.
Building and Environment (2023) Vol. 242, pp. 110589-110589
Closed Access | Times Cited: 6
Predicting the electric power consumption of office buildings based on dynamic and static hybrid data analysis
Rongwei Zou, Qiliang Yang, Jianchun Xing, et al.
Energy (2023) Vol. 290, pp. 130149-130149
Closed Access | Times Cited: 6
Deep learning based identification and interpretability research of traditional village heritage value elements: a case study in Hubei Province
Gangyi Tan, Jiangkun Zhu, Zhanxiang Chen
Heritage Science (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 1
Automated layout generation from sites to flats using GAN and transfer learning
Lufeng Wang, Xuhong Zhou, Jiepeng Liu, et al.
Automation in Construction (2024) Vol. 166, pp. 105668-105668
Closed Access | Times Cited: 1
A study on source domain selection for transfer learning-based cross-building cooling load prediction
Qiang Zhang, Jide Niu, Zhe Tian, et al.
Energy and Buildings (2024), pp. 114856-114856
Closed Access | Times Cited: 1
A review of physics-informed machine learning for building energy modeling
Zhihao Ma, Gang Yi Jiang, Yuqing Hu, et al.
Applied Energy (2024) Vol. 381, pp. 125169-125169
Closed Access | Times Cited: 1
Deep Learning Based Identification and Interpretability Research of Traditional Village Heritage Value Elements: A case study in Hubei Province
Gangyi Tan, Jiangkun ZHU, Z K. Chen
Research Square (Research Square) (2024)
Open Access
Unveiling the Impact of Base Model Selection in Heterogeneous Ensemble Learning for Building Energy Prediction
Hongping Yuan, Mengjie Zhang, Zeyu Wang
(2024)
Closed Access
Deep Learning Based Identification and Interpretability Research of Traditional Village Heritage Value Elements: A Case Study in Hubei Province
Gangyi Tan, Jiangkun Zhu, Zhanxiang Chen
(2023)
Open Access
Ensemble transfer learning strategy in forecasting power consumption for residential buildings
Bowen Yang, Yuxiang Chen, Mustafa Gül, et al.
Building Simulation Conference proceedings (2023) Vol. 18
Open Access
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