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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:

Interpretable deep learning model for building energy consumption prediction based on attention mechanism
Yuan Gao, Yingjun Ruan
Energy and Buildings (2021) Vol. 252, pp. 111379-111379
Closed Access | Times Cited: 81

Showing 1-25 of 81 citing articles:

Explainable Artificial Intelligence (XAI) techniques for energy and power systems: Review, challenges and opportunities
Ram Machlev, Leena Heistrene, M. Perl, et al.
Energy and AI (2022) Vol. 9, pp. 100169-100169
Open Access | Times Cited: 206
Interpretable machine learning for building energy management: A state-of-the-art review
Zhe Chen, Fu Xiao, Fangzhou Guo, et al.
Advances in Applied Energy (2023) Vol. 9, pp. 100123-100123
Open Access | Times Cited: 153
Improved multistep ahead photovoltaic power prediction model based on LSTM and self-attention with weather forecast data
Zehuan Hu, Yuan Gao, Siyu JI, et al.
Applied Energy (2024) Vol. 359, pp. 122709-122709
Closed Access | Times Cited: 61
Residential building energy consumption estimation: A novel ensemble and hybrid machine learning approach
Behnam Sadaghat, Sadegh Afzal, Ali Javadzade Khiavi
Expert Systems with Applications (2024) Vol. 251, pp. 123934-123934
Closed Access | Times Cited: 17
A spatial-temporal layer-wise relevance propagation method for improving interpretability and prediction accuracy of LSTM building energy prediction
Guannan Li, Fan Li, Chengliang Xu, et al.
Energy and Buildings (2022) Vol. 271, pp. 112317-112317
Closed Access | Times Cited: 43
Towards smart energy management for community microgrids: Leveraging deep learning in probabilistic forecasting of renewable energy sources
Jhon J. Quiñones, Luis R. Pineda, Jason K. Ostanek, et al.
Energy Conversion and Management (2023) Vol. 293, pp. 117440-117440
Open Access | Times Cited: 26
Short-term cooling and heating loads forecasting of building district energy system based on data-driven models
Hanfei Yu, Fan Zhong, Yuji Du, et al.
Energy and Buildings (2023) Vol. 298, pp. 113513-113513
Closed Access | Times Cited: 25
Combining physical approaches with deep learning techniques for urban building energy modeling: A comprehensive review and future research prospects
Zheng Li, Jun Ma, Yi Tan, et al.
Building and Environment (2023) Vol. 246, pp. 110960-110960
Closed Access | Times Cited: 25
A Review of Data-Driven Building Energy Prediction
Huiheng Liu, Jin Rui Liang, Yanchen Liu, et al.
Buildings (2023) Vol. 13, Iss. 2, pp. 532-532
Open Access | Times Cited: 24
Residential energy consumption forecasting using deep learning models
Paulo Vitor Barbosa Ramos, Saulo Moraes Villela, Walquiria N. Silva, et al.
Applied Energy (2023) Vol. 350, pp. 121705-121705
Closed Access | Times Cited: 24
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
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: 11
AI-Driven Innovations in Building Energy Management Systems: A Review of Potential Applications and Energy Savings
Dalia Mohammed Talat Ebrahim Ali, Violeta Motuzienė, Rasa Džiugaitė-Tumėnienė
Energies (2024) Vol. 17, Iss. 17, pp. 4277-4277
Open Access | Times Cited: 9
Recent advances in data mining and machine learning for enhanced building energy management
Xinlei Zhou, Han Du, Shan Xue, et al.
Energy (2024) Vol. 307, pp. 132636-132636
Open Access | Times Cited: 8
Data-Driven Energy Consumption Prediction of a University Office Building using Machine Learning Algorithms
Hasan Yeşilyurt, Yeşim DOKUZ, Ahmet Şakir Dokuz
Energy (2024), pp. 133242-133242
Closed Access | Times Cited: 8
Multi-step solar irradiation prediction based on weather forecast and generative deep learning model
Yuan Gao, Shohei Miyata, Yasunori Akashi
Renewable Energy (2022) Vol. 188, pp. 637-650
Open Access | Times Cited: 36
An adaptive federated learning system for community building energy load forecasting and anomaly prediction
Rui Wang, Hongguang Yun, Rakiba Rayhana, et al.
Energy and Buildings (2023) Vol. 295, pp. 113215-113215
Closed Access | Times Cited: 20
Exploring Local Explanation of Practical Industrial AI Applications: A Systematic Literature Review
Thi-Thu-Huong Le, Aji Teguh Prihatno, Yustus Eko Oktian, et al.
Applied Sciences (2023) Vol. 13, Iss. 9, pp. 5809-5809
Open Access | Times Cited: 20
Spatio-temporal interpretable neural network for solar irradiation prediction using transformer
Yuan Gao, Shohei Miyata, Yuki Matsunami, et al.
Energy and Buildings (2023) Vol. 297, pp. 113461-113461
Closed Access | Times Cited: 16
Improved convolutional neural network chiller early fault diagnosis by gradient-based feature-level model interpretation and feature learning
Guannan Li, Liang Chen, Cheng Fan, et al.
Applied Thermal Engineering (2023) Vol. 236, pp. 121549-121549
Closed Access | Times Cited: 16
A study of deep learning-based multi-horizon building energy forecasting
Zhongjun Ni, Chi Zhang, Magnus Karlsson, et al.
Energy and Buildings (2023) Vol. 303, pp. 113810-113810
Open Access | Times Cited: 16
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: 7
Interpretable Data-Driven Methods for Building Energy Modelling—A Review of Critical Connections and Gaps
Massimiliano Manfren, Karla M. Gonzalez-Carreon, P.A.B. James
Energies (2024) Vol. 17, Iss. 4, pp. 881-881
Open Access | Times Cited: 6
A Future Direction of Machine Learning for Building Energy Management: Interpretable Models
Luca Gugliermetti, Fabrizio Cumo, Sofia Agostinelli
Energies (2024) Vol. 17, Iss. 3, pp. 700-700
Open Access | Times Cited: 5
Leveraging explainable AI for informed building retrofit decisions: Insights from a survey
Daniel Leuthe, Jonas Mirlach, Simon Wenninger, et al.
Energy and Buildings (2024) Vol. 318, pp. 114426-114426
Open Access | Times Cited: 5
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