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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:

Deep learning and transfer learning techniques applied to short-term load forecasting of data-poor buildings in local energy communities
Miguel López Santos, Saúl Díaz García, Xela García‐Santiago, et al.
Energy and Buildings (2023) Vol. 292, pp. 113164-113164
Closed Access | Times Cited: 28

Showing 1-25 of 28 citing articles:

A comprehensive review of AI-enhanced smart grid integration for hydrogen energy: Advances, challenges, and future prospects
Morteza SaberiKamarposhti, Hesam Kamyab, Santhana Krishnan, et al.
International Journal of Hydrogen Energy (2024) Vol. 67, pp. 1009-1025
Closed Access | Times Cited: 51
Load Forecasting with Machine Learning and Deep Learning Methods
Moisés Cordeiro-Costas, Daniel Villanueva, Pablo Eguía, et al.
Applied Sciences (2023) Vol. 13, Iss. 13, pp. 7933-7933
Open Access | Times Cited: 41
The role of energy communities in electricity grid balancing: A flexible tool for smart grid power distribution optimization
Giovanni Barone, Annamaria Buonomano, Cesare Forzano, et al.
Renewable and Sustainable Energy Reviews (2023) Vol. 187, pp. 113742-113742
Open Access | Times Cited: 24
An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios
Borui Wei, Kangji Li, Shiyi Zhou, et al.
Journal of Building Engineering (2024) Vol. 85, pp. 108713-108713
Closed Access | Times Cited: 11
An advanced airport terminal cooling load forecasting model integrating SSA and CNN-Transformer
Bochao Chen, Wansheng Yang, Biao Yan, et al.
Energy and Buildings (2024) Vol. 309, pp. 114000-114000
Closed Access | Times Cited: 11
Enhancing source domain availability through data and feature transfer learning for building power load forecasting
Fanyue Qian, Yingjun Ruan, Huiming Lu, et al.
Building Simulation (2024) Vol. 17, Iss. 4, pp. 625-638
Closed Access | Times Cited: 9
Few-Sample Model Training Assistant: A Meta-Learning Technique For Building Heating Load Forecasting Based On Simulation Data
Yakai Lu, Xingyu Peng, Conghui Li, et al.
Energy (2025), pp. 134509-134509
Closed Access | Times Cited: 1
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
NSGA-II based short-term building energy management using optimal LSTM-MLP forecasts
Moisés Cordeiro-Costas, Hugo Labandeira-Pérez, Daniel Villanueva, et al.
International Journal of Electrical Power & Energy Systems (2024) Vol. 159, pp. 110070-110070
Open Access | Times Cited: 6
Edge computing and transfer learning-based short-term load forecasting for residential and commercial buildings
Muhammad Sajid Iqbal, Muhammad Adnan
Energy and Buildings (2025) Vol. 329, pp. 115273-115273
Closed Access
Seasonal forecasting of the hourly electricity demand applying machine and deep learning algorithms impact analysis of different factors
Heba-Allah Ibrahim El-Azab, R.A. Swief, Noha H. El-Amary, et al.
Scientific Reports (2025) Vol. 15, Iss. 1
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
A parameter-based multi-source transfer learning method for building load forecasting with sparse data scenarios
Zhi Gao, Yufei Xie
Energy Reports (2025) Vol. 13, pp. 4936-4947
Closed Access
Limited data-oriented building heating load prediction method: A novel meta learning-based framework
Yakai Lu, Xingyu Peng, Conghui Li, et al.
Energy and Buildings (2024) Vol. 308, pp. 114027-114027
Closed Access | Times Cited: 3
Temporal Fusion Transformer and transfer learning techniques applied to predict steam enthalpy with limited data in geothermal power plants
Hodaka Matsuzaki, Akira Yoshida, Yoshiharu AMANO
Mechanical Engineering Journal (2024) Vol. 11, Iss. 2, pp. 23-00465
Open Access | Times Cited: 2
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: 2
Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting
Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, et al.
(2024)
Open Access | Times Cited: 1
Enhanced Sequence-to-Sequence Deep Transfer Learning for Day-Ahead Electricity Load Forecasting
Vasileios Laitsos, Georgios Vontzos, Apostolos Tsiovoulos, et al.
Electronics (2024) Vol. 13, Iss. 10, pp. 1996-1996
Open Access | Times Cited: 1
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
Numerical Weather Prediction of Sea Surface Temperature in South China Sea Using Attention-Based Context Fusion Network
Hailun He, Benyun Shi, Yuting Zhu, et al.
Remote Sensing (2024) Vol. 16, Iss. 20, pp. 3793-3793
Open Access | Times Cited: 1
A recommendation model for optimizing transfer learning hyper-parameter settings in building heat load prediction with limited data samples
Di Bai, Shuo Ma, Xiaochen Yang, et al.
Energy and Buildings (2024) Vol. 325, pp. 115021-115021
Closed Access | Times Cited: 1
Seasonal Forecasting of the Hourly Electricity Demand: Impact Analysis of Different Factors Applying Machine and Deep Learning Algorithms
Heba-Allah Ibrahim El-Azab, R.A. Swief, Noha H. El-Amary, et al.
(2024)
Closed Access
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
Short-Term Energy Forecasting to Improve the Estimation of Demand Response Baselines in Residential Neighborhoods: Deep Learning vs. Machine Learning
Abdo Abdullah Ahmed Gassar
Buildings (2024) Vol. 14, Iss. 7, pp. 2242-2242
Open Access
Comparative Analysis of Short-Term Load Forecasting Using Machine Learning Techniques
Hagos L. Shifare, Ronak Doshi, Amit Ved
Communications in computer and information science (2024), pp. 117-133
Closed Access
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