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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:

Using Bayesian deep learning approaches for uncertainty-aware building energy surrogate models
Paul Westermann, Ralph Evins
Energy and AI (2021) Vol. 3, pp. 100039-100039
Open Access | Times Cited: 36

Showing 1-25 of 36 citing articles:

Data-driven probabilistic machine learning in sustainable smart energy/smart energy systems: Key developments, challenges, and future research opportunities in the context of smart grid paradigm
Tanveer Ahmad, Rafał Madoński, Dongdong Zhang, et al.
Renewable and Sustainable Energy Reviews (2022) Vol. 160, pp. 112128-112128
Closed Access | Times Cited: 336
Introducing the Open Energy Ontology: Enhancing data interpretation and interfacing in energy systems analysis
Meisam Booshehri, Lukas Emele, Simon Flügel, et al.
Energy and AI (2021) Vol. 5, pp. 100074-100074
Open Access | Times Cited: 63
Evaluation of the impact of input uncertainty on urban building energy simulations using uncertainty and sensitivity analysis
Enrico Prataviera, Jacopo Vivian, Giulia Lombardo, et al.
Applied Energy (2022) Vol. 311, pp. 118691-118691
Open Access | Times Cited: 62
An Efficient Method for Antenna Design Based on a Self-Adaptive Bayesian Neural Network-Assisted Global Optimization Technique
Yushi Liu, Bo Liu, Masood Ur Rehman, et al.
IEEE Transactions on Antennas and Propagation (2022) Vol. 70, Iss. 12, pp. 11375-11388
Open Access | Times Cited: 37
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
Meta-heuristics and deep learning for energy applications: Review and open research challenges (2018–2023)
Eghbal Hosseini, Abbas M. Al-Ghaili, Dler Hussein Kadir, et al.
Energy Strategy Reviews (2024) Vol. 53, pp. 101409-101409
Open Access | Times Cited: 13
The artificial intelligence reformation of sustainable building design approach: A systematic review on building design optimization methods using surrogate models
Ibrahim Elwy, Aya Hagishima
Energy and Buildings (2024), pp. 114769-114769
Open Access | Times Cited: 7
An optimal surrogate-model-based approach to support comfortable and nearly zero energy buildings design
Badr Chegari, Mohamed Tabaa, Emmanuel Simeu, et al.
Energy (2022) Vol. 248, pp. 123584-123584
Open Access | Times Cited: 35
Building energy model calibration: A review of the state of the art in approaches, methods, and tools
Ofelia Giannina Vera Piazzini, Massimiliano Scarpa
Journal of Building Engineering (2023) Vol. 86, pp. 108287-108287
Closed Access | Times Cited: 19
A Bayesian deep-learning framework for assessing the energy flexibility of residential buildings with multicomponent energy systems
Adamantios Bampoulas, Fabiano Pallonetto, Eleni Mangina, et al.
Applied Energy (2023) Vol. 348, pp. 121576-121576
Closed Access | Times Cited: 14
A novel temporal domain adaptation framework for residential electricity consumption forecasting under incomplete information
Sheng Li, Xiaoxiao Xu, Yadong Xu, et al.
Energy and Buildings (2025), pp. 115513-115513
Closed Access
Bayesian Optimization in Bioprocess Engineering—Where Do We Stand Today?
Florian Gisperg, Robert Klausser, Mohamed Elshazly, et al.
Biotechnology and Bioengineering (2025)
Open Access
A surrogate-based computational framework for optimizing thermal strategies in large multi-subsystem borehole heat exchanger sites
Quan Liu, Ernesto Meneses Rioseco, Finn Weiland, et al.
Energy (2025), pp. 135705-135705
Open Access
CityTFT: A temporal fusion transformer-based surrogate model for urban building energy modeling
Ting-Yu Dai, Dev Niyogi, Zoltán Nagy
Applied Energy (2025) Vol. 389, pp. 125712-125712
Closed Access
Bayesian Approaches for Efficient and Uncertainty-Aware Prediction of Pressure Distributions
Mehdi Anhichem, Sebastian Timme, Jony Castagna, et al.
AIAA SCITECH 2022 Forum (2024)
Closed Access | Times Cited: 3
Machine-learning-assisted searching for thermally conductive polymers: A mini review
Yinglong Hu, Qi Wang, Hao Ma
Journal of Applied Physics (2024) Vol. 135, Iss. 12
Closed Access | Times Cited: 3
Chance Constraints and Machine Learning integration for uncertainty management in Virtual Power Plants operating in simultaneous energy markets
Juan Aguilar, Carlos Bordons, Alicia Arce
International Journal of Electrical Power & Energy Systems (2021) Vol. 133, pp. 107304-107304
Closed Access | Times Cited: 24
Exploiting district cooling network and urban building energy modeling for large-scale integrated energy conservation analyses
Enrico Prataviera, Angelo Zarrella, Joshua Morejohn, et al.
Applied Energy (2023) Vol. 356, pp. 122368-122368
Closed Access | Times Cited: 9
Multi-objective optimisation with hybrid machine learning strategy for complex catalytic processes
Xin Tai, Raffaella Ocone, S. Christie, et al.
Energy and AI (2021) Vol. 7, pp. 100134-100134
Open Access | Times Cited: 21
Explainable deep transfer learning for energy efficiency prediction based on uncertainty detection and identification
Chanin Panjapornpon, Santi Bardeeniz, M.A. Hussain, et al.
Energy and AI (2022) Vol. 12, pp. 100224-100224
Open Access | Times Cited: 14
A New Tool for Building Energy Optimization: First Round of Successful Dynamic Model Simulations
Giacomo Chiesa, Francesca Fasano, Paolo Grasso
Energies (2021) Vol. 14, Iss. 19, pp. 6429-6429
Open Access | Times Cited: 19
Improved calibration of building models using approximate Bayesian calibration and neural networks
Kevin Cant, Ralph Evins
Journal of Building Performance Simulation (2022) Vol. 16, Iss. 3, pp. 291-307
Closed Access | Times Cited: 13
Geometric Data in Urban Building Energy Modeling: Current Practices and the Case for Automation
Shima Norouzi Kandelan, N. Mohammed, Kuljeet Singh, et al.
Journal of Building Engineering (2024), pp. 110836-110836
Open Access | Times Cited: 2
Comparison between conventional and deep learning-based surrogate models in predicting convective heat transfer performance of U-bend channels
Qi Wang, Weiwei Zhou, Li Yang, et al.
Energy and AI (2022) Vol. 8, pp. 100140-100140
Open Access | Times Cited: 11
Effect of adaptive intelligent sampling and machine-learning emulators in surrogate energy modeling of architectural massing
Abhishek Mehrotra, Hwang Yi
Journal of Building Engineering (2023) Vol. 72, pp. 106614-106614
Closed Access | Times Cited: 6
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