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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:

A deep learning framework using multi-feature fusion recurrent neural networks for energy consumption forecasting
Lei Fang, Bin He
Applied Energy (2023) Vol. 348, pp. 121563-121563
Closed Access | Times Cited: 32

Showing 1-25 of 32 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: 46
Investigating the Impact of Data Normalization Methods on Predicting Electricity Consumption in a Building Using different Artificial Neural Network Models.
Yang‐Seon Kim, Moon Keun Kim, Nuodi Fu, et al.
Sustainable Cities and Society (2024), pp. 105570-105570
Open Access | Times Cited: 16
Design of IoT energy consumption forecasting model for residential buildings based on improved long short-term memory (LSTM)
Mustafa Wassef Hasan
Deleted Journal (2025), pp. 100033-100033
Open Access | Times Cited: 1
Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models
Lanouar Charfeddine, Esmat Zaidan, Ahmad Qadeib Alban, et al.
Sustainable Cities and Society (2023) Vol. 98, pp. 104860-104860
Open Access | Times Cited: 25
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
A review of the influencing factors of building energy consumption and the prediction and optimization of energy consumption
Zhongjiao Ma, Z. Yan, M. He, et al.
AIMS energy (2025) Vol. 13, Iss. 1, pp. 35-85
Open Access
An improved multivariable grey Riccati–Bernoulli model and its application in energy consumption prediction
Meng Dun, Yaoguo Dang, Junjie Wang, et al.
Environment Development and Sustainability (2025)
Closed Access
Multi-step short-term forecasting of photovoltaic power utilizing TimesNet with enhanced feature extraction and a novel loss function
Shengquan Yu, Bin He, Lei Fang
Applied Energy (2025) Vol. 388, pp. 125645-125645
Closed Access
Interconnected Industry 4.0 technologies: identifying current network value and integration opportunities
Vincenzo Varriale, Antonello Cammarano, Francesca Michelino, et al.
Journal of Industrial Information Integration (2025), pp. 100838-100838
Open Access
Optimizing deep neural network architectures for renewable energy forecasting
Sunawar Khan, Tehseen Mazhar, Tariq Shahzad, et al.
Discover Sustainability (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 3
From irregular to continuous: The deep Koopman model for time series forecasting of energy equipment
Jiaqi Ding, Pu Zhao, Changjun Liu, et al.
Applied Energy (2024) Vol. 364, pp. 123138-123138
Closed Access | Times Cited: 2
Household Energy Consumption Forecasting based on Adaptive Signal Decomposition Enhanced iTransformer Network
Jian Liu, Fan Yang, Ke Yan, et al.
Energy and Buildings (2024) Vol. 324, pp. 114894-114894
Closed Access | Times Cited: 2
Deep neural network for investment decision planning on low-carbon transition in power grid
M Wang, Yixiao Wang, Bobo Chen, et al.
International Journal of Low-Carbon Technologies (2024) Vol. 19, pp. 1368-1379
Open Access | Times Cited: 2
DSPM: Dual sequence prediction model for efficient energy management in micro-grid
Zulfiqar Ahmad Khan, S. A. Khan, Tanveer Hussain, et al.
Applied Energy (2023) Vol. 356, pp. 122339-122339
Closed Access | Times Cited: 6
Spatio-temporal prediction of total energy consumption in multiple regions using explainable deep neural network
Shiliang Peng, Lin Fan, Zhang Li, et al.
Energy (2024) Vol. 301, pp. 131526-131526
Closed Access | Times Cited: 1
Research on predicting heat loads based on extracting temporal and spatial features of multiple buildings using data-driven methods
Quanwei Tan, Guijun Xue, Wenju Xie
Journal of Building Performance Simulation (2024) Vol. 17, Iss. 5, pp. 563-584
Closed Access | Times Cited: 1
Multi-swarm multi-tasking ensemble learning for multi-energy demand prediction
Hui Song, Boyu Zhang, Mahdi Jalili, et al.
Applied Energy (2024) Vol. 377, pp. 124553-124553
Open Access | Times Cited: 1
MILET: multimodal integration and linear enhanced transformer for electricity price forecasting
Lisen Zhao, Lihua Lü, Yu Xiang
Systems Science & Control Engineering (2024) Vol. 12, Iss. 1
Open Access | Times Cited: 1
A novel information enhanced Grey Lotka–Volterra model driven by system mechanism and data for energy forecasting of WEET project in China
Tianyao Duan, Huan Guo, Qi Xiao, et al.
Energy (2024) Vol. 304, pp. 132176-132176
Closed Access | Times Cited: 1
Manufacturing system evaluation in terms of system reliability via long short-term memory
Cheng-Hao Huang, Yi‐Kuei Lin
Reliability Engineering & System Safety (2024) Vol. 251, pp. 110365-110365
Closed Access | Times Cited: 1
Comparative analysis of deep neural network architectures for renewable energy forecasting: enhancing accuracy with meteorological and time-based features
Sunawar Khan, Tehseen Mazhar, Muhammad Amir Khan, et al.
Discover Sustainability (2024) Vol. 5, Iss. 1
Open Access | Times Cited: 1
Multiple Cross-Attention and Multi-Task Learning Based Model for Multi-Energy Load Forecasting with Hybrid Time Granularities
Yunfei Zhang, Jun Shen, Jian Li, et al.
(2024)
Closed Access
A method for analyzing the irrepazrability of diverse electricity consumption data based on improved data generation technology
Yuying Ma, Xiangyu Kong, Liang Zhao, et al.
Applied Energy (2024) Vol. 374, pp. 123994-123994
Closed Access
Real-time prediction of the chemical oxygen demand component parameters in activated sludge model using backpropagation neural network
Ping Wang, Yanqiong Chen, Chen Zhang, et al.
Heliyon (2024) Vol. 10, Iss. 16, pp. e35580-e35580
Open Access
Short-term multi-energy consumption forecasting for integrated energy system based on interactive multi-scale convolutional module
Zhiyuan Liu, Yucong Huang, Y Wang, et al.
Scientific Reports (2024) Vol. 14, Iss. 1
Open Access
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