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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:

Day-ahead electricity price forecasting employing a novel hybrid frame of deep learning methods: A case study in NSW, Australia
Yong Qiang Tan, Yan Shen, Xin Yu, et al.
Electric Power Systems Research (2023) Vol. 220, pp. 109300-109300
Closed Access | Times Cited: 19

Showing 19 citing articles:

Two-step deep learning framework with error compensation technique for short-term, half-hourly electricity price forecasting
Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, et al.
Applied Energy (2023) Vol. 353, pp. 122059-122059
Open Access | Times Cited: 25
Electricity market price forecasting using ELM and Bootstrap analysis: A case study of the German and Finnish Day-Ahead markets
Stylianos Loizidis, Andreas Kyprianou, George E. Georghiou
Applied Energy (2024) Vol. 363, pp. 123058-123058
Open Access | Times Cited: 15
1VMD-ATT-LSTM Electricity Price Prediction Based on Grey Wolf Optimization Algorithm in Electricity Markets Considering Renewable Energy
Yuzhen Xu, Xin Huang, Xidong Zheng, et al.
Renewable Energy (2024), pp. 121408-121408
Closed Access | Times Cited: 4
Dynamic energy cost prediction for industrial and commercial buildings using advanced Machine Learning and multi-pricing models
Qazi Waqas Khan, Anam Nawaz Khan, Bibi Misbah, et al.
Journal of Building Engineering (2025), pp. 112055-112055
Open Access
Short-Term Wind Power Prediction Method Based on CEEMDAN-VMD-GRU Hybrid Model
Na Fang, Zhengguang Liu, Shengli Fan
Energies (2025) Vol. 18, Iss. 6, pp. 1465-1465
Open Access
Comparative Study of Data-Driven Short-Term Forecasting Techniques in Electricity Price Prediction
Rahul Sagwal, S. N. Singh, J. Ramkumar, et al.
Lecture notes in networks and systems (2025), pp. 215-232
Closed Access
Day-Ahead Energy Price Forecasting with Machine Learning: Role of Endogenous Predictors
Chibuike Chiedozie Ibebuchi
Forecasting (2025) Vol. 7, Iss. 2, pp. 18-18
Open Access
A novel electricity price forecasting approach based on multi-attention feature fusion model optimized by variational mode decomposition
Yuzhen Xu, Xin Huang, Zongliang Gao, et al.
Measurement (2025), pp. 117596-117596
Closed Access
A Prediction Model for Spot LNG Prices Based on Machine Learning Algorithms to Reduce Fluctuation Risks in Purchasing Prices
Sun-Feel Yang, So-Won Choi, Eul‐Bum Lee
Energies (2023) Vol. 16, Iss. 11, pp. 4271-4271
Open Access | Times Cited: 7
A hybrid forecasting method considering the long-term dependence of day-ahead electricity price series
Yufeng Guo, Yilin Du, Pu Wang, et al.
Electric Power Systems Research (2024) Vol. 235, pp. 110841-110841
Closed Access | Times Cited: 2
A New Artificial Intelligence-Based System for Optimal Electricity Arbitrage of a Second-Life Battery Station in Day-Ahead Markets
Oğuz KIRAT, Alper Çiçek, Tarık Yerlikaya
Applied Sciences (2024) Vol. 14, Iss. 21, pp. 10032-10032
Open Access | Times Cited: 2
An Intra-Day Electricity Price Forecasting Based on a Probabilistic Transformer Neural Network Architecture
Sergio Cantillo-Luna, Ricardo Moreno‐Chuquen, Jesus Lopez-Sotelo, et al.
Energies (2023) Vol. 16, Iss. 19, pp. 6767-6767
Open Access | Times Cited: 4
Optimization strategy of power purchase and sale for electricity retailers in a two-tier market
Bowen Zhou, Yuwei Guo, Xin Liu, et al.
Heliyon (2024) Vol. 10, Iss. 4, pp. e26333-e26333
Open Access | Times Cited: 1
Day-ahead electricity price prediction in multi-price zones based on multi-view fusion spatio-temporal graph neural network
Anbo Meng, Jianbin Zhu, Yan Baiping, et al.
Applied Energy (2024) Vol. 369, pp. 123553-123553
Closed Access | Times Cited: 1
An Innovative Hybrid Deep Learning Approach for Enhanced Electrical Power Prediction Using Meteorological Data: GGWO-IEMD/SCPDAE-LSTM-SDPAM/RS Model
Mücella Özbay Karakuş, Muhammet Emin Şahin, Hasan Ulutaş
Arabian Journal for Science and Engineering (2024)
Closed Access
Advanced Optimal System for Electricity Price Forecasting Based on Hybrid Techniques
Hua Luo, Yuanyuan Shao
Energies (2024) Vol. 17, Iss. 19, pp. 4833-4833
Open Access
Predicting e-commerce product prices through the integration of variational mode decomposition and deep neural networks
Haojie Wu
PeerJ Computer Science (2024) Vol. 10, pp. e2353-e2353
Open Access
Enhancing Mutual Fund Price Prediction: A Hybrid Ensemble Approach with Random Forest, SVR, Ridge, and Gradient Boosting Regressors
Sanjay Kumar, M. Srivastava, Vijay Prakash
Advances in intelligent systems and computing (2024), pp. 551-566
Closed Access
Intra-day Electricity Price Forecasting Based on a Time2Vec-LSTM Model
Sergio Cantillo-Luna, Ricardo Moreno‐Chuquen, Jesus Lopez-Sotelo
Communications in computer and information science (2023), pp. 107-121
Closed Access
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