Open Access Helper

OpenAlex Citation Counts

OpenAlex Citations Logo

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:

Residential load forecasting based on LSTM fusing self-attention mechanism with pooling
Haixiang Zang, Ruiqi Xu, Lilin Cheng, et al.
Energy (2021) Vol. 229, pp. 120682-120682
Closed Access | Times Cited: 176

Showing 1-25 of 176 citing articles:

Review and prospect of data-driven techniques for load forecasting in integrated energy systems
Jizhong Zhu, Hanjiang Dong, Weiye Zheng, et al.
Applied Energy (2022) Vol. 321, pp. 119269-119269
Closed Access | Times Cited: 171
Oil well production prediction based on CNN-LSTM model with self-attention mechanism
Shaowei Pan, Bo Yang, Shukai Wang, et al.
Energy (2023) Vol. 284, pp. 128701-128701
Closed Access | Times Cited: 83
A performance degradation prediction model for PEMFC based on bi-directional long short-term memory and multi-head self-attention mechanism
Chunchun Jia, Hongwen He, Jiaming Zhou, et al.
International Journal of Hydrogen Energy (2024) Vol. 60, pp. 133-146
Closed Access | Times Cited: 55
Optimized EWT-Seq2Seq-LSTM with Attention Mechanism to Insulators Fault Prediction
Anne Carolina Rodrigues Klaar, Stéfano Frizzo Stefenon, Laio Oriel Seman, et al.
Sensors (2023) Vol. 23, Iss. 6, pp. 3202-3202
Open Access | Times Cited: 54
Short-term power load forecasting based on AC-BiLSTM model
Fang Liu, Liang Chen
Energy Reports (2024) Vol. 11, pp. 1570-1579
Open Access | Times Cited: 22
Enhancing accuracy in point-interval load forecasting: A new strategy based on data augmentation, customized deep learning, and weighted linear error correction
Weican Liu, Zhirui Tian, Y.Q. Qiu
Expert Systems with Applications (2025), pp. 126686-126686
Closed Access | Times Cited: 1
National-scale electricity peak load forecasting: Traditional, machine learning, or hybrid model?
Juyong Lee, Youngsang Cho
Energy (2021) Vol. 239, pp. 122366-122366
Open Access | Times Cited: 87
A deep model for short-term load forecasting applying a stacked autoencoder based on LSTM supported by a multi-stage attention mechanism
Zahra Fazlipour, Elaheh Mashhour, Mahmood Joorabian
Applied Energy (2022) Vol. 327, pp. 120063-120063
Closed Access | Times Cited: 57
A robust day-ahead scheduling strategy for EV charging stations in unbalanced distribution grid
Bakul Kandpal, Parikshit Pareek, Ashu Verma
Energy (2022) Vol. 249, pp. 123737-123737
Closed Access | Times Cited: 50
Principles, research status, and prospects of feature engineering for data-driven building energy prediction: A comprehensive review
Zeyu Wang, Lisha Xia, Hongping Yuan, et al.
Journal of Building Engineering (2022) Vol. 58, pp. 105028-105028
Closed Access | Times Cited: 49
A short-term load forecasting model based on mixup and transfer learning
Yuting Lu, Gaocai Wang, Shuqiang Huang
Electric Power Systems Research (2022) Vol. 207, pp. 107837-107837
Closed Access | Times Cited: 47
LSTM enhanced by dual-attention-based encoder-decoder for daily peak load forecasting
Kedong Zhu, Yaping Li, Wenbo Mao, et al.
Electric Power Systems Research (2022) Vol. 208, pp. 107860-107860
Closed Access | Times Cited: 45
Meta-ANN – A dynamic artificial neural network refined by meta-learning for Short-Term Load Forecasting
Xun Xiao, Huadong Mo, Yinan Zhang, et al.
Energy (2022) Vol. 246, pp. 123418-123418
Closed Access | Times Cited: 43
Missing well logs prediction using deep learning integrated neural network with the self-attention mechanism
Jun Wang, Junxing Cao, Jingcheng Fu, et al.
Energy (2022) Vol. 261, pp. 125270-125270
Closed Access | Times Cited: 39
Hybrid model for short-term wind power forecasting based on singular spectrum analysis and a temporal convolutional attention network with an adaptive receptive field
Zhen Shao, Jun Han, Wei Zhao, et al.
Energy Conversion and Management (2022) Vol. 269, pp. 116138-116138
Closed Access | Times Cited: 38
Short-term load forecasting based on WM algorithm and transfer learning model
Nan Wei, Chuang Yin, Lihua Yin, et al.
Applied Energy (2023) Vol. 353, pp. 122087-122087
Closed Access | Times Cited: 36
Improved LSTM based state of health estimation using random segments of the charging curves for lithium-ion batteries
Liang Zhang, Junyu Zhang, Tian Gao, et al.
Journal of Energy Storage (2023) Vol. 74, pp. 109370-109370
Open Access | Times Cited: 36
A deep LSTM‐CNN based on self‐attention mechanism with input data reduction for short‐term load forecasting
Shiyan Yi, Haichun Liu, Tao Chen, et al.
IET Generation Transmission & Distribution (2023) Vol. 17, Iss. 7, pp. 1538-1552
Open Access | Times Cited: 28
DA-LSTM: A dynamic drift-adaptive learning framework for interval load forecasting with LSTM networks
Firas Bayram, Phil Aupke, Bestoun S. Ahmed, et al.
Engineering Applications of Artificial Intelligence (2023) Vol. 123, pp. 106480-106480
Open Access | Times Cited: 28
Construction and Application of Short-Term and Mid-Term Power System Load Forecasting Model Based on Hybrid Deep Learning
Hongsheng Xu, Ganglong Fan, Guofang Kuang, et al.
IEEE Access (2023) Vol. 11, pp. 37494-37507
Open Access | Times Cited: 21
A framework for electricity load forecasting based on attention mechanism time series depthwise separable convolutional neural network
Huifeng Xu, Feihu Hu, Xinhao Liang, et al.
Energy (2024) Vol. 299, pp. 131258-131258
Closed Access | Times Cited: 12
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
Short-term power load forecasting based on Seq2Seq model integrating Bayesian Optimization, Temporal Convolutional Network and Attention
Yeming Dai, Wei–Jie Yu
Applied Soft Computing (2024), pp. 112248-112248
Closed Access | Times Cited: 7
A CNN-Sequence-to-Sequence network with attention for residential short-term load forecasting
Mosbah Aouad, Hazem Hajj, Khaled Shaban, et al.
Electric Power Systems Research (2022) Vol. 211, pp. 108152-108152
Closed Access | Times Cited: 35
Data-driven cooling, heating and electrical load prediction for building integrated with electric vehicles considering occupant travel behavior
Xiaofeng Zhang, Xiaoying Kong, Renshi Yan, et al.
Energy (2022) Vol. 264, pp. 126274-126274
Closed Access | Times Cited: 34
Page 1 - Next Page

Scroll to top