A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid

Publication type: Journal Article
Publication date: 2023-09-07
Institute of Electrical and Electronics Engineers (IEEE)
scimago Q1
wos Q1
SJR: 1.276
CiteScore: 11.8
Impact factor: 4.4
ISSN: 19328184, 19379234, 23737816
Computer Science Applications
Electrical and Electronic Engineering
Information Systems
Computer Networks and Communications
Control and Systems Engineering
Abstract
The uncertainty and unpredictable power generation and usage of resources at demand as well as supply side causes the mismatch problem. Therefore, the power demand and supply management of regional smart meters have become an essential and challenging research issue in the current era. The existing solutions have inadequately explored the relationship between forecasted load and power demand supply management of smart meters, which imposes a huge impact on the performance of power services to the regional customers. In this context, a novel quantum machine learning-driven power distribution and supply management (QPD-SM) model is proposed, which estimates the power demand of regional smart meters proactively by utilizing the load forecasting values and managing power supply efficiently. The model deploys a newly developed "quantum controlled-NOT gate induced feed-forward neural network" (QCNN) which is optimized using the quantum adaptive differential evolutionary (QuADE) learning algorithm to forecast regional load with precise accuracy. Further, a demand–supply management approach is designed to analyze and estimate the over-/under-supply of power units relative to different regions and govern the power generation and supply management accordingly. Experimental results from the Irish Commission for Energy Regulation and the Australian Smart Grid Smart City show the improvement of root-mean-squared error up to 77.02% and 78.92%, respectively, by the proposed model as compared to the existing state-of-the-art works.
Found 
Found 

Top-30

Journals

1
2
Cluster Computing
Cluster Computing, 2, 11.11%
Cluster Computing
2 publications, 11.11%
Applied Soft Computing Journal
Applied Soft Computing Journal, 1, 5.56%
Applied Soft Computing Journal
1 publication, 5.56%
Renewable and Sustainable Energy Reviews
Renewable and Sustainable Energy Reviews, 1, 5.56%
Renewable and Sustainable Energy Reviews
1 publication, 5.56%
Electrical Engineering
Electrical Engineering, 1, 5.56%
Electrical Engineering
1 publication, 5.56%
Neural Computing and Applications
Neural Computing and Applications, 1, 5.56%
Neural Computing and Applications
1 publication, 5.56%
Electronics (Switzerland)
Electronics (Switzerland), 1, 5.56%
Electronics (Switzerland)
1 publication, 5.56%
AEJ - Alexandria Engineering Journal, 1, 5.56%
AEJ - Alexandria Engineering Journal
1 publication, 5.56%
IEEE Transactions on Network and Service Management
IEEE Transactions on Network and Service Management, 1, 5.56%
IEEE Transactions on Network and Service Management
1 publication, 5.56%
Knowledge-Based Systems
Knowledge-Based Systems, 1, 5.56%
Knowledge-Based Systems
1 publication, 5.56%
International Journal of Management Science and Engineering Management
International Journal of Management Science and Engineering Management, 1, 5.56%
International Journal of Management Science and Engineering Management
1 publication, 5.56%
IEEE Access
IEEE Access, 1, 5.56%
IEEE Access
1 publication, 5.56%
Sustainable Computing: Informatics and Systems
Sustainable Computing: Informatics and Systems, 1, 5.56%
Sustainable Computing: Informatics and Systems
1 publication, 5.56%
IEEE Transactions on Neural Networks and Learning Systems
IEEE Transactions on Neural Networks and Learning Systems, 1, 5.56%
IEEE Transactions on Neural Networks and Learning Systems
1 publication, 5.56%
1
2

Publishers

1
2
3
4
5
6
7
Institute of Electrical and Electronics Engineers (IEEE)
Institute of Electrical and Electronics Engineers (IEEE), 7, 38.89%
Institute of Electrical and Electronics Engineers (IEEE)
7 publications, 38.89%
Elsevier
Elsevier, 5, 27.78%
Elsevier
5 publications, 27.78%
Springer Nature
Springer Nature, 4, 22.22%
Springer Nature
4 publications, 22.22%
MDPI
MDPI, 1, 5.56%
MDPI
1 publication, 5.56%
Taylor & Francis
Taylor & Francis, 1, 5.56%
Taylor & Francis
1 publication, 5.56%
1
2
3
4
5
6
7
  • We do not take into account publications without a DOI.
  • Statistics recalculated weekly.

Are you a researcher?

Create a profile to get free access to personal recommendations for colleagues and new articles.
Metrics
18
Share
Cite this
GOST |
Cite this
GOST Copy
Kumar J. et al. A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid // IEEE Systems Journal. 2023. pp. 1-12.
GOST all authors (up to 50) Copy
Kumar J., Saxena D., Singh A. K., Vasilakos A. V. A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid // IEEE Systems Journal. 2023. pp. 1-12.
RIS |
Cite this
RIS Copy
TY - JOUR
DO - 10.1109/jsyst.2023.3309324
UR - https://ieeexplore.ieee.org/document/10243065/
TI - A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid
T2 - IEEE Systems Journal
AU - Kumar, Jatinder
AU - Saxena, Deepika
AU - Singh, Ashutosh Kumar
AU - Vasilakos, Athanasios V.
PY - 2023
DA - 2023/09/07
PB - Institute of Electrical and Electronics Engineers (IEEE)
SP - 1-12
SN - 1932-8184
SN - 1937-9234
SN - 2373-7816
ER -
BibTex
Cite this
BibTex (up to 50 authors) Copy
@article{2023_Kumar,
author = {Jatinder Kumar and Deepika Saxena and Ashutosh Kumar Singh and Athanasios V. Vasilakos},
title = {A Quantum Controlled-NOT Neural Network-Based Load Forecast and Management Model for Smart Grid},
journal = {IEEE Systems Journal},
year = {2023},
publisher = {Institute of Electrical and Electronics Engineers (IEEE)},
month = {sep},
url = {https://ieeexplore.ieee.org/document/10243065/},
pages = {1--12},
doi = {10.1109/jsyst.2023.3309324}
}