Efficient energy utilization in smart grids an artificial intelligence perspective

Book chapter


Qadir, Zakria, Khan, Yasir Ali, Rana, Muhammad Tausif Afzal, Din, Fareed Ud and Shafiabady, Niusha. (2025). Efficient energy utilization in smart grids an artificial intelligence perspective. In In Bhatia, Tarandeep Kaur, El Hajjami, Salma, Kaushik, Keshav, Diallo, Gayo, Ouaissa, Mariya and Khan, Inam Ullah (Ed.). Ethical artificial intelligence in power electronics pp. 133-147 CRC Press. https://doi.org/10.1201/9781032648323-9
AuthorsQadir, Zakria, Khan, Yasir Ali, Rana, Muhammad Tausif Afzal, Din, Fareed Ud and Shafiabady, Niusha
EditorsBhatia, Tarandeep Kaur, El Hajjami, Salma, Kaushik, Keshav, Diallo, Gayo, Ouaissa, Mariya and Khan, Inam Ullah
Abstract

The primary objective of this chapter is to tackle the undesired fluctuations stemming from DC imbalances within the circuit. A newly proposed control technique aims to address these oscillations, ensuring a stable supply of AC voltage to the load. By mitigating the effects of DC imbalances, the proposed approach seeks to enhance the reliability and efficiency of the electrical system. Through detailed analysis and experimentation, this chapter explores the effectiveness of the control technique in achieving smoother voltage output, thereby contributing to the advancement of power electronics and electrical engineering practices.

Page range133-147
Year2025
Book titleEthical artificial intelligence in power electronics
PublisherCRC Press
Place of publicationBoca Raton, Florida
London, United Kingdom
ISBN9781032631158
9781032648316
9781032648323
Digital Object Identifier (DOI)https://doi.org/10.1201/9781032648323-9
Scopus EID2-s2.0-85200428869
Publisher's version
License
All rights reserved
File Access Level
Controlled
Output statusPublished
Publication dates
Online01 Aug 2024
Print2024
Publication process dates
Deposited21 Jan 2025
Permalink -

https://acuresearchbank.acu.edu.au/item/912q0/efficient-energy-utilization-in-smart-grids-an-artificial-intelligence-perspective

Restricted files

Publisher's version

  • 7
    total views
  • 0
    total downloads
  • 6
    views this month
  • 0
    downloads this month
These values are for the period from 19th October 2020, when this repository was created.

Export as

Related outputs

eXplainable Artificial Intelligence (XAI) for improving organisational regility
Shafiabady, Niusha, Hadjinicolaou, Nick, Hettikankanamage, Nadeesha, MohammadiSavadkoohi, Ehsan, Wu, Robert M. X. and Vakilian, James. (2024). eXplainable Artificial Intelligence (XAI) for improving organisational regility. PLoS ONE. 19(4), p. Article e0301429. https://doi.org/10.1371/journal.pone.0301429
Real-time structural health monitoring of bridges using convolutional neural network-based loss factor analysis for enhanced energy dissipation detection
Nguyen, Thanh Q., Vu, Tu B., Shafiabady, Niusha, Nguyen, Thuy T. and Nguyen, Phuoc T.. (2024). Real-time structural health monitoring of bridges using convolutional neural network-based loss factor analysis for enhanced energy dissipation detection. Structures. 70, p. Article 107733. https://doi.org/10.1016/j.istruc.2024.107733
Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems
Wu, Robert M.X., Shafiabady, Niusha, Zhang, Huan, Lu, Haiyan, Gide, Ergun, Liu, Jinrong and Charbonnier, Clement Franck Benoit. (2024). Comparative study of ten machine learning algorithms for short-term forecasting in gas warning systems. Scientific Reports. 14(1), pp. 1-18. https://doi.org/10.1038/s41598-024-67283-4
Using Artificial Intelligence (AI) to predict organizational agility
Shafiabady, Niusha, Hadjinicolaou, Nick, Din, Fareed Ud, Bhandari, Binayak, Wu, Robert M. X. and Vakilian, James. (2023). Using Artificial Intelligence (AI) to predict organizational agility. PLoS ONE. 18(5), p. Article e0283066. https://doi.org/10.1371/journal.pone.0283066
Using multi-focus group method as an effective tool for eliciting business system requirements : Verified by a case study
Wu, Robert M. X., Wang, Yongwen, Shafiabady, Niusha, Zhang, Huan, Yan, Wanjun, Gou, Jinwen, Shi, Yong, Liu, Bao, Gide, Ergun, Kang, Changlong, Zhang, Zhongwu, Shen, Bo, Li, Xiaoquan, Fan, Jianfeng, He, Xiangqian, Soar, Jeffrey, Zhao, Haijun, Sun, Lei, Huo, Wenying and Wang, Ya. (2023). Using multi-focus group method as an effective tool for eliciting business system requirements : Verified by a case study. PLoS ONE. 18(3), p. Article e0281603. https://doi.org/10.1371/journal.pone.0281603
Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms
Zaidi, Syed Fawad M., Shafiabady, Niusha and Beilby, Justin. (2023). Identifying presence of cybersickness symptoms using AI-based predictive learning algorithms. Virtual Reality. 27(4), pp. 3613-3620. https://doi.org/10.1007/s10055-023-00813-z
V-CarE—A conceptual conceptual design model for providing COVID-19 pandemic awareness : Proposal for a virtual reality design approach to facilitate people with persistent postural-perceptual dizziness
Zaidi, Syed Fawad M., Shafiabady, Niusha, Afifi, Shereen and Beilby, Justin. (2023). V-CarE—A conceptual conceptual design model for providing COVID-19 pandemic awareness : Proposal for a virtual reality design approach to facilitate people with persistent postural-perceptual dizziness. JMIR Research Protocols. 12, p. Article e38369. https://doi.org/10.2196/38369
An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework
Wu, Robert M. X., Zhang, Zhongwu, Zhang, Huan, Wang, Yongwen, Shafiabady, Niusha, Yan, Wanjun, Gou, Jinwen, Gide, Ergun and Zhang, Siqing. (2023). An FSV analysis approach to verify the robustness of the triple-correlation analysis theoretical framework. Scientific Reports. 13(1), p. Article 9621. https://doi.org/10.1038/s41598-023-35900-3
Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals
Bhandari, Binayak, Park, Gijun and Shafiabady, Niusha. (2023). Implementation of transformer-based deep learning architecture for the development of surface roughness classifier using sound and cutting force signals. Neural Computing and Applications. 35(18), pp. 13275-13292. https://doi.org/10.1007/s00521-023-08425-z
Evolving Hybrid partial genetic algorithm classification model for cost-effective frailty screening : Investigative study
Oates, John, Shafiabady, Niusha, Ambagtsheer, Rachel, Beilby, Justin, Seiboth, Chris and Dent, Elsa. (2022). Evolving Hybrid partial genetic algorithm classification model for cost-effective frailty screening : Investigative study. JMIR Aging. 5(4), p. Article e38464. https://doi.org/10.2196/38464
The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set
Ambagtsheer, R. C., Shafiabady, N., Dent, E., Seiboth, C. and Beilby, J.. (2020). The application of artificial intelligence (AI) techniques to identify frailty within a residential aged care administrative data set. International Journal of Medical Informatics. 136, p. Article 104094. https://doi.org/10.1016/j.ijmedinf.2020.104094
ST (Shafiabady-Teshnehlab) optimization algorithm
Shafiabady, Niusha. (2018). ST (Shafiabady-Teshnehlab) optimization algorithm. In In Tan, Ying (Ed.). Swarm intelligence : Volume 2 : Innovation, new algorithms and methods pp. 83-110 The Institution of Engineering and Technology. https://doi.org/10.1049/pbce119g_ch4
Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents
Motlagh, Farid, Ibrahim, Fatimah, Rashid, Rusdi, Shafiabady, Niusha, Seghatoleslam, Tahereh and Habil, Hussain. (2018). Acute effects of methadone on EEG power spectrum and event-related potentials among heroin dependents. Psychopharmacology. 235(11), pp. 3273-3288. https://doi.org/10.1007/s00213-018-5035-0