Smart City Gnosys

Smart city article details

Title	Smart Grid Stability Prediction Using Binary Manta Ray Foraging-Based Machine Learning
ID_Doc	50976
Authors	Jayasheel Kumar K.A.; V. R.; kavin B.P.; Seng G.H.
Year	2024
Published	Green Machine Learning and Big Data for Smart Grids: Practices and Applications
DOI	http://dx.doi.org/10.1016/B978-0-443-28951-4.00007-1
Abstract	Modern information and communication technologies integration has had a tremendous impact on power grids. The creation of smart grids, which are notable for their higher efficiency and reduced running costs, is being facilitated by this advancement. Maintaining the integrity of these power networks is the top priority in this paradigm shift due to their critical role in meeting the expanding energy demands of smart cities, residences, industrial sites, and more. To get around this problem, several Machine Learning and Deep Learning models may be employed to predict stability in energy networks. The crucial role that IoT technology plays in providing electricity grid networks with intelligence is highlighted by this research. To do this, the study employs a Weighted Extreme Learning Machine (WELM) with the innovative Binary Manta Ray Foraging for weight selection. Notably, the research compares the efficacy of several prediction models using key metrics including accuracy, precision, recall, and the F1 score. Additionally, the dataset undergoes extensive preprocessing employing data augmentation and feature scaling techniques, yielding excellent results. Particularly, the extended dataset exhibits a tremendous boost in performance, with an astounding accuracy rate of 99%. This investigation therefore demonstrates unequivocally that the proposed WELM model beats rival predictive models in the domain of forecasting energy grid stability, holding the possibility of increased grid resilience and dependability. © 2025 Elsevier Inc. All rights are reserved including those for text and data mining AI training and similar technologies.
Author Keywords	binary manta ray foraging; machine learning; Smart grids; stability prediction; weighted extreme learning machine

Similar Articles

Id	Similarity	Authors	Title	Published
35861	0.875	Gunjal G.P.; Teke M.M.; Deulkar A.M.; Pardeshi D.B.	Machine - Learning Based Prediction Of Stability Of Smart Grid	Proceedings - 2024 International Conference on Expert Clouds and Applications, ICOECA 2024 (2024)
10158	0.874	Salam A.; El Hibaoui A.	Applying Deep Learning Model To Predict Smart Grid Stability	Proceedings of 2021 9th International Renewable and Sustainable Energy Conference, IRSEC 2021 (2021)
36056	0.873	Tiwari S.; Jain A.; Ahmed N.M.O.S.; Charu; Alkwai L.M.; Dafhalla A.K.Y.; Hamad S.A.S.	Machine Learning-Based Model For Prediction Of Power Consumption In Smart Grid- Smart Way Towards Smart City	Expert Systems, 39, 5 (2022)
35996	0.872	Haque A.; Malik A.	Machine Learning In Renewable Energy Systems For Smart Cities	Smart Cities: Power Electronics, Renewable Energy, and Internet of Things (2024)
27599	0.872	Martinelli F.; Mercaldo F.; Santone A.	Fuzzy Machine Learning For Smart Grid Instability Detection	Artificial Intelligence for Security: Enhancing Protection in a Changing World (2024)
42836	0.869	Sarkar P.K.; Sarkar P.K.; Bin Atique M.M.A.	Prediction Of Power Consumption In Smart Grid: A Reliable Path To A Smart City Based On Various Machine Learning Models	International Conference on Recent Progresses in Science, Engineering and Technology, ICRPSET 2022 (2022)
32373	0.867	Mithul Raaj A.T.; Balaji B.; R R S.A.P.; Naidu R.C.; Rajesh Kumar M.; Ramachandran P.; Rajkumar S.; Kumar V.N.; Aggarwal G.; Siddiqui A.M.	Intelligent Energy Management Across Smart Grids Deploying 6G Iot, Ai, And Blockchain In Sustainable Smart Cities	IoT, 5, 3 (2024)
42128	0.862	Almoussawi Z.A.; Kurdi W.H.M.; Khaleel B.M.; Al-Attabi K.; Sabah H.A.; Alazzai W.K.	Planet Optimization With Machine Learning Enabled Power Usage Forecasting Modeling In Smart Grid Environment	6th Iraqi International Conference on Engineering Technology and its Applications, IICETA 2023 (2023)
35082	0.862	Mendhiratta A.; Fernandes C.; Hindlatti D.; Vinayak D.; Pomu Chavan C.	Leveraging Graph Digital Twin For Fault Detection And Improved Power Grid Stability In Smart Cities	Lecture Notes in Computer Science, 15912 LNCS (2025)
37643	0.862	Abbas, S; Khan, MA; Falcon-Morales, LE; Rehman, A; Saeed, Y; Zareei, M; Zeb, A; Mohamed, EM	Modeling, Simulation And Optimization Of Power Plant Energy Sustainability For Iot Enabled Smart Cities Empowered With Deep Extreme Learning Machine	IEEE ACCESS, 8 (2020)