Smart City Gnosys

Smart city article details

Title	Detection Of Smart Grids Instability With Convolutional Neural Networks And Global Explainability
ID_Doc	19282
Authors	Martinelli F.; Mercaldo F.; Santone A.
Year	2023
Published	2023 8th International Conference on Smart and Sustainable Technologies, SpliTech 2023
DOI	http://dx.doi.org/10.23919/SpliTech58164.2023.10193281
Abstract	A smart grid is an advanced concept of power systems devoted to harmonize electricity and communication in system networks. It is able to provide real-time information to producers, operators and consumers. There is an urgent demand to efficiently route supplied energy to consumer domains such as, for instance, households, organisations, industries and also smart cities. In this context, a smart grid with a stable system is required to supply the dynamic energy demand. In this paper, we propose a method aimed to detect, in real-time, whether a smart grid is in an instable state. We exploit deep learning, by considering a neural network developed by authors and global explainability, with the aim to determine the features useful in the model prediction. We evaluate a dataset composed by 60000 smart grid observations we obtain interesting results, by demonstrating the effectiveness of the proposed method. © 2023 University of Split, FESB.
Author Keywords

Similar Articles

Id	Similarity	Authors	Title	Published
10158	0.935	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)
24580	0.891	Martinelli F.; Mercaldo F.; Santone A.	Evaluating Fuzzy Machine Learning For Smart Grid Instability Detection	IEEE International Conference on Fuzzy Systems (2023)
24568	0.888	Younisse R.; AlKasassbeh M.	Evaluating Deep Learning For Detecting Data Integrity Attacks In Energy Smart Grids	2025 International Conference on New Trends in Computing Sciences, ICTCS 2025 (2025)
42836	0.887	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)
27599	0.887	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)
10561	0.877	Sulaiman A.; Nagu B.; Kaur G.; Karuppaiah P.; Alshahrani H.; Reshan M.S.A.; AlYami S.; Shaikh A.	Artificial Intelligence-Based Secured Power Grid Protocol For Smart City	Sensors, 23, 19 (2023)
8065	0.867	Baig M.A.A.; Ratyal N.I.; Amin A.; Jamil U.; Liaquat S.; Khalid H.M.; Zia M.F.	An Ensemble Deep Cnn Approach For Power Quality Disturbance Classification: A Technological Route Towards Smart Cities Using Image-Based Transfer	Future Internet, 16, 12 (2024)
36056	0.864	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)
35861	0.863	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)
23955	0.861	Rao S.P.C.; Swarnam S.; Kumar N.; Sheikameer Batcha S.; Sivalanka V.; Chakravarthi A.V.D.	Enhancing Smart City Efficiency By Mitigating Electricity Theft In Smart Grids Using Lightweight Dnn And Smo	3rd International Conference on Integrated Circuits and Communication Systems, ICICACS 2025 (2025)