A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence

Shahzadi, Nazia; Javaid, Nadeem; Akbar, Mariam; Aldegheishem, Abdulaziz; Alrajeh, Nabil; Bouk, Safdar Hussain

doi:10.1016/j.eswa.2024.124182

Expert Systems with Applications

2024

DOI: 10.1016/j.eswa.2024.124182

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A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence

Nazia Shahzadi,

Nadeem Javaid,

Mariam Akbar

et al.

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2024

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Cited by 7 publications

References 30 publications

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Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management

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Using optimization techniques based on neural networks, this study explores how microgrids might integrate renewable energy sources. Dealing with problems caused by the uncertainty and unpredictability of renewable energy generation is the primary goal. Renewable energy generation has been showing encouraging trends, according to data analysis spanning many time periods. From 120 kWh to 140 kWh, there was a steady rise of 16.67% in solar energy utilization. Also, there was an 18.75% rise, from 80 kWh to 95 kWh, in the use of wind power. There was a 30% rise, from 50 kWh to 65 kWh, in the output of biomass energy. Microgrid load utilization analysis shows rising energy demands in commercial, industrial, and residential areas. Commercial and industrial loads climbed by 15% and 10%, respectively, while residential energy use increased by 10%, from 150 kWh to 165 kWh. With solar predictions at 98.4%, wind predictions at 95.5%, and biomass predictions at 97.3%, predictions made using neural networks were highly congruent with actual output of renewable energy.

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A novel data driven approach for combating energy theft in urbanized smart grids using artificial intelligence

Cited by 7 publications

References 30 publications

Enhancing clinical decision support with explainable deep learning framework for C-section forecasting

Enhancing clinical decision support with explainable deep learning framework for C-section forecasting

Electricity consumption forecasting for sustainable smart cities using machine learning methods

Optimizing Smart Grids with Advanced AI Algorithms for Real-time Energy Management

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