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Modern power grids have become more complex, which makes efficient energy management solutions imperative. Smart grids are a promising solution to optimize the distribution of energy and maintain stability in the grid by employing automation and predictive analytics. The present paper deals with various automated techniques, including machine learning models and real-time data processing, for future load forecasting. A comparative analysis was presented on classical and modern forecast methods, whereby the benefits and superiority of predictions through artificial intelligence were highlighted and underlined concerning energy consumption behavior. The finding thus emphasizes more the integration of advanced analytics to energy management.

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Energy Prediction Model in IoT Networks Using Deep Learning

  • 会议论文

  • 发表日期：

    2026年

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  • Subhankar Panda,
  • Senthilnathan Balasubramaniam,
  • Mohan Kumar Meesala,
  • Direesh Reddy Aunugu,
  • Mahima Bansod,
  • Venkata Penumarthi

摘要

Modern power grids have become more complex, which makes efficient energy management solutions imperative. Smart grids are a promising solution to optimize the distribution of energy and maintain stability in the grid by employing automation and predictive analytics. The present paper deals with various automated techniques, including machine learning models and real-time data processing, for future load forecasting. A comparative analysis was presented on classical and modern forecast methods, whereby the benefits and superiority of predictions through artificial intelligence were highlighted and underlined concerning energy consumption behavior. The finding thus emphasizes more the integration of advanced analytics to energy management.