Scholarlink

Long-term sustainability is increasingly important for smart cities, and the energy management in smart cities plays a key role in achieving the aim of research. This research focuses on improving energy efficiency using Neural Support Vector Machine (NSVM) learning in smart cities to measure the energy consumption to optimize smart grids. The research highlights the limitations of smart grid to integrate renewable energy sources and energy consumption patterns. This paper uses advanced model to analyze, forecast, and improve energy consumption in smart grids. The proposed NSVM-based machine learning understands the data energy usage patterns with improved accuracy.

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Enhancing Energy Efficiency in Smart Cities Through Neural Support Vector Machine Learning for Smart Grids

  • 会议论文

  • 发表日期：

    2025年

查看全文
  • G. Indra,
  • Telagamalla Gopi,
  • Prashant Kumar Gangwar,
  • K. R. Senthil Kumar,
  • M. Rajendiran,
  • S. Balasubramani

摘要

Long-term sustainability is increasingly important for smart cities, and the energy management in smart cities plays a key role in achieving the aim of research. This research focuses on improving energy efficiency using Neural Support Vector Machine (NSVM) learning in smart cities to measure the energy consumption to optimize smart grids. The research highlights the limitations of smart grid to integrate renewable energy sources and energy consumption patterns. This paper uses advanced model to analyze, forecast, and improve energy consumption in smart grids. The proposed NSVM-based machine learning understands the data energy usage patterns with improved accuracy.