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<p>This study focuses on deploying sophisticated metering infrastructure in smart grids to allow efficient Demand-Side Management (DSM) programs in rural India and other nations. The study presents a flexible architecture composed of four linked modules: a renewable energy distribution system, a Seasonal ARIMA (SARIMA)-based forecast engine, Advanced Metering Infrastructure (AMI), and demand-side energy management modules. The goal is to decrease expenses, greatest demand, waiting time, &amp; Carbon dioxide (CO2) emissions while considering user comfort. The research focuses on the DSM of residential, industrial, and commercial regions by utilizing two innovative forecasts and AMI in smart grid frameworks. The suggested approach employs Ant Colony Optimization (ACO) to effectively arrange energy consumption and pricing profiles based on historical data and price-based Demand Response programs. The results show that the recommended technique decreases power prices, peak demand, CO2 emissions, and customer displeasure while improving energy efficiency and sustainability in rural regions.</p>

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An Intelligent Approach for Efficient Demand Side Management Based on Price in Smart Grids Using Advanced Metering Infrastructure Frameworks for Rural Electrification

  • 期刊论文

  • 发表日期：

    2025年6月20日

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  • S. Kannaki,
  • S. Jaganathan

摘要

This study focuses on deploying sophisticated metering infrastructure in smart grids to allow efficient Demand-Side Management (DSM) programs in rural India and other nations. The study presents a flexible architecture composed of four linked modules: a renewable energy distribution system, a Seasonal ARIMA (SARIMA)-based forecast engine, Advanced Metering Infrastructure (AMI), and demand-side energy management modules. The goal is to decrease expenses, greatest demand, waiting time, & Carbon dioxide (CO2) emissions while considering user comfort. The research focuses on the DSM of residential, industrial, and commercial regions by utilizing two innovative forecasts and AMI in smart grid frameworks. The suggested approach employs Ant Colony Optimization (ACO) to effectively arrange energy consumption and pricing profiles based on historical data and price-based Demand Response programs. The results show that the recommended technique decreases power prices, peak demand, CO2 emissions, and customer displeasure while improving energy efficiency and sustainability in rural regions.