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The rise in electricity demand, driven by economic growth and population increase, necessitates accurate forecasting for effective power grid management. India's energy requirements have surged, contributing to its rapid economic expansion. The residential sector significantly impacts total energy consumption, accounting for approximately 27% of global electricity use. Immediate utilization of generated electricity makes demand forecasting critical for both current and future smart grids. This paper reviews various machine learning forecasting techniques for residential house and power utility. Each technique is analyzed based on input parameters, output parameters, error types, and forecasting timeframes.

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Comprehensive Review of Techniques for Forecasting Electricity Consumption

  • 会议论文

  • 发表日期：

    2025年

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  • B. N. Shwetha,
  • K. S. Harish Kumar

摘要

The rise in electricity demand, driven by economic growth and population increase, necessitates accurate forecasting for effective power grid management. India's energy requirements have surged, contributing to its rapid economic expansion. The residential sector significantly impacts total energy consumption, accounting for approximately 27% of global electricity use. Immediate utilization of generated electricity makes demand forecasting critical for both current and future smart grids. This paper reviews various machine learning forecasting techniques for residential house and power utility. Each technique is analyzed based on input parameters, output parameters, error types, and forecasting timeframes.