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Accurate forecasting of electricity demand is essential for efficient energy management and policy planning, particularly in India, where the power sector plays a vital role in economic growth. This study explores three predictive models—Multiple Regression, the Trend Seasonality Model, and ARIMA—to estimate electricity demand in New Delhi. It analyzes the influence of climatic and seasonal variables on power consumption, highlighting their role in demand fluctuations. Additionally, the study evaluates the accuracy of these models, providing comparative insights that can aid in optimizing energy distribution and ensuring a reliable power supply.

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AI Model for Electricity Demand and Peak Projection for New Delhi

  • 会议论文

  • 发表日期：

    2026年

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  • Shubham Dashore,
  • Sankalp Hardiya,
  • Sanyam Shrivastav,
  • Satyam Patidar,
  • Leeladhar Chourasiya,
  • Sushma Khatri,
  • Kamal K. Sethi

摘要

Accurate forecasting of electricity demand is essential for efficient energy management and policy planning, particularly in India, where the power sector plays a vital role in economic growth. This study explores three predictive models—Multiple Regression, the Trend Seasonality Model, and ARIMA—to estimate electricity demand in New Delhi. It analyzes the influence of climatic and seasonal variables on power consumption, highlighting their role in demand fluctuations. Additionally, the study evaluates the accuracy of these models, providing comparative insights that can aid in optimizing energy distribution and ensuring a reliable power supply.