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<p>Efficient and accurate energy consumption modeling is fundamental for optimizing grid stability, forecasting demand and developing sustainable energy policies. This paper introduces the Shifted-Mode Lomax (SM Lomax) regression model, a novel statistical framework designed to address two key challenges in energy datasets: non-zero peaks reflecting baseline consumption and heavy-tailed distributions that capture extreme usage events. Traditional models, such as the Gamma and standard Lomax distributions, often fail to accommodate these features simultaneously due to the unrealistic zero-mode assumptions. Rooted in weighted distribution theory, the SM Lomax model inherently supports positive modes while providing enhanced control over tail behavior. Its regression framework connects location and shape parameters to covariates, enabling interpretable predictions of peak demand. Simulations confirm the asymptotic unbiasedness and consistency of the MLEs. Empirical validation using 105,140 smart meter readings from the Tanzania Electric Supply Company (TANESCO) demonstrates a 22.8% improvement in the Akaike Information Criterion (AIC) over benchmark models.</p>

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Enhancing energy consumption modeling with a Shifted-Mode Lomax regression for non-zero peaks

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年12月8日

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  • Edward Ngailo,
  • Diana Rwegasira,
  • Elimboto Yohana,
  • Noel Masasi

摘要

Efficient and accurate energy consumption modeling is fundamental for optimizing grid stability, forecasting demand and developing sustainable energy policies. This paper introduces the Shifted-Mode Lomax (SM Lomax) regression model, a novel statistical framework designed to address two key challenges in energy datasets: non-zero peaks reflecting baseline consumption and heavy-tailed distributions that capture extreme usage events. Traditional models, such as the Gamma and standard Lomax distributions, often fail to accommodate these features simultaneously due to the unrealistic zero-mode assumptions. Rooted in weighted distribution theory, the SM Lomax model inherently supports positive modes while providing enhanced control over tail behavior. Its regression framework connects location and shape parameters to covariates, enabling interpretable predictions of peak demand. Simulations confirm the asymptotic unbiasedness and consistency of the MLEs. Empirical validation using 105,140 smart meter readings from the Tanzania Electric Supply Company (TANESCO) demonstrates a 22.8% improvement in the Akaike Information Criterion (AIC) over benchmark models.