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

Le résumé fourni par la source

Abstract 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.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.