Work

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

Year: 2025

Type: article

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.

Source: Journal of Electrical Systems and Information Technology

DOI: 10.1186/s43067-025-00298-7

Authors: Edward Ngailo, Diana Rwegasira, Elimboto Yohana, Noel Masasi

Institutions: Linköping University, University of Dar es Salaam

Language: English

Topic: Statistical Distribution Estimation and Applications

Subfield: Statistics and Probability

Field: Mathematics

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure, Climate action

Open Access status: diamond

Funders: Styrelsen för Internationellt Utvecklingssamarbete, University of Dar es Salaam, Linköpings Universitet