Work

SP-RF-ARIMA: A sparse random forest and ARIMA hybrid model for electric load forecasting

Year: 2025

Type: article

Abstract: Accurate Electric Load Forecasting (ELF) is crucial for optimizing production capacity, improving operational efficiency, and managing energy resources effectively. Moreover, precise ELF contributes to a smaller environmental footprint by reducing the risks of disruption, downtime, and waste. However, with increasingly complex energy consumption patterns driven by renewable energy integration and changing consumer behaviors, no single approach has emerged as universally effective. In response, this research presents a hybrid modeling framework that combines the strengths of Random Forest (RF) and Autoregressive Integrated Moving Average (ARIMA) models, enhanced with advanced feature selection— Minimum Redundancy Maximum Relevancy and Maximum Synergy (MRMRMS) method—to produce a sparse model. Additionally, the residual patterns are analyzed to enhance forecast accuracy. High-resolution weather data from Weather Underground and historical energy consumption data from PJM for Duke Energy Ohio and Kentucky (DEO&K) are used in this application. This methodology, termed SP-RF-ARIMA, is evaluated against existing approaches; it demonstrates more than 40% reduction in mean absolute error and root mean square error compared to the second-best method.

Source: Global Energy Interconnection

DOI: 10.1016/j.gloei.2025.04.003

Authors: Kamran Hassanpouri Baesmat, Farhad Shokoohi, Zeinab Farrokhi

Institution: University of Nevada, Las Vegas

Language: English

Topic: Energy Load and Power Forecasting

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

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

Open Access status: diamond

Funder: University of Nevada, Las Vegas