2019 journal article
Robust Regression Models for Load Forecasting
IEEE TRANSACTIONS ON SMART GRID, 10(5), 5397–5404.
author keywords: Cybersecurity; data integrity attack; electric load forecasting; iteratively re-weighted least squares; L-1 regression; robust regression
TL;DR:
Numerical experiments indicate the dominating performance of the three proposed robust regression models, especially <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex- math></inline- formula> regression, compared to other representative load forecasting models.
(via Semantic Scholar)
UN Sustainable Development Goal Categories
7. Affordable and Clean Energy
(Web of Science)
Source: Web Of Science
Added: September 16, 2019
Electric load forecasting has been extensively studied during the past century. While many models and their variants have been proposed and tested in load forecasting literature, most of the existing case studies have been conducted using the data collected under normal operating conditions. A recent case study shows that four representative load forecasting models easily fail under data integrity attacks. To address this challenge, we propose three robust load forecasting models including two variants of the iteratively re-weighted least squares regression models and an <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regression model. Numerical experiments indicate the dominating performance of the three proposed robust regression models, especially <inline-formula> <tex-math notation="LaTeX">$L_{1}$ </tex-math></inline-formula> regression, compared to other representative load forecasting models.