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Robust Regression Models for Load Forecasting
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DOI：10.1109/TSG.2018.2881562.png)
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Abstract
En 中文
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 L-1 regression model. Numerical experiments indicate the dominating performance of the three proposed robust regression models, especially L-1 regression, compared to other representative load forecasting models.
Keywords:
Cybersecurity
data integrity attack
electric load forecasting
iteratively re-weighted least squares
L-1 regression
robust regression
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