Robust Regression Models for Load Forecasting

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 L1regression model. Numerical experiments indicate the dominating performance of the three proposed robust regression models, especially L1regression, compared to other representative load forecasting models.