@article{luo_hong_fang_2019, title={Robust Regression Models for Load Forecasting}, volume={10}, ISSN={1949-3053 1949-3061}, url={http://dx.doi.org/10.1109/TSG.2018.2881562}, DOI={10.1109/TSG.2018.2881562}, abstractNote={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.}, number={5}, journal={IEEE Transactions on Smart Grid}, publisher={Institute of Electrical and Electronics Engineers (IEEE)}, author={Luo, Jian and Hong, Tao and Fang, Shu-Cherng}, year={2019}, month={Sep}, pages={5397–5404} }
 @article{yan_bai_fang_luo_2018, title={A proximal quadratic surface support vector machine for semi-supervised binary classification}, volume={22}, ISSN={1432-7643 1433-7479}, url={http://dx.doi.org/10.1007/s00500-017-2751-z}, DOI={10.1007/s00500-017-2751-z}, number={20}, journal={Soft Computing}, publisher={Springer Science and Business Media LLC}, author={Yan, Xin and Bai, Yanqin and Fang, Shu-Cherng and Luo, Jian}, year={2018}, month={Oct}, pages={6905–6919} }