Name
Abstract | ||
|---|---|---|
Electric load forecasting has been extensively studied during the past century. While many models and their variants have been proposed and tested in the 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 L₁ regression model. Numerical experiments indicate the dominating performance of the three proposed robust regression models, especially L₁ regression, compared to other representative load forecasting models. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1109/tsg.2018.2881562 | IEEE Transactions on Smart Grid |
Keywords | Field | DocType |
Load modeling,Load forecasting,Biological system modeling,Predictive models,Data models,Data integrity | Least squares,Data modeling,Mathematical optimization,Regression,Electrical load,Regression analysis,Robust regression,Control engineering,Load forecasting,Data integrity,Engineering | Journal |
Volume | Issue | ISSN |
10 | 5 | 1949-3053 |
Citations | PageRank | References |
0 | 0.34 | 0 |
Authors | ||
3 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Jian Luo | 1 | 14 | 3.97 |
| tao hong | 2 | 6 | 1.79 |
| Shu-Cherng Fang | 3 | 1153 | 95.41 |