.png){kind=icon}
返回
Robust Regression Models for Load Forecasting
.png){kind=icon}
OA
AI
DOI：10.1109/TSG.2018.2881562.png)
.png){kind=icon}
.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
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.
Keyword:
Cybersecurity
data integrity attack
electric load forecasting
iteratively re-weighted least squares
L-1 regression
robust regression
AI总结
对已上传原文的论文进行重点信息的提取，主要内容包括：简要概述、研究摘要、背景介绍、关键亮点、图文解析、展望与总结。
期刊
IF:
9.8
论文数:
5.7K
被引数:
4.3W
机构
引用论文
Improving short term load forecast accuracy via combining sister forecasts通过组合姐妹预测提高短期负荷预测准确性.png)
ENERGYOAAI
ENERGYIF9.4
OAAI分享收藏Additive models and robust aggregation for GEFCom2014 probabilistic electric load and electricity price forecastingGEFCom2014概率电力负荷和电价预测的加性模型和鲁棒聚合
PREAI
分享收藏分享收藏分享收藏
查看更多内容