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Title: Real-time anomaly detection for very short-term load forecasting

Abstract

Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.

Authors:
 [1]; ORCiD logo [2];  [3]
  1. Dongbei Univ. of Finance and Economics, Dalian (China)
  2. Univ. of North Carolina at Charlotte, Charlotte, NC (United States)
  3. Brookhaven National Lab. (BNL), Upton, NY (United States)
Publication Date:
Research Org.:
Brookhaven National Laboratory (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (OE-10); USDOE
OSTI Identifier:
1416032
Alternate Identifier(s):
OSTI ID: 1438436
Report Number(s):
BNL-203646-2018-JAAM
Journal ID: ISSN 2196-5625
Grant/Contract Number:
SC0012704; M616000124
Resource Type:
Journal Article: Published Article
Journal Name:
Journal of Modern Power Systems and Clean Energy
Additional Journal Information:
Journal Volume: 6; Journal Issue: 2; Journal ID: ISSN 2196-5625
Publisher:
Springer
Country of Publication:
United States
Language:
English
Subject:
24 POWER TRANSMISSION AND DISTRIBUTION; Real-time anomaly detection; Very short-term load forecasting; Multiple linear regression; Data cleansing

Citation Formats

Luo, Jian, Hong, Tao, and Yue, Meng. Real-time anomaly detection for very short-term load forecasting. United States: N. p., 2018. Web. doi:10.1007/s40565-017-0351-7.
Luo, Jian, Hong, Tao, & Yue, Meng. Real-time anomaly detection for very short-term load forecasting. United States. doi:10.1007/s40565-017-0351-7.
Luo, Jian, Hong, Tao, and Yue, Meng. Sat . "Real-time anomaly detection for very short-term load forecasting". United States. doi:10.1007/s40565-017-0351-7.
@article{osti_1416032,
title = {Real-time anomaly detection for very short-term load forecasting},
author = {Luo, Jian and Hong, Tao and Yue, Meng},
abstractNote = {Although the recent load information is critical to very short-term load forecasting (VSTLF), power companies often have difficulties in collecting the most recent load values accurately and timely for VSTLF applications. This paper tackles the problem of real-time anomaly detection in most recent load information used by VSTLF. This paper proposes a model-based anomaly detection method that consists of two components, a dynamic regression model and an adaptive anomaly threshold. The case study is developed using the data from ISO New England. This paper demonstrates that the proposed method significantly outperforms three other anomaly detection methods including two methods commonly used in the field and one state-of-the-art method used by a winning team of the Global Energy Forecasting Competition 2014. Lastly, a general anomaly detection framework is proposed for the future research.},
doi = {10.1007/s40565-017-0351-7},
journal = {Journal of Modern Power Systems and Clean Energy},
number = 2,
volume = 6,
place = {United States},
year = {Sat Jan 06 00:00:00 EST 2018},
month = {Sat Jan 06 00:00:00 EST 2018}
}

Journal Article:
Free Publicly Available Full Text
Publisher's Version of Record at 10.1007/s40565-017-0351-7

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