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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:
; ORCiD logo;
Publication Date:
Research Org.:
Brookhaven National Lab. (BNL), Upton, NY (United States)
Sponsoring Org.:
USDOE; USDOE Office of Electricity Delivery and Energy Reliability (OE), Power Systems Engineering Research and Development (OE-10)
OSTI Identifier:
1416032
Alternate Identifier(s):
OSTI ID: 1438436
Report Number(s):
BNL-203646-2018-JAAM
Journal ID: ISSN 2196-5625; PII: 351
Grant/Contract Number:  
M616000124; SC0012704
Resource Type:
Published Article
Journal Name:
Journal of Modern Power Systems and Clean Energy
Additional Journal Information:
Journal Name: Journal of Modern Power Systems and Clean Energy Journal Volume: 6 Journal Issue: 2; Journal ID: ISSN 2196-5625
Publisher:
Springer Science + Business Media
Country of Publication:
Germany
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. Germany: 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. Germany. 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". Germany. 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 = {Germany},
year = {2018},
month = {1}
}

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

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