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

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:
Report Number(s):
BNL-203646-2018-JAAM
Journal ID: ISSN 2196-5625
Grant/Contract Number:
SC0012704; M616000124
Type:
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
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
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
OSTI Identifier:
1416032
Alternate Identifier(s):
OSTI ID: 1438436