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

Jian LUO / Tao HONG / Meng YUE

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. Finally, a general anomaly detection framework is proposed for the future research.

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

Jian LUO / Tao HONG / Meng YUE

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. Finally, a general anomaly detection framework is proposed for the future research.

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

Jian LUO / Tao HONG / Meng YUE

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. Finally, a general anomaly detection framework is proposed for the future research.

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Title:

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

Contributors:

Jian LUO (author) / Tao HONG (author) / Meng YUE (author)

Published in:

Journal of Modern Power Systems and Clean Energy, Vol 6, Iss 2, Pp 235-243 (2018)

Publication date:

2018

ISSN:

2196-5625 , 2196-5420

DOI:

https://doi.org/10.1007/s40565-017-0351-7

Type of media:

Article (Journal)

Type of material:

Electronic Resource

Language:

Unknown

Keywords:

Real-time anomaly detection , Very short-term load forecasting , Multiple linear regression , Data cleansing , Production of electric energy or power. Powerplants. Central stations , TK1001-1841 , Renewable energy sources , TJ807-830

Metadata by DOAJ is licensed under CC BY-SA 1.0

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