Sliding window-based LightGBM model for electric load forecasting using anomaly repair

Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can l...

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Bibliographic Details
Published in:The Journal of supercomputing 2021-11, Vol.77 (11), p.12857-12878
Main Authors: Park, Sungwoo, Jung, Seungmin, Jung, Seungwon, Rho, Seungmin, Hwang, Eenjun
Format: Article
Language:English
Subjects:
Abnormalities
Anomalies
Artificial Intelligence for Smart Cities
Compilers
Computer Science
Electric power systems
Electrical loads
Energy storage
Forecasting
Interpreters
Mathematical models
Processor Architectures
Programming Languages
Repair
Sliding
Smart grid
Statistical methods
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Summary:Smart grids have attracted much attention recently for their potential to reduce power system operating and management costs. Smart grid core components include energy storage, renewable energy source(s), and smart meters. Smart meters collect diverse data regarding smart grid operation, which can lead to inefficient operation if the meter data are damaged or tampered with during collection or transmission. Therefore, it is important to identify abnormalities in smart grid data and process them accordingly. Various anomaly detection models have been proposed using statistical methods, but they cannot detect some anomaly patterns accurately, and the models generally did not consider repair strategies for the detected anomalies. Anomaly repair should be included with model training to improve forecasting performance. This paper proposes a robust sliding window-based LightGBM model for short-term load forecasting using anomaly detection and repair. We first show how to detect anomalies using a variational autoencoder and then how they can be repaired using a random forest method. Finally, we verify that the proposed sliding window-based LightGBM achieves superior forecasting performance in combination with anomaly detection and repair.
ISSN:0920-8542
1573-0484
DOI:10.1007/s11227-021-03787-4