Detection of Outliers in Time Series Power Data Based on Prediction Errors

Changzhi Li, Dandan Liu (), Mao Wang, Hanlin Wang and Shuai Xu
Additional contact information
Changzhi Li: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 185, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Dandan Liu: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 185, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Mao Wang: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 185, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Hanlin Wang: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 185, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China
Shuai Xu: College of Electronics and Information Engineering, Shanghai University of Electric Power, No. 185, Hucheng Ring Road, Pudong New Area District, Shanghai 201306, China

Energies, 2023, vol. 16, issue 2, 1-19

Abstract: The primary focus of smart grid power analysis is on power load forecasting and data anomaly detection. Efficient and accurate power load prediction and data anomaly detection enable energy companies to develop reasonable production and scheduling plans and reduce waste. Since traditional anomaly detection algorithms are typically for symmetrically distributed time series data, the distribution of energy consumption data features uncertainty. To this end, a time series outlier detection approach based on prediction errors is proposed in this paper, which starts by using an attention mechanism-based convolutional neural network (CNN)-gated recursive unit (GRU) method to obtain the residual between the measured value and its predicted value, and the residual data generally conform to a symmetric distribution. Subsequently, for these residual data, a random forest classification algorithm based on grid search optimization is used to identify outliers in the power consumption data. The model proposed in this paper is applied to both classical and real energy consumption datasets, and the performance is evaluated using different metrics. As shown in the results, the average accuracy of the model is improved by 25.2% and the average precision is improved by 17.2%, with an average recall improvement of 16.4% and an average F1 score improvement of 26.8% compared to the mainstream algorithms.

Keywords: neural network; forecast error; outlier detection; electricity consumption data (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://www.mdpi.com/1996-1073/16/2/582/pdf (application/pdf)
https://www.mdpi.com/1996-1073/16/2/582/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:16:y:2023:i:2:p:582-:d:1024541

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().