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Article

Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads

1
School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology (GIST), 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
2
School of Mechatronics, GIST, 123 Cheomdangwagi-ro, Buk-gu, Gwangju 61005, Korea
3
Honam Research Center, Electronics and Telecommunications Research Institute, Gwangju 61012, Korea
*
Author to whom correspondence should be addressed.
Energies 2021, 14(1), 165; https://doi.org/10.3390/en14010165
Received: 1 November 2020 / Revised: 21 December 2020 / Accepted: 25 December 2020 / Published: 30 December 2020
(This article belongs to the Special Issue Machine Learning for Energy Systems 2021)
Residential electricity load data can include numerous types of bad data, even clustered bad data, as they that are typically captured by simple measurement instruments. For example, in the case of a time-series of Not-a-Number (NaN) errors, the values before or next to a NaN may appear as the sum of actual values during the times of the NaN series. To utilize load data that includes such erroneous data for prediction or data mining analysis, customized detection and imputation should be conducted. This study proposes a new joint detection and imputation method for handling clustered bad data in residential electricity loads. Examples of these data are known invalid data points, such as consecutive NaN or zero values followed by or being ahead of an outlier. The proposed joint detection and imputation scheme first investigates the neighbors of the invalid data points, using probabilistic forecasting techniques. These techniques are implemented by the next valid neighbors to determine whether there is an anomaly or not. Then, adaptive imputations are applied on the basis of the detection, the candidate point should be imputed simultaneously or not. To assess the potential of the newly proposed scheme to characterize the clustered bad data, we analyzed the electricity loads of 354 households. Moreover, joint detection and imputations are conducted to test with the randomly injected synthesized clustered bad data (containing NaNs of various lengths) that is followed by the summation of the actual NaN values. The proposed scheme succeeded in detecting clustered bad data with an accuracy of 95.5% and a false alarm rate of 3.6% for all households in the dataset. Outlier detection-assisted imputation schemes are evaluated for NaNs with optional outliers. Results demonstrate that these schemes improve the overall accuracy significantly compared to schemes without outlier detection. View Full-Text
Keywords: bad data detection; probabilistic forecasting; residential electricity load bad data detection; probabilistic forecasting; residential electricity load
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MDPI and ACS Style

Park, S.; Yoon, S.; Lee, B.; Ko, S.; Hwang, E. Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads. Energies 2021, 14, 165. https://doi.org/10.3390/en14010165

AMA Style

Park S, Yoon S, Lee B, Ko S, Hwang E. Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads. Energies. 2021; 14(1):165. https://doi.org/10.3390/en14010165

Chicago/Turabian Style

Park, Soyeong, Seungwook Yoon, Byungtak Lee, Seokkap Ko, and Euiseok Hwang. 2021. "Probabilistic Forecasting Based Joint Detection and Imputation of Clustered Bad Data in Residential Electricity Loads" Energies 14, no. 1: 165. https://doi.org/10.3390/en14010165

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