Research on repairing anomalous electrical energy data based on the Grey Model

HUANG Fuxing; ZHOU Guangshan; ZHENG Kuanyun; FENG Zejia; YUAN Peisen

doi:10.3969/j.issn.1000-5641.201921016

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Jul. 2020

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Article Navigation > Journal of East China Normal University (Natural Sciences) > 2020 > (4): 156-163

HUANG Fuxing, ZHOU Guangshan, ZHENG Kuanyun, FENG Zejia, YUAN Peisen. Research on repairing anomalous electrical energy data based on the Grey Model[J]. Journal of East China Normal University (Natural Sciences), 2020, (4): 156-163. doi: 10.3969/j.issn.1000-5641.201921016

Citation:

HUANG Fuxing, ZHOU Guangshan, ZHENG Kuanyun, FENG Zejia, YUAN Peisen. Research on repairing anomalous electrical energy data based on the Grey Model[J]. Journal of East China Normal University (Natural Sciences), 2020, (4): 156-163. doi: 10.3969/j.issn.1000-5641.201921016

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Research on repairing anomalous electrical energy data based on the Grey Model

doi: 10.3969/j.issn.1000-5641.201921016

HUANG Fuxing^{1, 2},
ZHOU Guangshan^{1, 2},
ZHENG Kuanyun^{1, 2},
FENG Zejia³,
YUAN Peisen^{3
,
,}

1.
NARI Group Corporation (State Grid Electric Power Research Institute), Nanjing　211106, China
2.
NARI Technology Co., Ltd, Nanjing　211106, China
3.
College of Information Science and Technology, Nanjing Agricultural University, Nanjing　210095, China

Received Date: 2019-08-26
Available Online: 2020-07-20
Publish Date: 2020-07-20

Abstract

Abstract

The traditional technique of repairing anomalous electrical energy data requires large amounts of data, has a high operational cost, and results in poor timeliness by using interpolation and other statistical methods; hence, the accuracy and efficiency of repairing results are limited. In this paper, a method for repairing anomalous electrical energy data based on the Grey Model is proposed. The normal historical electrical energy data is taken as an input variable, and the time node electrical energy data at which the abnormal point is located is taken as the output variable. The ratio test and the prediction equation are used to obtain the predicted value. The electrical energy data is iteratively predicted. Finally, the accuracy of the predicted value is tested. The average relative residual of the prediction was found to be 2.182%. The original data is then modified according to the result so as to repair the electrical energy anomaly data. The model prediction and repair are carried out with the actual electrical energy data of a certain area, and the results and errors are analyzed. The feasibility of the method is subsequently verified.
- electrical energy,
- anomaly data,
- Grey Model,
- data repair

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References(30)

References

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