Abstract
In recent years, power grid data missing which caused by manual operation error and equipment failure often occurs, bringing difficulties to power grid big data analysis. In order to solve the problem that the accuracy of grid data filling is insufficient, a method of grid missing data filling based on historical data mining assisted multi-dimensional scenario analysis is proposed. Firstly, the highly correlated attribute data are selected as the reference basis for missing attribute data filling through the Fluctuation cross-correlation Analysis (FCCA), and the correlation degree is further quantified by combining weights. Secondly, based on load scenario analysis, the similarity between data sources is measured by Dynamic Time Warping (DTW). Finally, combined with DTW and combined weight, the date which has the most similar data was found, and use the same time period data of it to fill the missing data. The simulation results show that the proposed data filling method has higher accuracy.
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Chen, G., Zhu, Z., Yang, L., Lin, G., Yun, Y., Jiang, P. (2022). Power Grid Missing Data Filling Method Based on Historical Data Mining Assisted Multi-dimensional Scenario Analysis. In: Tian, Y., Ma, T., Khan, M.K., Sheng, V.S., Pan, Z. (eds) Big Data and Security. ICBDS 2021. Communications in Computer and Information Science, vol 1563. Springer, Singapore. https://doi.org/10.1007/978-981-19-0852-1_27
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DOI: https://doi.org/10.1007/978-981-19-0852-1_27
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