Abstract
With the rapid development of power grids, data fusion between systems has become a trend. Aiming at the problem of missing data that often occurs in system fusion, this paper proposes an improved random forest algorithm based on attribute comprehensive weighting IRFNNIS (Improved Random Forest-Assisted Nearest Neighbor Interpolation Strategy)’s method of identifying missing grid data. Based on the error expectation, this method firstly proposes an attribute comprehensive weighting strategy based on the error expectation, performs comprehensive attribute calculation on the initial missing set, and generates a complete set of examples; secondly, obtains the similar set according to the attribute comprehensive weighting strategy, and trains the random forest model; Finally, an improved random forest algorithm based on attribute comprehensive weighting can identify missing data and improve the identification accuracy of missing data. The calculation example uses real power grid data to analyze and the results show the feasibility and effectiveness of the method proposed in this paper.
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Wang, Y., Jin, Y., Zhu, Y., Li, X., Li, D. (2021). Improved Random Forest Algorithm Based on Attribute Comprehensive Weighting Used in Identification of Missing Data in Power Grid. In: Tian, Y., Ma, T., Khan, M.K. (eds) Big Data and Security. ICBDS 2020. Communications in Computer and Information Science, vol 1415. Springer, Singapore. https://doi.org/10.1007/978-981-16-3150-4_49
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DOI: https://doi.org/10.1007/978-981-16-3150-4_49
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