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This study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method.
Nov 4, 2021 ， In order to estimate for missing values of time series data measured from smart meters, a total of four methods were experimented and the performance ...
Missing: Long Memory
In this paper, a stage-wise missing value treatment approach involving particle swarm optimization (PSO) comprising six stages has been proposed
In this research, we study a hybrid method that combines the advantages of the linear interpolation method and those of the LSTM estimation-based compensation ...
Method of estimation of missing data in AMI system | Semantic Scholar
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In order to estimate for missing values of time series data measured from smart meters, a total of four methods were experimented and the performance ...
Dec 1, 2023 ， This paper investigates the handling of missing values in demand data, and a new approach is developed for improving the performance of demand analytics.
Jul 2, 2021 ， By using LSTM networks in the autoencoder, it allows the autoencoder to specialize in analyzing sequential data like timeseries. 5.2 Datasets.
Jan 11, 2022 ， This paper presents a novel, sequentially executed supervised machine learning-based electric theft detection framework using a Jaya-optimized combined Kernel ...
Jun 22, 2021 ， We develop a novel technique to perform spatiotemporal missing data imputation. Using the power grid topology and timeseries data obtained from the metering ...
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In real life, the demand data are usually affected by the missing data problem. This study proposes an analysis of the role of missing data imputation in the ...