Hyuk-Rok Kwon’s scientific contributions


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Publications (3)


Figure 1. Example of electricity consumption data with missing values.
Figure 2. Calculation method of linear interpolation.
Figure 4. Power consumption data: abnormal data (non-stationary).
Figure 5. Comparison of linear interpolation results and real data.
Figure 6. Comparison between the real data and the results obtained from the past-similar-situation substitution method.

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A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System
  • Article
  • Full-text available

August 2021

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132 Reads

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3 Citations

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Hyuk-Rok Kwon

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Pan-Koo Kim

With the expansion of advanced metering infrastructure (AMI) installations, various additional services using AMI data have emerged. However, some data is lost in the communication process of data collection. Hence, to address this challenge, the estimation of the missing data is required. To estimate the missing values in the time-series data generated from smart meters, we investigated four methods, ranging from a conventional method to an estimation method applying long short-term memory (LSTM), which exhibits excellent performance in the time-series field, and provided the performance comparison data. Furthermore, because power usages represent estimates of data that are missing some values in the middle, rather than regular time-series estimation data, the simple estimation may lead to an error where the estimated accumulated power usage in the missing data is larger than the real accumulated power usage appearing in the data after the end of the missing data interval. Therefore, this study proposes a hybrid method that combines the advantages of the linear interpolation method and the LSTM estimation-based compensation method, rather than those of conventional methods adopted in the time-series field. The performance of the proposed method is more stable and better than that of other methods.

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Citations (3)


... Therefore, although this method may be effective in certain datasets that have cyclic patterns, it is not suitable for datasets that have multiple types of power consumption patterns. Experiments are conducted on compensation methods based on ARIMA and long short-term memory (LSTM) estimations, which are compensation methods based on time-series estimation, in addition to these two conventional methods [29]. 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; subsequently, we perform a comparative analysis. ...

Reference:

A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System
Method of estimation of missing data in AMI system
  • Citing Conference Paper
  • September 2020

... An LSTM network is based on the ordinary recurrent neural network (RNN) [44], which controls the transmission of information within the network by gating the state and retains the important temporal state in each pass-through step in a long-time memory manner to achieve better prediction results. An LSTM model is generally applicable to various types of continuous temporal prediction models, such as traffic flow prediction [45], network attacks prediction [46], and missing data compensation [47]. Considering the possible discontinuity of DNS data, an additional LSTM variant, time-aware LSTM (T-LSTM) [48], was chosen for comparison in this paper. ...

A Missing Data Compensation Method Using LSTM Estimates and Weights in AMI System

Information

... The AMI operating system enables the convergence of various services such as remote meter reading, demand management, power consumption reduction, and power quality improvement based on a bi-directional communication between consumers and power companies [3]. The Table 1 is shows, Starting with the first phase of the AMI construction project for 2 million households in 2013, with a goal of completing the construction for a total of 22.5 million households by 2020, according to the new energy industry acceleration policy, the Korea Electric Power Corporation (KEPCO) completed the construction of AMI for approximately 6.8 million households by 2018 and 400 households in 2019, thereby handling AMI operations for approximately 10 million households [4]. However, it has become difficult to construct the AMI for all 22.5 million households by 2020, as originally planned. ...

Estimate method of missing data using Similarity in AMI system
  • Citing Article
  • December 2019