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http://dx.doi.org/10.9717/kmms.2021.24.9.1251

A Research for Imputation Method of Photovoltaic Power Missing Data to Apply Time Series Models  

Jeong, Ha-Young (TEF Co., ltd.)
Hong, Seok-Hoon (TEF Co., ltd.)
Jeon, Jae-Sung (TEF Co., ltd.)
Lim, Su-Chang (TEF Co., ltd.)
Kim, Jong-Chan (Dept of Computer Engineering, Sunchon National University)
Park, Chul-Young (TEF Co., ltd.)
Publication Information
Journal of Korea Multimedia Society / v.24, no.9, 2021 , pp. 1251-1260 More about this Journal
Abstract
This paper discusses missing data processing using simple moving average (SMA) and kalman filter. Also SMA and kalman predictive value are made a comparative study. Time series analysis is a generally method to deals with time series data in photovoltaic field. Photovoltaic system records data irregularly whenever the power value changes. Irregularly recorded data must be transferred into a consistent format to get accurate results. Missing data results from the process having same intervals. For the reason, it was imputed using SMA and kalman filter. The kalman filter has better performance to observed data than SMA. SMA graph is stepped line graph and kalman filter graph is a smoothing line graph. MAPE of SMA prediction is 0.00737%, MAPE of kalman prediction is 0.00078%. But time complexity of SMA is O(N) and time complexity of kalman filter is O(D2) about D-dimensional object. Accordingly we suggest that you pick the best way considering computational power.
Keywords
Missing Data; Imputation; Simple Moving Average; Kalman Filter; Photovoltaic Power;
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Times Cited By KSCI : 1  (Citation Analysis)
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