Missing data imputation of solar radiation data under different atmospheric conditions
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Missing data imputation of solar radiation data under different atmospheric conditionsAutor(es)
Data
2014-10-29Cita bibliográfica
TURRADO, C. C., et al. 2014. Missing Data Imputation of Solar Radiation Data Under Different Atmospheric Conditions. Sensors (Switzerland). 14(11), pp 20382-20399
Resumo
[Abstract] Global solar broadband irradiance on a planar surface is measured at weather stations by pyranometers. In the case of the present research, solar radiation values from nine meteorological stations of the MeteoGalicia real-time observational network, captured and stored every ten minutes, are considered. In this kind of record, the lack of data and/or the presence of wrong values adversely affects any time series study. Consequently, when this occurs, a data imputation process must be performed in order to replace missing data with estimated values. This paper aims to evaluate the multivariate imputation of ten-minute scale data by means of the chained equations method (MICE). This method allows the network itself to impute the missing or wrong data of a solar radiation sensor, by using either all or just a group of the measurements of the remaining sensors. Very good results have been obtained with the MICE method in comparison with other methods employed in this field such as Inverse Distance Weighting (IDW) and Multiple Linear Regression (MLR). The average RMSE value of the predictions for the MICE algorithm was 13.37% while that for the MLR it was 28.19%, and 31.68% for the IDW.
Palabras chave
Missing data imputation
Multivariate imputation by chained equations (mice)
Multiple linear regression
Solar radiation
Pyranometer
Multivariate imputation by chained equations (mice)
Multiple linear regression
Solar radiation
Pyranometer
Versión do editor
Dereitos
Reconocimiento 4.0 Internacional
ISSN
1428-8220