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Sensors 2014, 14(11), 20382-20399; doi:10.3390/s141120382

Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions

1
Maintenance Department, University of Oviedo, San Francisco 3, Oviedo 3307, Spain
2
Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain
3
Department of Construction and Manufacturing Engineering, University of Oviedo, Gijón 33204, Spain
4
Project Management Area, Mining Department, University of Oviedo, Oviedo 33004, Spain
*
Author to whom correspondence should be addressed.
Received: 7 September 2014 / Revised: 8 October 2014 / Accepted: 21 October 2014 / Published: 29 October 2014
(This article belongs to the Section Physical Sensors)
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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. View Full-Text
Keywords: missing data imputation; multivariate imputation by chained equations (MICE); multiple linear regression; solar radiation; pyranometer missing data imputation; multivariate imputation by chained equations (MICE); multiple linear regression; solar radiation; pyranometer
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. (CC BY 4.0).

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MDPI and ACS Style

Turrado, C.C.; López, M.C.M.; Lasheras, F.S.; Gómez, B.A.R.; Rollé, J.L.C.; Juez, F.J.C. Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions. Sensors 2014, 14, 20382-20399.

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