Missing Data Imputation of Solar Radiation Data under Different Atmospheric Conditions

Sensors

Concepción Crespo Turrado²  Mar໚ del Carmen Meizoso López¹  Fernando Sánchez Lasheras³  Benigno Antonio Rodríguez Gómez¹  José Luis Calvo Rollé¹ 

[1] Departamento de Ingeniería Industrial, University of A Coruña, A Coruña 15405, Spain; E-Mails:;Maintenance Department, University of Oviedo, San Francisco 3, Oviedo 3307, Spain; E-Mail:;Department of Construction and Manufacturing Engineering, University of Oviedo, Gijón 33204, Spain

关键词: missing data imputation;    multivariate imputation by chained equations (MICE);    multiple linear regression;    solar radiation;    pyranometer;   

DOI  :  10.3390/s141120382

来源: mdpi

【 摘 要 】

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.

【 授权许可】

CC BY   
© 2014 by the authors; licensee MDPI, Basel, Switzerland.