Missing Data Imputation for Solar Radiatıon by Deep Neural Network

Abstract

The quality of observations is fundamental issue in natural sciences. Here, the accurate and complete data is required to accomplish satisfactory estimations. There are several factors impairing the quality of measurements, such as a broken or mis-calibrated device and error in reading the measurements. Thus, this study primarily aims the imputation of the missing values in measurement of solar radiation data. Deep Neural Network (DNN) method was used to handle the missing data, and benchmarked with the classical approaches, i.e., Mean Imputation (MI), which one of the most frequently adopted data imputation method in the pertinent literature, the Linear Interpolation (LI) and Spline Interpolation (SI). The overall results highlighted that the DNN method outperformed its counterparts in terms of missing value handling through providing a greater accuracy according to the various performance metrics compared to the classical methods. It is believed that the proposed approach could make valuable contribution to the body of knowledge as well as providing significant overview to the interested researchers by filling the important gap exists in the pertinent literature.