# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 1 Introduction
Here, NRMSE is the value obtained by dividing the RMSE value by the difference between the maximum and the minimum values of measured irradiation data. On the other hand, the second method yields higher error than the first method. In addition to research on solar radiation, several research works have focused on meteorological factors [17; 18; 19; 20]. In [17], the authors demonstrated the drawbacks of the IDW method and proposed three other methods: the coefficient correlation weighing method (CCWM), the artificial neural network estimation method (ANNEM), and the kriging estimation method (KEM),  
Figure 1: Global solar photovoltaic capacity and annual additions from 2007–2017. which outperformed conventional methods. Meanwhile, another study [18] has suggested the fixed functional set genetic algorithm method (FFSGAM), which utilizes genetic algorithms and nonlinear optimization methods to estimate the missing data. When compared with IDW, the FFSGAM yields greater prediction accuracy. Here, we note that only six rain-gauging stations in Korea are able to estimate missing data; thus, missing data imputation becomes extremely important in this context. The method proposed in [19] is similar to that in [17] in that both are based on hybrid models. The former approach utilizes an artificial neural network (ANN) and a regression tree (RT) and affords better prediction accuracy than the ANN or RT models alone. Meanwhile, in [20], the authors demonstrated 17 deterministic missing data imputations for missing precipitation data and concluded that the most suitable method is multiple linear regression weighted by the square of the missing data ratio. Several studies have demonstrated improved prediction performance via application of these methods to classification or prediction models [21; 22]. In [21], the authors demonstrated a neural network model of breast cancer diagnosis with the use of three statistical methods and three missing data machine learning methods. In [22], the authors demonstrated that the conventional road traffic congestion prediction performance can be improved via application of a missing data imputation method based on machine learning techniques. However, very few studies have applied missing weather data imputation to PV generation forecasts. Although in [23], the authors developed a PV forecasting model using missing data imputation methods, they only considered missing PV power data imputation, and not missing weather data imputation. Missing data imputation forms a significant data preprocessing issue in predicting solar power generation because precipitation data are generally inaccurate. For example, as per the Korea Meteorological Administration, the approximate missing data rate was 16% in 2015 and 2016 and 19% in 2017. In particular, it is difficult to estimate how rainfall must be considered because a large amount of precipitation data is missing. However, very few researches have focused on how solar power generation forecasts change when the missing-point replacement method is applied. Against this backdrop, here, we generate randomly missing data in the weather data of the period of 2016-2017 with three missing data ratios, corresponding to the missing completely at random (MCAR) process [24]. Next, we replace the missing data with suitable values by using four different approaches, linear interpolation (LI), mode imputation (MI), \(k\)-nearest neighbors (KNN), and multivariate imputation by chain equations (MICE). Thirdly, for four cases that utilize the missing data imputation methods and one that does not do so, we construct forecasting models using the SVR method with the solar power generation data for 2016. Finally, we predict solar PV generation for 2017 with the forecasting model and missing value-corrected weather data of 2017. Further, we calculate and compare the forecast errors for a specific period in 2017, and we examine the accuracy resulting from the application of the missing data imputation methods.