# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 4 Results and Discussion
### Comparison of Methods and Discussion  
PV prediction simulations were conducted for the case wherein the missing data imputation methods were applied to the training data and test data separately; the results for each of these cases have been presented in Sections 4.1.1 and 4.1.2, respectively. When the missing data imputation method is applied to the training data, the RMSD is at most 5 kW and the MRD is less than 2%. That is, in this case, a large error does not occur when training is performed with data having no missing values. The reason underlying this result is as follows: first, the values for predicted power generation in this study correspond to the power generation values measured in 2017. However, this is not directly affected by the training data, which had missing values corresponding to 2016. Here, we note that the weather and PV generation data are time dependent data (i.e., time series data). The predicted value for PV generation in 2017 was determined by the 2017 training data alone. Accordingly, the 2016 weather data were only used to build the prediction model and did not directly impact the data generated for 2017. Second, even if the missing data rate is at most 20%, the training model is more influenced by the remaining 80% training data, and not the 20% missing data. Therefore, although missing data occur in the training data, there is no noticeably large error. On the other hand, there is a noticeable difference between when the missing data imputation method is applied to the test data and when this method is not applied. Because the test data correspond to 2017, the predicted values for 2017 are directly affected. For example, the PV power generation prediction for 20 January 2017 was determined using meteorological data, such as solar radiation, temperature, and precipitation, as well as predicted time of day. Since the independent to dependent variable relationship is established between data of the same time, the value for the predicted PV power generation changes as values for the weather data are changed. From Tables 5-7, it can be observed that the error between the reference model and predicted model with missing data imputation increases with increasing missing data rates. When the missing data rate is low, LI affords the least error. However, for high missing data rates, the KNN method yields the smallest error. As the missing data rate increases, the amount of adjoining missing values increases in the datasets. LI is suitable for missing value imputations at a specific point in time, but it is not accurate for missing values in successive sections. Thus, when the missing data rate is 20%, the error of LI rapidly increases. MI yields the largest error value regardless of the missing data rate and test duration. This method is suitable for discrete data; however, weather data are continuous data. In conclusion, these simulations indicate that the KNN method is the most suitable missing data imputation method for day-ahead PV power forecasting. ensure increased accuracy, it is required to compensate for missing meteorological data values suitably. In this study, we studied four methods to fill in missing values in PV power generation prediction: LI, MI, the statistical missing data imputation method MICE, and the machine learning-based KNN. In order to test these methods, we chose the SVR model as the solar power prediction model and applied the four methods to the SVR model. We used the indices of RMSD (kW) and MRD (%) to calculate the degree of difference between the predicted value with and without the application of missing data imputation. Four different predictions were obtained for each missing data imputation method. We next obtained the error for each method over five trials to evaluate the performance of each missing data imputation method. When the missing data imputation is applied to PV power forecasting, the missing training data do not affect PV power prediction. However, missing test data considerably affect PV power prediction.