# Analysis and Impact Evaluation of Missing Data Imputation in Day-ahead PV Generation Forecasting
## 2 Forecasting Model and Missing Data Imputation Methodology  
### Data and Prediction Model  
The solar power generation data are based on the PV dataset provided by the Korea Southern Power Co., Ltd. (KOSPO). The dataset can be downloaded in the CSV, JSON, etc., file formats from the Korea Open Data Portal [25]. The time step of the data is 1 h, and it covers the period from 1 January 2015-19 September 2017. In this study, the data from 1 January-31 December 2016 are used as the PV power prediction model training data, and data from 1 January-19 September 2017 are used as test data. The solar power plant of interest is located at 55, Hwasunhaean-ro 106 been-gil, Andeok-myeon, Seogwipo-si, Jeju-do, Republic of Korea (around latitude 33.24\({}^{\circ}\) and longitude 126.34\({}^{\circ}\)). The peak output PV power of the plant is 196 kW. The meteorological hourly data can be downloaded by use of the open weather data portal provided by the Korea Meteorological Administration [26]. In the study, we used the local live forecast data of Jeju Island. The weather conditions included temperature, relative humidity, wind speed, wind direction, precipitation, and sky condition. Here, the sky condition expresses the amount of cloud cover as a numerical value in the range from 1-4, where 1 indicates the least cloud cover and 4 the highest cloud cover. As in the case of 1-h time-step power generation data, 1-h time-step weather data of 2016 were used to train the prediction model, and 1-h time-step weather data for 2017 were used for the tests. Time and month values were used in the model to capture the time and seasonal characteristics not reflected by weather variables. Here, we mention that the weather data factor most closely related to solar power generation is solar irradiance. However, meteorological data portals do not provide the predicted solar irradiance data. Therefore, we used the solar irradiance data from the Python Library (PVLIB) [27]. The library was utilized as follows: First, we defined the time zone of solar radiation data to be obtained. We next calculated the zenith and azimuth angles with time by inputting time data into the get_solarposition function of PVLIB with the time zone. Next, we used the get_clearsky function of PVLIB to calculate various solar radiation data such as global horizontal irradiation (GHI), direct normal irradiation (DNI), and diffuse horizontal irradiation (DHI). A detailed description cab be found in [28]. We note here that it is possible to compute the solar irradiation amount per hour at the PV panel location on sunny days by combining the three solar irradiation datasets, the Sun's zenith angle, azimuth, latitude, longitude, time zone, tilt of the solar panel, and azimuth information of the surface. The azimuth information corresponds to the plane of array irradiance components on a tilted surface. This value does not reflect weather conditions. Even though it is not considered in solar radiation, we estimate that this factor can be predictable even on days with cloudy weather, because the prediction model is learned by using the predicted amount of precipitation and cloud amount data. The relationship between the input and output data is shown in Figure 2. The meteorological data and the power generation data are 1-h time-step data, and the meteorological data predicted at 23:00 the day before are used for the PV generation prediction model for the next day. The PV forecasting is made on day N, and the forecasting model is used to calculate the hourly power generation of day N + 1. The time horizon depends on the time of the forecasting time, and it ranges from 1 h to a maximum of 25 h. For example, when the amount of power generated at 01:00 is predicted, the forecast is made at 23:00 on the day before, i.e., the forecast is made two hours ahead. In contrast, to estimate the power generation at 22:00, the forecast is made at 23:00 on the previous day. Here, we assume that the prediction of the next day's power is made simultaneously with the release of the forecast data.