Solar energy utilization is rapidly growing all over the world. According to the International Renewable Energy Agency (IRENA), solar power generation capacity was 397 GW at the end of 2017. It took first place again with a capacity increase of 94 GW, accounting for a 32% increase—higher than the 10% wind power growth rate [
1]. In the United States, the electric power company PJM will supply 13% of its total load as renewable energy by 2031. In the case of solar power generation, installed capacity will be increased to 8.1 GW by 2027 [
2]. South Korea’s solar power generation capacity is 904.1 MW and its cumulative installation capacity is 4519.4 MW as of 2016, which is a small percentage compared with existing generators. As solar power generation capacity increases, many electric utilities are expected to have difficulty managing power system planning and operation. Meteorological variables that change over time and space mean that solar energy is highly intermittent and uncertain. Forecasting among various technologies can play an important role in preventing transmission congestion and maintaining a power balance, thus reducing the difficulty. Various forecasting techniques are being formulated abroad. UC San Diego uses a Total Sky Imager to predict the movement and location of clouds to forecast solar irradiation levels, determines the sky cover every 30 s, and estimates the position of clouds 5 min in advance [
3,
4]. San Antonio, Texas, U.S. uses satellites to forecast and assess solar irradiation for use in solar power systems and power system planning and integration [
5]. In addition, a review of recent forecasting methods as related to solar generation resources is shown in [
6,
7]. Reference [
6] accentuates the need for accurate forecasting of intermittent resources to achieve power gird balance. Various methods are currently being studied for the forecasting of solar energy resources, such as clear sky models, regressive methods, Artificial Intelligence (AI) techniques, remote sensing models, Numerical Weather Prediction (NWP), Local sensing, and Hybrid systems. In NWP-based forecast, reference [
8] used the Environment Canadas Global Environmental Multiscale NWP model to forecast hourly Global Horizontal Irradiance (GHI) and solar power for horizons out to 48 h. They applied spatial averaging and bias removal using a Kalman filter on the NWP forecasts to increase the predictions’ accuracy. Reference [
9] used NWP forecasts from the National Weather Service’s (NWS) database as exogenous inputs for Artificial Neural Networks (ANNs) to predict hourly GHI and Direct Normal Irradiance (DNI) out to 6 days ahead of time for Merced, California. In stochastic forecasts, reference [
10] constructed three autoregressive integrated moving average (ARIMA) forecasting models for next-hour GHI including cloud cover effects. The main difference in the three models tested concerns the inputs used: GHI in the first model, DNI and Diffuse Horizontal Irradiance (DHI) in the second model, and cloud cover (CC) in the third model. The authors used the third typical meteorological year (TMY3) data from the National Solar Radiation Data Base [
11] to estimate the ARIMA models and to validate the forecasting accuracy. In AI forecasting, reference [
12] used an ANN with exogenous variables to forecast the hourly solar power for a forecasting horizon of 12 h. This model shows an improvement in root-mean-square error (RMSE) of about 2.07%. Reference [
13] applied several stochastic and AI techniques (ARIMA,
k-Nearest Neighbor (
k-NN), ANN) to predict the one- and two-hour averaged power output of a 1 MW solar power plant in Merced, California. In hybrid forecasting, hybrid models have recently been used to improve forecast error by combining the benefits of forecasting models. Reference [
14] tested hybrid forecasting models that combine information from processed satellite images with ANNs.
Many solar forecasting methods use expensive and restricted equipment, such as satellite images and sky imagers, and complex equations. In addition, existing forecasting methods take a deterministic approach that represents a single value for a forecasting target. This has limited ability to express uncertainty in solar energy [
15,
16,
17,
18]. Some recent works have dealt with probabilistic forecasting for addressing uncertainty in solar energy. References [
19,
20] assess the performance of three probabilistic models for intra-day solar forecasting. The results demonstrated that the NWP exogenous inputs improve the quality of the intra-day probabilistic forecasts. Reference [
21] shows three different methods for ensemble probabilistic forecasting, derived from seven individual machine learning models, to generate 24-h-ahead solar power forecasts. The results have shown that the ensemble models offer even more accurate results than any individual machine learning model like ARIMA. GEFCOM represents a general framework of probabilistic forecasts for renewable energy generation [
22,
23]. This is demonstrated by an application in probabilistic solar power forecasting. The results from its evaluation show that the RMSE and quantile score are quite low, verifying the precision of the proposed forecasting method. This paper proposes a probabilistic approach for solar power forecasting using spatial interpolation and a naïve Bayes Classifier.
Section 2 describes the hybrid forecasting model of solar energy resources using kriging and naïve Bayes Classifier models. First, we show the spatial interpolation using what is called the kriging method. This method can spatially estimate the weather factor at different points of interest using a current weather value without historical data. Next, we propose a method for solar power forecasting using a naïve Bayes Classifier. This method can probabilistically forecast solar power in one-hour intervals.
Section 3 verifies the proposed method. We apply a hybrid spatio-temporal forecasting model that combines kriging and naïve Bayes Classifier based on empirical NWP data in South Korea. We also perform a comparison of the proposed model with a persistence model using normalized mean absolute error (NMAE) to validate the proposed hybrid forecasting model.