## 1 Introduction
To reduce the uncertainty of solar power caused by different weather patterns, studies that combine weather research and forecasting models have increased gradually in recent years [38; 39; 40]. In these related studies, weather classification is conducted as a pre-processing step for short-term solar forecasting to achieve better prediction accuracy than the same methods using a single simple uniform model for all weather conditions [41; 42; 43]. According to the existing achievements in solar forecasting studies, weather status pattern recognition and classification approaches have proven to be an effective way to increase the accuracy of forecasting results, especially for day-ahead forecasting. As each site or region experiences different weather patterns at different times, multiple forecasting models fit for different weather conditions can be more precise and efficient than using only one single uniform model. For research about weather status pattern recognition, NWP model outputs are an important data source [44; 45]. However, for instance, most PV power plants in China are located in relatively remote regions and often lack precise and detailed NWP data, or the corresponding observational data that is known to increase NWP accurately in the local region. Therefore, to realize a classified DAST solar PV power forecasting system in different weather conditions, a pattern recognition algorithm based only on the historical data of the PV power plant is generally necessary. In the previous research [8], we presented a pattern recognition model for weather statuses based on solar irradiance feature extraction using one-year historical data from a PV power plant. A variety of regional information and seasonal factors can influence the weather condition at an individual PV plant. Meanwhile, a comprehensive historical data collection process imposes a time penalty of several years, during which time the PV plant is operating with insufficient meteorological data being provided to the forecasting algorithms. Thus, a major problem for newly built PV plants to produce a weather classification is the general lack of historical data and particularly the acute lack of data in rare weather circumstances. As the plant operates for more time, the quantity of recorded data for the PV plant is increasing, and the distribution of data in different weather situations categories may change as well. It is almost impossible for a single classification model to offer accurate weather status recognition results facing these problems [46]. Therefore, research on the performance of different classifiers in a variety of situations is warranted. This paper investigates and assesses the performance of two commonly used classification methods, _K_-nearest neighbors (KNN) and support vector machines (SVM), for different scenarios of sample scale. The main contributions are to clarify the influence of different classifiers and training data scale on weather classification for DAST solar PV power forecasting, and then figure out a relatively optimal solution of weather classification modeling for DAST solar PV power forecasting under different dataset conditions. The rest of this paper is organized as follows. In Section 2, the mathematical description of data classification is presented and two primary classification methods, KNN and SVM, are introduced. Section 3 provides a statement of weather type classification problems for DAST solar PV power forecasting. Section 4 presents a case study using actual data from a grid-connected solar PV plant in China. Modeling and simulation of collected meteorological data for weather classification are proposed. The results are analyzed and discussed in Section 5. Section 6 summarizes the main contributions of the manuscript.