## 2 Overview of PVF Methods  
When evaluating the performance of PV power systems, it is crucial to consider unpredictability and variability, which are affected by meteorological conditions. These variables include solar irradiation, temperature, humidity, and wind speed. The precise forecasting of the power produced by a solar PV system over a specific timeframe is a critical engineering challenge. To accurately describe the PV forecasting problem, recent studies [3] can be classified by considering (i) the forecasting horizon, (ii) the forecast result, and (iii) the type of algorithm, as schematically displayed in Figure 1.  
First, different time horizons were considered for forecasting. The literature [13] classifies PV forecasts into four categories based on the forecast horizon, which is the period to forecast future PV power generation: ultra-short-term (minute scale), short-term (hour scale), medium-term (day scale), and long-term (month scale). Each category has various implications for scheduling PV plants. While long-term and medium-term forecasts are valuable for planning, short-term forecasts are indispensable for developing daily power consumption strategies and ensuring the dependable operation of PV power stations. Some studies have used meteorological data to calculate solar irradiance and predict the future PV power output based on PV power generation models, which are indirect prediction methods. A recent study [14] comprehensively reviewed the value of solar forecasting in the implementation of PVPF methods. Solar forecasting can be classified into two categories, deterministic and probabilistic, based on their uncertainty representations. Deterministic solar forecasting provides a single irradiance/PV power value for each forecast timestep. By contrast, probabilistic solar forecasting can also include information on the probability of occurrence and forecast distribution [15]. Finally, considering the type of algorithm, the most commonly used methods for implementing the PVPF method are as follows. * Physical models are deterministic closed-form solutions for PVPF. These models depend on data related to solar irradiance; therefore, historical power-generation data are not required. However, accurate predictions require the use of detailed parameters for the PV plants. In most cases, future solar irradiance is obtained from NWP [16]. Additional data, such as meteorological information including temperature, humidity, wind direction, PV plant capacity, and installation angle, may be used to optimize the results [17]. However, solar irradiance is the most crucial meteorological data and NWP is necessary for its effectiveness. Therefore, nearby weather stations are essential to PV performance models [18]. * Statistical methods, which involve extensive numerical pattern analysis based on statistical forecasting, require the acquisition of a historical dataset. Recently, the literature has demonstrated that statistical methods outperform physical ones [19]. Conventional linear statistical techniques are relatively simple and have a limited number of parameters, but are less capable of fitting complex curves [20]. Statistical techniques rely on historical time series and real-time data to identify patterns, and the forecast accuracy depends on the quality and dimensions of the available data. These methods are less costly than physical methods and are generally better for short-term forecasting. These models are useful tools for predicting linear data, but they are limited to nonlinear data. Consequently, they are often combined with artificial intelligence algorithms to improve accuracy [21]. * The use of artificial intelligence techniques, which incorporate advanced methods for acquiring knowledge and expertise, has led to precise outcomes and enhanced generalization capabilities. These approaches provide greater flexibility through dynamic learning procedures, thereby enabling real-time computation.