## 1 Introduction
They are not universal, cannot be adapted to changing meteorological conditions, and can only be used for short-term forecasting. Statistical forecasting methods include time series methods [10], regression analysis [11], gray theory [12], fuzzy theory [13], multi-source data-driven methods [14], and spatio-temporal correlation [15]. Statistical methods include curve fitting, parameter estimation, and correlation analysis of historical data such as solar radiation and PV power generation. Predicting future PV power generation is achieved by establishing a correlation mapping relationship between input and output data. Compared to physical methods, there is no need to have a clear and complete understanding of the complex photovoltaic conversion relationships of PV systems [16]. Therefore, it is characterized by the simplicity of modeling and generalizability across different regions compared to physical methods. However, the premise of the implementation of statistical methods is that a large amount of correct historical data needs to be processed, and there are difficulties in data collection and calculation during the implementation process. Due to the need for a large number of numerical calculations in the forecasting process, the general computer forecasting time is long. Meeting the requirements of forecasting speed (especially at the minute level) is difficult for ultra-short-term PV power forecasting. In addition, the prediction quality of statistical methods is closely related to the data quality and the degree of historical data retention. Therefore, screening and elimination of spurious data have a significant impact on forecast accuracy. Prediction accuracy relies on high-dimensional computation for effectiveness, which increases the computational effort and reduces the prediction speed. Machine learning can efficiently extract high-dimensional complex nonlinear features and map them directly to the output. Taking advantage of this, machine learning-based forecasting methods have become one of the most widely used methods for predicting different time series [17]. In recent years, deep learning models represented artificial neural networks (ANN), recurrent neural networks (RNN), gated recurrent unit neural networks (GRU), and so on, which have been widely used in the field of PV power prediction [18; 19]. The LSSVM model has proven to be an effective advanced prediction model, which has been increasingly used in solving prediction problems to minimize the sum-of-squares error and marginal error of the training data [20; 21]. A previous study [22] proposed a hybrid prediction model combining variable mode decomposition (VMD), particle swarm optimization (PSO), and least squares support vector regression (LSSVM), which improves the accuracy of day-ahead prediction by at least 19% compared to the best RecMO strategy. A previous study [23] developed an evolutionary seasonal decomposition LSSVM model to predict the PV power generation in the current month while using a genetic algorithm to optimize the parameters of the LSSVM, and the results showed that the prediction system has better prediction accuracy. Another study [24] used a hybrid prediction model combining wavelet transform, particle swarm optimization, and support vector machine for day-ahead power generation prediction of real microgrid PV systems with a daily average MAPE of 4.2% and NMAE of 0.4%. Although the LSSVM model performs well in solving the prediction problem, it has some limitations. First, the LSSVM model may face the challenge of high computational complexity for large-scale datasets, leading to longer training time or an inability to handle large-scale data. Second, LSSVM models are more sensitive to selecting data features and adjusting parameters, which need to be carefully designed and adjusted to obtain good performance. In addition, the predictive performance of LSSVM models is affected by data noise and outliers, which may require additional processing steps to enhance the robustness of the model.