## 1 Introduction
However, traditional optimization algorithms also have some limitations in optimizing the parameters of the LSSVM model, and these algorithms may converge slowly, tend to fall into local optimal solutions, or need help dealing with high-dimensional complex datasets. PV power output is highly volatile, especially in unstable weather. In this case, a combined prediction model based on signal decomposition is often used, which mainly decomposes the PV power sequence into several sub-sequences, models the prediction, and superimposes the prediction results. Classic decomposition techniques include wavelet transform (WT) [25], empirical mode decomposition (EMD) [26], and ensemble empirical mode decomposition (EEMD) [27]. However, both EMD and its improved methods exhibit apparent modal overlapping phenomena, which affect the prediction performance of the model to a certain extent. Compared with EEMD and WT, intrinsic computing-expressive empirical mode decomposition with adaptive noise (ICEEMDAN) best decomposes sequences [28]. The frequencies of the decomposed components fluctuate with significant frequency differences, representing different information at different timescales within the time series. However, the direct use of a single model to predict all of the components and superimpose the results will cause some errors. Changes in weather conditions can make PV power generation stochastic and volatile. Utilizing all historical data as training samples, especially those day-by-day records with significant variations in weather conditions, will prolong the model training time and reduce the forecasting accuracy. Therefore, it is necessary to divide the large amount of historical power data by weather type and train them separately [29]. A similar day selection method based on the Levy-Flight Beetle antenna search algorithm was proposed in the literature [30]. Considering the global horizontal radiation (GHR) trend, GHR fluctuations, and other weather factors, similar days are selected as training samples and better prediction results are achieved. Currently, many short-term PV power prediction studies focus on improving model accuracy, often ignoring the impact of weather factors on PV power generation [31]. In order to improve the prediction accuracy of the short-term PV power prediction model, it is necessary to effectively extract the relevant data from the massive data, avoid data redundancy, and reduce the non-stationarity of the PV power series. In this paper, the advantages of various models are synthesized and an ultra-short-term PV prediction model is constructed. The model considers weather factor feature selection, similar day clustering, signal decomposition, and hybrid deep learning. The main contents of this paper are as follows:  
1. A similar day clustering model is proposed. The K-means clustering method is applied to the historical data to accurately classify the weather conditions into sunny, cloudy, and rainy days. This concise and targeted classification strategy effectively avoids data clutter and redundancy, dramatically enhances the relevance and reliability of the training data, and lays a solid foundation for the accurate construction of the subsequent ultra-short-term PV prediction model. 2. To improve the algorithm's optimization ability, a multi-strategy improved snow ablation optimizer (MISAO) combining cyclic chaotic mapping initialization strategies, Gaussian diffusion strategies, and random follower search strategies is proposed. The parameters of ICEEMDAN are optimized using MISAO. The model decomposes the fluctuating stochastic raw PV power series into multiple subsequences, each with unique frequencies and features, thus reducing the non-stationarity and complexity of the PV power series. 3. Constructing the multivariate prediction model extends the available data dimensions and enhances the model's predictability.