## 1 Introduction  
In recent years, excessive energy consumption has caused problems such as ecological environment deterioration and depletion of non-renewable energy sources. Worldwide, populations have begun to seek a new energy development path to gradually replace traditional fossil fuels with renewable energy [1]. Solar energy is valued because of its safety, efficiency, economy, and environmental friendliness. New energy grid-connected power generation, represented by photovoltaic power generation, is integral to future power systems [2]. In 2022, the global photovoltaic installed capacity reached 230 GW, and the cumulative installed capacity will reach 1156 GW, with a year-on-year growth of 35.3%. The output power of PV systems exhibits a high degree of randomness and volatility due to meteorological factors such as solar radiation [3; 4]. When the grid is connected to a high proportion of PV, the complexity of large-scale grid scheduling increases, and the stability and reliability of the power system operation are threatened. PV power prediction has become one of the vital fundamental technologies used to improve operation quality and ensure the stable operation of PV power generation. Accurate prediction of the power output of PV systems in the ultra-short-term is essential for extending the life of storage devices such as batteries, helping dispatchers to develop rational scheduling plans, improving system reliability, and deploying PV power on a large scale. In addition, it helps to reduce the uncertainty of PV generation on the power system, coordinate with other conventional power sources, improve grid scheduling, and increase the utilization of PV generation. In addition, PV power generation prediction is also utilized to enable online PV array fault diagnosis, which allows for the rapid detection of faults or anomalies due to corrosion, high winds, heavy rains, hailstorms, dirt, UV irradiation, thermal cycling, etc. [5]. Therefore, accurate ultra-short-term PV power prediction is an important research area [6]. Various prediction methods have been proposed for PV power prediction [7]. There are four time periods for PV power prediction: long-term prediction is from one month to one year, medium-term prediction is from one week to one month, short-term prediction is from one week or less, and very short-term or ultra-short-term prediction is from one minute to a few minutes. Long-term forecasting is used for planning and decision-making for PV generation, transmission, and distribution and guarantees reliable power system operation. For medium-term forecasting, a model can be used for medium-term decision support for power system dispatches. Short-term forecasts are essential for day-ahead generation planning and improving the power system reliability. Ultra-short-term forecasts are used to guide real-time grid scheduling and battery storage control to ensure the safe operation of the grid. In addition, this method helps improve the quality of the information and make real-time corrections to short-term PV power forecast data. Forecasting methods can be classified into physical, statistical, and machine learning-based methods [8]. The physical approach is based on the principle of PV power generation. It uses mathematical models of solar radiation, temperature, humidity, cloud cover, barometric pressure, and wind speed obtained from numerical weather prediction (NWP). Physical forecasts are based on detailed site geographic information, accurate weather data, and complete PV information. Historical data are not required. However, since the meteorological environment limits solar resources, the complexity of the atmospheric environment will directly lead to a dramatic increase in model complexity. Therefore, simulation and prediction of PV output using physical models cannot provide accurate and meaningful predictions reflecting reality under extremely severe weather conditions [9].