# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 1 Introduction  
In recent decades, renewable energy development and carbon reduction have been energy policy targets in various countries in response to global warming. Due to the intrinsic intermittent and uncertain nature of renewable energy, previous experience shows that a high penetration percentage of renewable energy will affect the system's voltage, frequency, and load demand fluctuations. In the state of California, the solar penetration rate of residential roofs has increased rapidly, causing the load to show a duck curve, which conflicts with conventional generation efficiency and the frequency of daily start-up/shutdown operations [1; 2]. In this context, developing an accurate forecast of long-term and short-term renewable generation ability is vital for efficient power dispatch and operations. This paper compares different models and weather data to predict one-day-ahead hourly PV power generation. Accurate prediction of PV power generation is challenging due to complex interactions between many environmental conditions and uncontrollable factors. Therefore, studying weather data is crucial to predicting the impact of solar power generation [3; 4; 5]. [PERSON] et al. [6] selected the most influential variables, including sky coverage, relative humidity, and temperature, for forecasting model training. Sensitivity tests showed that relative humidity plays a dominant role in forecasting. [PERSON] et al. [7] concluded thatthe most common weather variables, including sky coverage, dew point, relative humidity, and temperature, can be used to predict solar PV generation. [PERSON] and [PERSON] [8] estimated global solar radiation using relative humidity and daily mean temperature and showed that their model outperformed other cases ((1) daily maximum air temperature and (2) daily mean air temperature), with an absolute mean percentage error of 4.49%. [PERSON] et al. [9] compared the impact of seven meteorological variables on the accuracy of predictions, including air temperature, relative humidity, pressure, cloud coverage, wind speed, and wind direction. The best results strongly depended on cloud coverage and relative humidity. The least influential variable was wind direction, followed by wind speed. As described by [PERSON] et al. [10], sunshine hours, daily mean air temperature, and relative humidity are key variables, and the proper processing of these variables on a specific timescale outperformed the other timescale, with an absolute mean percentage error of 8.84%. [PERSON] et al. [11] determined the influence of ambient temperature on the forecast of PV power and solar irradiance and yielded the best predictive ability. [PERSON] et al. [12] noted that temperature was not as sensitive as other weather parameters among several methods for solar energy prediction. Several studies indicated that weather parameters had a potential impact on PV power prediction, including the relative humidity, temperature, diffuse horizontal radiation, and global horizontal radiation, which significantly affected PV power output. Moreover, daily precipitation seems to be a less critical factor in PV power prediction [13; 14; 15]. Additionally, previous studies pointed out the importance of weather data classification, which significantly helped in improving the PV prediction effect. [PERSON] and [PERSON] [16] proposed a novel weather classification process and a novel way to embed a physical model of PV units into the K-Nearest Neighbor (KNN) algorithm model. The results showed that the method could reduce the uncertainty of PV output forecasting. [PERSON] and [PERSON] [17] used the K-means algorithm to classify different levels of irradiance, where the interval was based on the hour of the day and the season. [PERSON] et al. [18] proposed a new method, which was a factor-based graph modeling method using numerical weather prediction (NWP) data for PV power prediction. This method was used to explore and analyze a multi-graph model that can adapt to different weather conditions for PV forecasting. [PERSON] et al.