# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 1 Introduction
[19] pointed out that inaccurate predictions usually occur on cloudy days. Therefore, to improve prediction accuracy in cloudy weather, for the classification of weather types, cloudy days were divided into cloudy and mixed. [PERSON] et al. [20] proposed a new ensemble method based on the probabilistic distribution of trials using the probabilistic ensemble method (PEM) for PV power forecasting on cloudy days. The result showed that the nRMSE metric reached 4.79% in 2017 in the totally cloudy days class. This paper proposes a data classification method and finds the critical weather factor that affects PV power generation forecasting. Furthermore, with advances in the calculation speed of computers, machine learning has been widely used in the past decade, especially in prediction-related literature using large amounts of complex historical information [21; 22]. [PERSON] et al. [23] proposed power output forecasting of a PV system based on 24 h ahead insolation forecasting by using reported weather data, fuzzy theory, and a neural network. [PERSON] et al. [24] proposed a hybrid method for hourly forecasting of 1-day-ahead PV power. The historical data of PV output power were classified by the self-organizing map (SOM) and learning vector quantization (LVQ) networks. The results showed that the proposed approach achieved better predictive accuracy than the traditional ANN and simple Support Vector Regression (SVR) methods. [PERSON] et al. [25] assessed deep learning with the time series method and claimed that it is suitable and competitive for solar forecasting. The results showed that LSTM and GRU models are suitable and competitive for solar radiation time series forecasting. [PERSON] et al. [26] used a backpropagation neural network (BPNN) and extreme learning machine (ELM) for one-hour-ahead solar power forecasting. The results showed that the accuracy and computational time of extreme learning machine were better than those of backpropagation neural networks. [PERSON] et al. [27] used weather conditions that were categorized as sunny, cloudy, rainy, low light, sunny-cloudy, and rainy-sunny as the training dataset to carry out one-day-ahead solar power forecasting. The results showed that their proposed simplified LSTM model outperformed the multilayer perceptron (MLP) model. [PERSON] et al. [28] provided a comprehensive and systematic review of the direct forecasting of photovoltaic power generation. They also provided a critical analysis of recent work, including statistics and machine learning models based on historical data. [PERSON] et al. [29] compared the performance of LSTM and MLP for PV power forecasting in winter and summer. The results indicated that the effect using LSTM was better for short-term forecasting. A summary of different forecasting algorithms, data inputs, and horizons in the literature is shown in Table 1.  
The actual PV power generation site used in this study is in Taoyuan City, Taiwan. The number of equivalent sunshine hours is about 3 h. The weather in this city in the experimental year was relatively unstable. The objective of this study was to compare three kinds of deep learning models, namely, the artificial neural network (ANN), long short-term memory (LSTM), and gated recurrent unit (GRU), for one-day-ahead PV power generation forecasting. Furthermore, a data pre-processing framework is proposed to improve predictive accuracy and prevent the failure of one-day-ahead PV power forecasts. The structure of this article is as follows: Section 1 presents a brief review of weather factors and modeling research for PV power forecasting. The details of the various weather data resources used in this study are introduced in Section 2. Then, Section 3 proposes the framework for data pre-processing. Section 4 introduces the application of deep learning