## 1 Introduction
The data-driven approach predicts solar power through pattern analysis, which is trained on past data in time increments, such as 15 min, one hour, and daily units. This method is suitable for predicting solar power values within a short period of time as the amount of past data increases. Data-driven approaches can be either statistical or artificial intelligence methods. Statistics can obtain high accuracy in short-term forecasting, but cannot accurately forecast long-term solar power due to the progressive accumulation of errors. An example of a statistical approach is the auto regression integrated moving average (ARIMA) model, a time series analysis model [13] that has many disadvantages. Several approaches have been suggested to work around these limitations [14; 15]. In contrast, artificial intelligence includes neural networks [16], fuzzy inference [17], particle swarm optimization [18], genetic algorithms [19], support vector machines [20], and deep learning [21], which utilize long short-term memory [22], gated recurrent units [23], autoencoders [24], and convolutional neural networks [25]. Artificial intelligence approaches have superior performance for general purposes but are limited because the relationships between model elements cannot be accurately explained. 3. Hybrid approaches produce predictions by applying a statistical technique after using a physical approach. This method combines the advantages of physical and statistical approaches. In other words, traditional physical approaches are more suitable for long-term forecasting because of their large scale, while the data-driven approaches are accurate for short-term forecasting but accumulate errors in long-term forecasts. Traditional hybrid approaches suffer from many errors, and studies are underway to improve their predictions [26; 27; 28; 29]. Recently, hybrid approaches have been developed by combining machine learning and fuzzy theory, machine learning and deep learning, and multiple types of deep learning [30; 31; 32; 33]. The objectives of this study can be summarized as follows:1. A total of 22 numerical models were constructed for correlation analysis to assess the influence of meteorological factors such as solar radiation (SR), sunlight, humidity, temperature, cloud cover (CC), and wind speed (WS) on PV solar power forecasting. 2. Traditional long short-term memory (LSTM) is limited when predicting solar power generation in the time step between predictions, because small inaccuracies from the previous step compound and continuously increase the prediction error. The goal of the modified LSTM was to obtain the observed value from the time interval between predictions, and use the observed value instead of the predicted value to update the network for solar power generation forecasts. The correlation analysis between solar power and meteorological factors was verified by collecting data on buildings located in Ansan (Location \"A\" in Figure 1), Gyeonggi-do, Korea, from April 2018 to March 2019 in one-hour increments. Weather data for accurately forecasting solar power were collected by attaching sensors to the building in Ansan; however, this building is located in an industrial complex that has a temporary installation for solar power generation and has no relevance for weather data. Therefore, the meteorological data were collected from the closest meteorological observation point at Suwon (Location \"B\" in Figure 1), Gyeonggi-do, Korea. Data from the Suwon observation point were collected on an hourly basis by accessing the weather open data portal site of the Korea Meteorological Administration [34]. The organization of this study is as follows: Section 2 describes the correlations between solar power and meteorological factors based on data from April 2018. Recurrent neural networks (RNNs) and LSTM, which have superior performance among the artificial intelligence approaches, are explained and the limitations of traditional LSTM are introduced.