## 1 Introduction
### Background
#### 1.1.2 Related Studies from Literature  
Over the past several years, there has been increasing interest and investigation into using LSTM networks for solar forecasting. Many of these methods, such as those presented in [8], require exogenous meteorological data.  
In [8], [PERSON] et al. developed two LSTM models for hourly PV power forecasting in Gumi City, South Korea. Their first LSTM model received six inputs: temperature, humidity, cloudiness, radiation, month, and day. Their second LSTM model used only the four meteorological inputs and omitted the seasonal information of month and day. Both models had three hidden layers and gave a single output of PV power for the next hour. The seasonal LSTM model outperformed all the other forecast models it was compared to including an ARIMA, seasonal ARIMA, simple feedforward ANN, deep-learning ANN, and a seasonal deep-learning ANN.  
Figure 1: Diagram of an LSTM cell [3]  
In [9], [PERSON] et al. developed several LSTM models for 1-hour ahead solar power forecasting in Aswan and Cario Egypt. They trained and tested five different LSTM architectures with varying inputs, numbers of layers, and type of LSTM. All of their models used purely endogenous, historical PV data as input. Their base LSTM model (Model 1) received the PV production value as its only input one hour prior to the desired forecast time. It had a single hidden layer which consisted of four LSTM cells. Their second model (Model 2) was similar to Model 1 except it received production values from the last three, one-hour timesteps as separate input features to forecast one-hour ahead. Their third model (Model 3) processed the last three timesteps through a single input feature before calculating its forecasted value. Model 4 is similar to Model 1 except that it used stateful LSTM blocks which enabled the network to remember trends across training batches. Finally, their Model 5 was a two-layer, stateful LSTM network.  
The researchers in [9] used 70% of their data for training and 30% for testing. Each model was evaluated after being trained with 20, 50 and 100 epochs. Model 3, trained with 50 epochs performed the best. The researchers compared Model 3's performance to a multiple-linear regression model (MLR), a bagged regression trees (BRT) model, and a simple feedforward neural network model, all of which were trained using the same data. [PERSON] et al. found that the Model 3 LSTM network significantly outperformed the MLR, BRT and feedforward neural network.  
While LSTM models using meteorological data show promise, depending on the location of the PV system being forecasted, exogenous data like those used in [8] may be unavailable, expensive to obtain or unreliable. Because of these potential problems, the PROGNOSIS project aims to develop LSTM models that use only endogenous, historical PV production data as their input.  
Our study is similar to [9] in that it explores different LSTM architectures for solar forecasting using only endogenous data. Two main differences exist between our study and [9]. The most significant difference is that our study uses 192, 15-min timesteps as input instead of the 1-2, one-hour timesteps used in [9]. Our larger number of timesteps can take better advantage of the LSTM cell's ability to remember longer-term trends in the data. The 15 min sample time provided our forecast models with greater temporal resolution which could be more helpful to grid operators than an hourly forecast. Additionally, Ref. [9] used relatively small LSTM networks of one or two layers with four LSTM cells per layer, whereas our study investigates one and five layer LSTM networks with 50 LSTM cells per layer. As LSTM optimization is largely trial and error, it was possible that our, the slightly larger networks would perform better than the smaller ones presented in [9].