## 3 Results  
The single-layer LSTM network received 192 timesteps of the PV system's energy production data and forecasted 6 timesteps into the future. Each timestep was 15 min, so the model received just over three days of data as input and forecasted up to 1.5 h into the future. Both the single-layer models and the 5-layer model to which they were compared had 50 neurons in each hidden layer. Table 1 shows results from testing the two single-layer LSTM models with the test sets and compares their performances with that of the pre-existing 5-layer LSTM network. The RMSE is the standard deviation of the residuals and is a measure of how well a regression fits a set of data [10]. The nRMSE is the RMSE divided by the mean of the data [11], which makes it possible to compare the results between models regardless of units. The MBE is a measure of the average bias in the prediction and indicates whether the model tends to over or under estimate [12]. The MAE measures the average error between the forecasted and actual values [13]. The \(R^{2}\) value measures the strength of the relationship between the forecasted and actual values where 1 represents a strong correlation and 0 represents no correlation. In addition to the test data, 10-fold validation was used to evaluate the model. The results of the validation are shown in Table 2 in the form of the mean squared error (MSE) and variance. \begin{table}
\begin{tabular}{c c c c c} \hline \hline  & **RMSE (W)** & **nRMSE** & **MBE (W)** & **MAE (W)** & \(R^{2}\) \\ \hline time-interpolated single-layer model & 450.90 & 10.7\% & 26.56 & 235.10 & 0.882 \\ linearly interpolated single-layer model & 455.35 & 10.8\% & 1.30 & 259.39 & 0.879 \\
5-layer (time interpolated) model & 478.25 & 11.4\% & 76.2 & 240.65 & 0.868 \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Test results for single-layer and 5-layer LSTM model. At 10.7% and 10.8% nRMSE, the error of the single-layer models presented in this article fall at the low end of the published spectrum. Given the predictability of Cyprus weather, this is not surprising. What this study does show, however, is that LSTM networks are at least as good as the other, more well-researched ANNs for near-future solar forecasting. Additionally, because the single-layer LSTM networks performed similarly to the five-layer LSTM network, smaller LSTM networks may be able to accomplish quite complicated tasks as well as, or better than their larger counterparts. Most of the high-performing ANN models use large multi-layered networks that are slow to train and require high processing power. If a single-layer LSTM network can achieve the same results as a larger MLP, or a larger LSTM, it would be more economical to choose the smaller LSTM network. Of course, this is a small-scale study, and further research would have to be done to confirm these findings. Specifically the performance of single-layer LSTM networks should be compared to that of larger LSTM and deep-learning MLP networks in several differing climates to see if they are indeed superior for solar forecasting. For this Article, [PERSON]-[PERSON] trained and tested the single-layer LSTM models, evaluated the results, and wrote this article with guidance from [PERSON], and supervision by [PERSON] and [PERSON]. [PERSON] designed, trained, and tested the 5-layer LSTM network that the single-layer models were compared to. She also aided in much of the programming of the single-layer models. All authors have read and agreed to the published version of the manuscript. Part of this work was supported by the SOLAR-ERA.NET Cofund Joint Call/Cyprus Research Promotion Foundation (CRPF) [grant number KOINA/SOLAR-ERA.NET/1216/0014]. This work was also funded by University of Twente and by the COST Association through COST Action CA16235 PEARL PV ([[https://www.pearlpv-cost.eu](https://www.pearlpv-cost.eu)]([https://www.pearlpv-cost.eu](https://www.pearlpv-cost.eu))).