# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 2 Methodology and Resources
### Proposed Methodology  
This study introduces a hybrid strategy to increase the accuracy of the intra-hour forecast of photovoltaic power generation. The proposal consists of the following elements: first, the \(\text{NWP}_{\text{GH}}\) data are post-processed to obtain more accurate GHI forecasts. Subsequently, a simple physical chain model is attached, which includes the processes of decomposition, transposition, and conversion from the plane of array irradiance Ipoa to power but with a deep learning approach as seen in Figure 2.  
The upper section represents the traditional physical chain model, where each stage (calibration, decomposition, and transposition) is performed using statistical techniques and empirical equations. The lower section presents the proposed deep learning-based approach, where each stage is replaced by a Transformer BI-LSTM model. The colours represent: green for the input Global Horizontal Irradiance (GHI), yellow for the steps in the traditional method, red for the deep learning models in the proposed method, and blue for the final power forecast.  
It should be explicitly noted that in this article, no physical model of a photovoltaic module is implemented, as only an indirect approach is used, employing a deep learning model to model the transformation process from Ipoa to photovoltaic power. The latter is based on [23], where the author comments that the transformation steps between GHI and Ipoa are where the biggest errors occur. The proposed model inspired by existing research is used. This sequence of four steps is defined as a deep chain model. Each element of the chain is trained to replicate the function of each block of the traditional physical model chain. Later, we use the transfer learning technique, and all the models are concatenated to make the power forecast.  
\begin{table}
\begin{tabular}{l l l l l l l l l} \hline \hline
**Article** & **Model** & **Metric** & \multicolumn{5}{c}{**Station**} \\  & & & **0** & **1** & **2** & **3** & **4** & **5** & **8** \\ \hline
[21] & PSC-SVR & MAE & - & 1.29 & 0.96 & - & 1.39 & - & 1.03 \\  & & RSME & - & 2.15 & 1.52 & - & 2.34 & - & 1.70 \\  & SVR & MAE & - & 1.32 & 0.98 & - & 1.41 & - & 1.04 \\  & & RSME & - & 2.17 & 1.54 & - & 2.34 & - & 1.72 \\  & PSC-GBR & MAE & - & 1.31 & 0.98 & - & 1.41 & - & 1.05 \\  & & RSME & - & 2.17 & 1.53 & - & 2.35 & - & 1.71 \\  & GBR & MAE & - & 1.34 & 1.00 & - & 1.42 & - & 1.06 \\  & RSME & - & 2.19 & 1.54 & - & 2.33 & - & 1.73 \\  & PSC-RF & MAE & - & 1.38 & 1.02 & - & 1.50 & - & 1.12 \\  & & RSME & - & 2.30 & 1.61 & - & 2.47 & - & 1.82 \\  & CRT-SVR & MAE & - & 1.38 & 1.02 & - & 1.50 & - & 1.12 \\  & & RSME & - & 2.30 & 1.61 & - & 2.47 & - & 1.82 \\  & Persistence & MAE & - & 2.78 & 1.95 & - & 3.40 & - & 2.18 \\  & RSME & - & 3.55 & 2.49 & - & 4.21 & - & 2.80 \\
[22] & GSTANN-S & MAE & 1.40 & 1.43 & 1.38 & 1.99 & 1.29 & 1.86 & - \\  & RSME & 1.88 & 1.83 & 1.92 & 2.53 & 1.80 & 2.37 & - \\  & GSTANN & MAE & 1.36 & 1.09 & 1.07 & 1.36 & 1.24 & 1.29 & - \\  & RSME & 1.89 & 1.51 & 1.56 & 1.85 & 1.74 & 1.91 & - \\ \hline \hline \end{tabular}
\end{table}
Table 4: Table of results found in the literature review for one-hour-ahead forecasts using the PVOD dataset.