# Enhancing Photovoltaic Power Predictions with Deep Physical Chain Model
## 2 Methodology and Resources
### Literature Review  
In the field of photovoltaic generation forecasting, multiple methodologies have been proposed to both increase the predictive accuracy and to approach the inherent variability in the photovoltaic generated power. Three contemporary studies that have used the aforementioned dataset are briefly compared here. The study of [20] uses LSTM networks and an extreme gradient boosting model (XGBoost) with attention mechanisms to solve problems derived from overfitting. Using NWP data from the Computational Network Center of the Chinese Academy of Science and ground sensor measurements, this study focuses on optimizing feature selection using the Pearson coefficient. The innovation resides in the application of attention mechanisms that calculate the weight of each characteristic in each instance. However, the lack of details about the partitioning of data for training and validation is criticized. On the other hand, since this work focuses on the estimation of the power for a time \(t\) using data corresponding to the same moment \(t\), we will not consider their results since they are solving a different problem from the one considered in this investigation; however, we will use their methodology as inspiration for this research. The work of [21] uses physical models to generate features that are used in training the two SVR models that make up its proposal for power forecasting. Using a dataset similar to that of the first study, the methodology is based on the fusion of NWP data, climatic data measured by the sensor, and historical data on photovoltaic production. Performance metrics are compared with existing models, showing better performance in terms of mean absolute error (MAE), mean square error (MSE), and root mean square error (RMSE). \begin{table}
\begin{tabular}{l l l l} \hline \hline
**File** & **Name** & **Description** & **Units** \\ \hline Station & Date\_time & Format: Year-Month-Day Hour-Min & – \\ Data & NWP\({}_{\text{GH}}\) & Global Horizontal Irradiation from NWP & W/m\({}^{2}\) \\ \([0-9]\) & NWP\({}_{\text{DNI}}\) & Direct Normal Irradiation from NWP & W/m\({}^{2}\) \\  & NWP\({}_{\text{T}}\) & 10 m dry-bulb temperature from NWP & \({}^{\circ}\)C \\  & NWP\({}_{\text{H}}\) & 10 m relative humidity from NWP & \% \\  & NWP\({}_{\text{WD}}\) & 10 m wind direction from & degrees \\  & NWP\({}_{\text{W}}\), zero north clockwise & \\ \hline  & NWP\({}_{\text{WS}}\) & Wind speed from NWP & degrees \\  & NWP\({}_{\text{P}}\) & Atmospheric pressure from NWP & hPa \\  & LMD\({}_{\text{GH}}\) & Global Horizontal Irradiation from LMD & W/m\({}^{2}\) \\  & LMD\({}_{\text{DHI}}\) & Diffuse Horizontal Irradiance from LMD & W/m\({}^{2}\) \\  & LMD\({}_{\text{T}}\) & Temperature from LMD & \({}^{\circ}\)C \\  & LMD\({}_{\text{P}}\) & Atmospheric pressure from LMD & hPa \\  & LMD\({}_{\text{WD}}\) & Wind direction from LMD & degrees \\  & LMD\({}_{\text{WS}}\) & Wind speed from LMD & m/s \\  & Power & Photovoltaic output from the station & MW \\ \hline \hline \end{tabular}
\end{table} 
Table 2: _Cont._ \begin{table}
\begin{tabular}{c c c c c} \hline \hline
**Station Name** & **Capacity** & **Longitude** & **Latitude** & **Elevation** & **Surface Tilt** \\  & **[kW]** & **[Degrees]** & **[Degrees]** & **[m]** & **[Degrees]** \\ \hline station00 & 6600 & 114.951 & 38.047 & 51.09 & 33.0 \\ station01 & 20,000 & 117.457 & 38.183 & 6.88 & 33.0 \\ station02 & 17,000 & 114.198 & 38.057 & 258.95 & 29.0 \\ station03 & 20,000 & 114.114 & 38.109 & 344.39 & 33.0 \\ station04 & 20,000 & 114.867 & 39.515 & 1196.78 & 37.0 \\ station05 & 35,000 & 114.123 & 38.235 & 156.96 & 33.0 \\ station06 & 15,000 & 114.548 & 36.898 & 59.54 & 34.0 \\ station07 & 20,000 & 113.641 & 36.644 & 714.22 & 31.0 \\ station08 & 20,000 & 113.899 & 36.707 & 467.64 & 33.0 \\ station09 & 20,000 & 115.059 & 38.731 & 53.22 & 31.0 \\ \hline \hline \end{tabular}
\end{table} 
Table 3: Metadata of the 10 photovoltaic stations present in the selected dataset.