# TCNformer Model for Photovoltaic Power Prediction
## 4 Experiment
### Variable Selection Results and Discussion  
The VS module in the long- and short-sequence correction network includes correlation analysis and periodicity analysis. The results of the correlation analysis on photovoltaic power are shown in Table 2.  
It can be seen from Table 2 that photovoltaic power is positively correlated with direct radiation intensity, scattered radiation intensity, temperature, and wind speed, while it is negatively correlated with humidity, wind direction, and rainfall. According to their numerical values, the data were filtered by 0.1. It can be seen that the correlation between direct radiation intensity and photovoltaic power is the largest, while variables such as scattered radiation intensity, temperature, humidity, and wind speed have a certain correlation with photovoltaic power, which show that these influencing factors have a certain degree of impact on the photovoltaic power, and the impact decreases in turn. Although wind direction and rainfall are negatively related to the photovoltaic power, the value is too small to impact the output.  
It can be seen from Table 3 that the cycle of photovoltaic power, humidity, direct radiation intensity, and scattered radiation intensity is 24.03 h, approximately 1 day, and the cycle of the wind speed, wind direction, and rainfall is 0.17 h, which can be regarded as a periodicity. The temperature cycle is 8760 h; that is, the temperature cycle conforms to the changes in the four seasons, and the above results basically conform to the natural logic.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Parameter** & **Value** \\ \hline LSTM hidden layers & 2 \\ SkipGRU hidden layers & 2 \\ Encoder layers & 2 \\ LSTM hidden unit & 64 \\ SkipGRU hidden unit & 64 \\ Decoder layers & 1 \\ d\({}_{\text{model}}\) & 512 \\ batch-size & 8 \\ learn\_rate & 0.0001 \\ epochs & 2000 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Model parameter setting.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & \(\mathbf{p}\) \\ \hline Wind speed & 0.2096 \\ Temperature & 0.4246 \\ Humidity & \(-\)0.4072 \\ Direct radiation & 0.9690 \\ Scattered radiation & 0.5183 \\ Wind direction & \(-\)0.0444 \\ Rainfall & \(-\)0.0244 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Results of correlation analysis.  
\begin{table}
\begin{tabular}{c c} \hline \hline
**Variable** & \(\mathbf{p}\) \\ \hline Wind speed & 0.2096 \\ Temperature & 0.4246 \\ Humidity & \(-\)0.4072 \\ Direct radiation & 0.9690 \\ Scattered radiation & 0.5183 \\ Wind direction & \(-\)0.0444 \\ Rainfall & \(-\)0.0244 \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results of periodicity analysis.  
By correlation analysis, five influencing factors should be selected, including direct radiation, scattered radiation, temperature, humidity, and wind speed. Three influencing factors, namely, direct radiation, scattered radiation, and humidity, were screened through periodic analysis. Finally, the time series related variables of photovoltaic power were screened through the VS module, those being direct radiation, scattered radiation, and humidity.