# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 5 Numerical Results
### Forecast Performance with Different Weather Data Groups
This means that the predictive ability is still limited when only historical data are employed. Conversely, the forecast trend of the hybrid model is similar to the actual PV power generation results. The forecasting results of the GRU model are shown in Figure 9. It is also evident from the results that when using CWB data, the GRU model can effectively improve the accuracy in comparison with ANN, especially on 27 June 2021. However, the results show that if only the LWS historical weather data are used for forecasting, the prediction results are still inaccurate with severe deviations in tendency. This consequence is the same as those of ANN and LSTM, e.g., on 28 June and 1 July 2021. In contrast, when the hybrid data are used, the one-day-ahead solar photovoltaic forecasting trend is better than when only using CWB or LWS data. The prediction results on 1 July 2021 show that hybrid data significantly help improve the effect of the one-day-ahead solar photovoltaic forecast. Figure 8: The PV power forecasting results of the LSTM model. Figure 7: The PV power forecasting results of the ANN model. The predictive ability of different models can be clearly seen in the above figures. In addition, the predicted generation intensity every hour will largely vary when using different methods with different weather data sources. The average evaluation indices of the three models with different weather data groups are shown in Table 8. Comparing the forecasting results of different models, it is found that the predictive effect of ANN is relatively poor compared to those of LSTM and GRU. The one-week average value of MAPE can vary by up to 8-10%, and the maximum differences in MAE and RMSE are up to 1.089 and 0.46, respectively. The results imply that the LSTM and GRU models are more suitable for one-day-head solar photovoltaic forecasting. Subsequently, the forecasting results of different weather groups are compared. From the one-week average result, when only LWS historical data are used for forecasting, the prediction effect of all models is comparatively poor. Finally, the comprehensive comparison results from the trend of the PV power forecast curve or numerical statistical analysis show that using hybrid weather data can effectively improve the accuracy of solar photovoltaic forecasts. On the other hand, from the average prediction results of the three different groups of weather data, the effect of the LSTM model is the best, indicating that this model is more suitable as a solar photovoltaic prediction model in this study, with a MAPE of 20.0%, MSE of 1.158, and RMSE of 1.004. Based on the suggestion by [PERSON] [41], the accuracy of the predictive ability based on MAPE is classified as highly accurate (less than 10%), good (11% to 20%), reasonable (21% to 50%), and inaccurate (more than 50%). It is shown that the data pre-processing method proposed in this study combined with the LSTM deep learning forecasting model can achieve good prediction results. From the analysis results mentioned above, the use of hybrid data can significantly improve the one-day-ahead solar photovoltaic forecast effect. Therefore, the results of one-day-ahead solar photovoltaic forecasting with the hybrid data group for one week were further analyzed in this study, as shown in Figure 10. The results of the one-week forecast statistical numerical analysis are presented in Table 9. In Figure 10, there is an obvious prediction difference among the three models on 2 and 3 July 2021. The forecast results of ANN are worse than those of LSTM and GRU. This means that when there is a