# Assessment of Different Deep Learning Methods of Power Generation Forecasting for Solar PV System
## 6 Conclusions  
The acquisition of weather feature data greatly influences the one-day-ahead solar photovoltaic prediction results. The previous literature focused on a single data source, comparing different weather features, or used historical time-series data directly for one-day-ahead PV power forecasting. The objective of this study was to find the appropriate weather features and weather data group paired with a deep learning algorithm to implement one-day-ahead PV power forecasting. In this work, the application of ANN, LSTM, and GRU combined with different weather data (LWS, CWB, and hybrid data) was used for one-day-ahead photovoltaic power generation forecasting, and the forecasting effects were compared. The area of study covers a rooftop solar photovoltaic system in Yangmei District, Taiwan. The data pre-processing method proposed in this article can prevent the failure of forecasting results and solve the data unavailability problem. Hybrid weather data show up to 5-8% improvements in accuracy compared to the CWB and LWS. Considering all of the weather patterns, the LSTM model is more accurate for one-day-ahead PV power forecasting. MAPE, MAE, and RMSE are 16%, 0.71, and 0.83, respectively. The results indicate that when the amount of data is limited, using hybrid data and the five weather features is helpful for training the model. Accordingly, the proposed model shows better one-day-ahead PV forecasting.  
The pre-processing data framework may remove a large number of historical data points, which may lead to insufficient data and data sample discontinuity. This causes a limitation in model training in this study. Nevertheless, the deep learning method has good generalization ability (the adaptability of machine learning algorithms to fresh samples). Therefore, keeping the valuable data will have the best effect on long-term predictions. This article shows that the validation data over the one-month study is stable. The MAE, MAPE,  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Paper** & **Method Used** & **Inputs** & **Horizon** & **Best Results for Accuracy** \\ \hline \multirow{4}{*}{[17]} & \multirow{4}{*}{LSTM NN} & Solar irradiance, temperature, & & MAPE (\%): 22.31 \\  & & relative humidity, and & 24 h & RMSE: 0.71 \\  & & wind speed & & MAE: 0.36 \\ \multirow{4}{*}{[23]} & Weather-based & Temperature, probability of & & One-day-ahead \\  & hybrid method: SOM, & precipitation, and & hourly & RMSE: 1.6811 \\  & LVQ, and SVR & & solar irradiance & \\ \multirow{4}{*}{This manuscript} & \multirow{4}{*}{LSTM} & Temperature, relative humidity, & & MAPE (\%): 16.984 \\  & & rainfall, average wind speed, & 24 h & RMSE: 1.764 \\ \cline{1-1}  & & and UV index & & MAE: 1.283 \\ \hline \hline \end{tabular}
\end{table}
Table 10: Summary of different algorithms, data inputs, and horizons of one day-ahead PV forecasting.  
Figure 11: Forecasting results of the LSTM model for the month of June.  
and RMSE for one month are 16.984%, 1.764, and 1.283, respectively. These results support that the LSTM model combined with hybrid weather data is reliable for one-day-ahead PV power forecasting.  
This paper is the collaborative work of all authors. Conceptualization, C.-H.C. and C.-C.W.; methodology, W.-C.K. and S.-H.H.H.; software, W.-C.K. and S.-H.H.; validation, W.-C.K., C.-H.C. and S.-H.H.; formal analysis, W.-C.K.; investigation, W.-C.K. and C.-H.C.; resources, C.-H.C.; data curation, W.-C.K.; writing--original draft preparation, W.-C.K.; writing--review and editing, C.-C.W.; supervision, C.-H.C.; project administration, C.-H.C., C.-C.W. and W.-C.K. All authors have read and agreed to the published version of the manuscript.  
This research was funded by the Ministry of Science and Technology, Taiwan, R.O.C., grant number MOST 110-2221-E-035-091 and MOST 110-2221-E-A49-079.  
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The authors have not provided data associated with this article.  
The authors declare no conflict of interest.