## 5 Conclusions and Future Works  
As green energy technology develops, so too grows research interest in topics such as solar power forecasting. Existing forecasting methods assume the accuracy of input values, which is problematic in practice because errors are inevitable in data collected  
Figure 24: Comparison of NRMSE between our ensemble random forest and previous models when two input errors are present. (**a**) Surface solar rad down vs. total column liquid water, (**b**) surface solar rad down vs. surface pressure, (**c**) surface solar rad down vs. relative humidity at 1000 mbar, (**d**) surface solar rad down vs. total cloud cover, (**e**) surface solar rad down vs. 2 m temperature. by sensors. In view of this, we designed a robust solar power forecasting model based on the concepts of ensembling and lightweight deep learning models. In experiments, the proposed method demonstrated the following advantages: (1) In situations without input error, the NRMSE of NC-RBF-DNN in solar power generation prediction was only 0.07874, which is 11.5% better than the typical random forest algorithm. (2) NC-RBF-DNN proved more effective than the Pearson correlation coefficient in assigning model input importance scores, thereby allowing it to skip input factors that are redundant or of low importance to achieve lightweight modeling without sacrificing accuracy. (3) Compared to a prominent existing ensemble model based on four machine learning models, our NC-RBF-DNN ensemble model proved more effective in preventing output errors caused by input errors, as evidenced by ten times lower NRMSE. These experiment results demonstrate the rationale and effectiveness of the proposed method. In future work, we plan to make the following improvements: (1) establish models for different conditions, such as sunny and rainy days and different seasons, and (2) construct retrainable models. With regard to the first, we discuss in this paper how the importance ranking derived by the NC-RBF-DNN is influenced by whether the factor affects power generation in the long term or the short term. Some important factors with short-term effects are often ranked lower in importance by the NC-RBF-DNN. To overcome this, we plan to deal with normal and special circumstances separately to promote more accurate forecasting. As for the second improvement, it is well known that the prediction accuracy of an artificially intelligent (AI) model gradually decreases with time, so most AI models are trained regularly. However, the proposed model cannot be changed once the input factor combinations have been determined. To make changes, the modeling process must be started from scratch, which is time-consuming and labor-intensive. We therefore hope to adjust this in the future to lighten the burden of retraining. Conceptualization, C.-H.L., Y.-C.C. and H.-Y.S. ; methodology, C.-H.L. and Y.-C.C. ; software, C.-H.L. and Y.-C.C. ; validation, [PERSON]-H.L. and [PERSON]-[PERSON]; formal analysis, [PERSON]-H.L. and [PERSON]-[PERSON]; investigation, [PERSON]-H.L. and [PERSON]-[PERSON]; resources, [PERSON]-H.L., [PERSON]-[PERSON], [PERSON]-T.S. and H.-Y.S. ; data curation, H.-Y.S. ; writing--original draft preparation, [PERSON]-H.L. and [PERSON]-[PERSON]; writing--review and editing, [PERSON]-H.L. and [PERSON]-[PERSON]; visualization, [PERSON]-H.L. and [PERSON]-C.C. ; supervision, [PERSON]-C.C., [PERSON]-T.S. and H.-Y.S. ; project administration, [PERSON]-C.C., [PERSON]-T.S. and H.-Y.S. ; funding acquisition, [PERSON]-C.C., [PERSON]-T.S. and H.-Y.S. All authors have read and agreed to the published version of the manuscript. This research was funded by National Science and Technology Council, Taiwan, grant number 112-2121-M-005-006, 113-2121-M-005-003, and 111-2628-E-027-006-MY3.