## 2 Related Works
### Related Works: Solar Power Forecasting
The second category of methods is based on a set of broad standards to shield the model computation results from the errors in the inputs. For instance, [PERSON] et al. [28] preventedabnormal input values from creating numerous errors in the output results by limiting the upper bound of the prediction model's loss function to a constant, thereby ultimately achieving robustness. [PERSON] et al. [78] recommended that models should be trained to output reasonable prediction values when the input contains errors so as to prevent them from outputting inappropriate results when online; the results of their simulation experiments served as proof of concept. Finally, the last category of methods ensembles multiple models with different inputs but the same prediction target. For instance, [PERSON] and [PERSON] [79] combined principal components analysis, k-mean, and random forest algorithms to create a hybrid model and applied it to solve instabilities in solar power generation. [PERSON] et al. [80] proposed a hybrid model with high precision in solar power forecasting. The model included support vector machines, decision trees, random forest, and gradient boosting to predict various factors, and they used precision, recall, and F1-score to verify the accuracy of the hybrid model predictions. [PERSON] et al. [33] first employed different methods to obtain various prediction results, used a technique called the continuous ranked probability score to give the prediction results different weights, and then ensembled the prediction results based on the weights. [PERSON] et al. [2] used different probability distributions to establish multiple models, competed these models against one another, and then used the three most accurate models for ensemble predictions. [PERSON] et al. [19] recently established integrated models for existing machine learning models, such as gradient boosting machines, random forest, k-nearest neighbor, and support vector machines. Their results confirmed that this approach is highly intuitive and effective in dealing with multiple types of inputs, achieving extremely low prediction error even in the presence of input error. Researchers have also determined that clustering data prior to modeling helps in managing input error more effectively. [PERSON] et al. [34] ensembled different forecasting results using clustering and blending and then performed experiments to demonstrate that this approach could provide more robust forecasting results. Adopting the approach used by [34], [PERSON] and [PERSON] [35] conducted the cluster analysis of solar power generation to obtain weather conditions, established random forests with different parameters depending on different weather conditions, and then performed weighted calculations of the different random forest models to obtain the final results. More recently, [PERSON] et al. [1] believed that conventional clustering methods may not be effective, so they performed clustering using kernel density estimation. In the online application, they first identified the cluster closest to the current conditions and then ensembled the model of said cluster for forecasting. They demonstrated through experiments that their approach was superior among clustering methods.