## 2 Flow of the Proposed Combined Model  
In the 1960s, [PERSON] et.al. discovered combined forecasting methods, which use more than two different forecasting models to address the same problem. Multiple models can be combined by the combination of quantitative methods. The main purpose of this combination is to make full use of the information provided by various models to improve forecasting accuracy [51]. In our study, a combined forecasting model was developed to forecast the nonlinear characteristics of wind speed and wind power. The forecasting processes used are shown in Figure 1, and the details of the forecasting procedure are described below.  
**Procedure 1:** Data Pretreatment  
This stage included two integral parts. First, SSA was used to decompose the raw wind speed series into a (_r_) low-frequency components group [52] and a (_d-r_) high-frequency components group, according to KPAC. Second, EEMD [53] was utilized to further denoise the (_d-r_) high-frequency components group on the premise of SSA.  
**Procedure 2:** Prediction of Hybrid Models  
The forecasting accuracy of a single model cannot be optimal when a change in training data occurs during the forecasting process. Thus, single models do not always give optimal forecasting results when training data changes. To avoid a poor forecasting performance, combined models were developed. In this study, seven different models (ANFIS, BPNN, ELM, ENN, GRNN, LSTM, RNN, WNN and SVM) were employed to forecast wind speed and wind power, and five optimal forecasting models were selected as sub-models of the combined model. Meanwhile, data pre-processing was used to eliminate noise and useless information in the original time series. In the process of forecasting and modeling, the first 9 days (1296 data point) were used as the training set for the model, and days 10-14 (720 data points) were used as the testing set of the model. Each model produced 144 forecasting values.  
Figure 1: Flow Chart of the Proposed Model.  
**Procedure 3:** Establishment of the Proposed Combined System  
After completing the modeling and forecasting process for the single hybrid model, the accuracy of each model testing set was calculated, and the five forecasting models with the highest accuracy levels were selected as sub-models of the combined model. Then, the testing data (720 data points) of the sub-models were used to build the combined model. The modified multi-objective dragonfly algorithm (the detail of MODA shown in Appendix A) was used to determine the optimal weight of the combined model. Subsequently, the forecasting results obtained from every individual model were integrated using the obtained weight coefficients, and wind speed and wind power forecasting was conducted.  
**Procedure 4:** Wind Speed and Wind Power Forecasting  
Based on realistic data, the developed combined model was used to carry out day-ahead forecasting. The theory behind day-ahead forecasting is as follows: First, the forecast origin and forecast horizon are set and denoted by a time point \(h\) and a positive whole number \(l\), respectively. Then, if we want to forecast \(\hat{y}_{h+l}\) at time point \(h\) where \(l\geq 1\), we now define \(\hat{y}_{h}(l)\) as the prediction data of \(y_{h+l}\); therefore, \(\hat{y}_{h}(l)\) can represent the \(l\)-step day-ahead forecasting of \(\mathbf{y}_{t}\) at prediction point \(h\). Once \(l=1\), \(\hat{y}_{h}(l)\) is used to carry out one-step day-ahead forecasting [54].  
**Procedure 5:** Model Evaluation  
The forecasting precision and stability of the models during point forecasting were evaluated by four metrics. Three metrics were used for the uncertainty analysis.