## 1 Introduction
Fortunately, the timely emergence of artificial intelligence (AI) arithmetic, subsuming artificial neural networks (ANNs) [35], support vector machines (SVMs) [36], deep neural networks [37], and fuzzy logical methods (FLMs) [38] have efficiently remedied the flaws in the wind speed forecasting territory in recent years [39]. However, because of the inherent disadvantages of each model and the boom in the integration of wind power into the grid system, a variety of hybrid and combined models with promising forecasting potentials have been created [40]. Generally, artificial intelligence methods can achieve greater forecasting accuracy than physical or statistical models [41]; however, they also possess insurmountable drawbacks. ANNs have been extensively studied and applied to explore the complexity of wind speed forecasting; however, their performance mostly relies on training sets, which can result in a focus on local optima, over-fitting, and a reduction in the convergence rate [42]. 5. Differently to conventional or single models, hybrid models can reduce the current shortcomings associated with the forecasting of irregular, fluctuant, and nonstationary time series with noise or unpredictable components. In this regard, significant hybrid models have recently been launched [43]. Hybrid methods integrate different single algorithms to achieve a superior forecasting performance, and can overcome defects in AI models (e.g., falling into local minima, over-fitting, etc.) --greatly improving the accuracy of continuous fluctuant wind speed forecasting and providing better validity and stability than a single model [44]. Recently, the use of hybrid methods in the wind speed forecasting field has been widespread. [PERSON] et al. [45] proposed a hybrid model consisting of a grey correlation analysis, cuckoo search algorithm, and \(v\)-SVM (\(v\)-support vector machine). [PERSON] et al. [46] proposed a hybrid preprocessing strategy coupled with an optimized local linear fuzzy neural network for wind power forecasting. It has been proven that this is a effective approach for predicting wind power. In 2017, [PERSON] et al. [47] proposed a novel approach based on the Gaussian process with a \(t\)-observation model for short-term wind speed forecasting. Based on a spatio-temporal method, in [48], the performance of predictive clustering trees with a new feature space for wind power forecasting was investigated. The results showed that the proposed model achieved a satisfactory level of point forecasting accuracy and interval forecasting performance. A forecasting framework has been proposed in 2021 [49], that is multi-layer stacked bidirectional long/short-term memory (LSTM)based for short-term time series forecasting. After being studied extensively, it is clear that no arithmetic method is omnipotent across all data cases. In future research, individual statistical models and artificial intelligence algorithms will be integrated to improve the precision of wind speed forecasting--these are referred to as hybrid models [50]. As mentioned previously, conventional or individual methods always show inherent weaknesses when approaching complex and actual wind time series with noise, which results in poor forecasting performances. In accordance with previous studies, we designed three procedures for wind speed and wind power forecasting. First, data preprocessing was employed to identify features and eliminate useless information from the original time series. Then, the preprocessed time series was used to train and test the multi-artificial neural network. The models were ranked based on the test set accuracy for each model. Finally, the top five models were used as sub-models of the combined model. To obtain stabilized and accurate forecasting results, the weight of the combined model was optimized by the multi-objective optimization algorithm, which improved the stabilization and accuracy of the combined model.