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Accurate wind speed forecasting holds the potential to optimize wind farm design and enhance the utilization of wind energy resources. However, the majority of current research has encountered three issues: (1) Feature selection is independent of the sub-models and does not consider the sensitivity of the model to features, resulting in all sub-models using the same combination of features for training and prediction. (2) Sub-models are chosen only on the basis of their fitting ability, neglecting the distinctions between them. (3) There is a lack of interpretability analysis regarding the model's forecasting. It is particularly important to make interpretable analyses for predictive models, as it not only helps to increase the trustworthiness of the model, but also assist in fine-tuning the prediction model and identifying wind speed-related datasets. To address these issues, this paper proposes a novel method for combining wind speed forecasts. The approach comprises five steps: data noise reduction, feature selection, sub-model selection, sub-model combination, and interpretability analysis. (1) In the data noise reduction module, noise reduction is carried out on the raw data based on a combination of singular spectral analysis (SSA) and intrinsic computing expressive empirical mode decomposition with adaptive noise (ICEEMDAN). (2) In the feature selection module, a quantitative feature importance calculation approach is proposed to select the optimal feature input for eight different sub-models, back propagation neural network (BPNN), extreme learning machine (ELM), Elman, wavelet neural network (WNN), generalized regression neural network (GRNN), long short-term memory (LSTM), gate recurrent unit (GRU) and recurrent neural network (RNN). The method calculates the average marginal effect of each feature output in each sub-model and its fluctuation as the feature importance. (3) In the sub-model selection module, we propose an optimal comprehensive sub-model selection metric (CSMS), it combines the fitting ability and the variability of the sub-model. (4) In the combination module, a multi-objective coati optimization algorithm (MOCOA) is used to combine the selected optimal sub-models. (5) In the interpretability analysis, we leverage the deletion diagnosis method to explain and analyze the forecasting behavior of sub-models from both global and local sample perspectives. The experimental results show that compared with the other models, the proposed model is optimal, the MAPE is 8.4571 %, the R 2 can reach 0.9755, and it can be concluded at a pr = 99 % confidence level that the proposed model and the sub-models have a significant difference in performance. [ABSTRACT FROM AUTHOR]

As one of the effective renewable energy sources, wind energy has received attention because it is sustainable energy. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, current methods ignore the temporal characteristics of wind speed, which leads to inaccurate forecasting results. In this paper, we propose a novel SSA-CCN-ATT model to forecast the wind speed. Specifically, singular spectrum analysis (SSA) is first applied to decompose the original wind speed into several sub-signals. Secondly, we build a new deep learning CNN-ATT model that combines causal convolutional network (CNN) and attention mechanism (ATT). The causal convolutional network is used to extract the information in the wind speed time series. After that, the attention mechanism is employed to focus on the important information. Finally, a fully connected neural network layer is employed to get wind speed forecasting results. Three experiments on four datasets show that the proposed model performs better than other comparative models. Compared with different comparative models, the maximum improvement percentages of MAPE reaches up to 26.279%, and the minimum is 5.7210%. Moreover, a wind energy conversion curve was established by simulating historical wind speed data. [ABSTRACT FROM AUTHOR]

Network traffic prediction performs a main function in characterizing network community performance. An approach which could appropriately seize the salient characteristics of the network visitors could be very useful for network analysis and simulation. Network traffic prediction methods could be divided into two classes: one is the single models and the opposite is the hybrid fashions. The hybrid models integrate the merits of several single models and consequently can enhance the network traffic prediction accuracy. In this paper, a new hybrid network traffic prediction method (EPSVM) primarily based on Empirical Mode Decomposition (EMD), Particle Swarm Optimization (PSO), and Support Vector Machines (SVM) is presented. The EPSVM first utilizes EMD to eliminate the impact of noise signals. Then, SVM is applied to model training and fitting, and the parameters of SVM are optimized by PSO. The effectiveness of the presented method is examined by evaluating it with different methods, including basic SVM (BSVM), Empirical Mode Decomposition processed by SVM (ESVM), and SVM optimized by Particle Swarm Optimization (PSVM). Case studies have demonstrated that EPSVM performed better than the other three network traffic prediction models. [ABSTRACT FROM AUTHOR]

With the growth of global energy demand, the proportion of the total installed wind capacity continues to increase. Wind speed forecasting is essential to enhance the utilization of wind energy. However, it is not easy to make accurate wind speed forecasting since wind speed time series data has nonlinearity, fluctuation, and intermittence. These features of wind speed time series data make classical models unable to obtain expected forecasting results. Many researchers proposed wind speed forecasting models to overcome the shortage. Nevertheless, some wind speed prediction methods are limited because they cannot mine all implicit features of wind speed data. This paper proposes a novel wind speed forecasting model that can capture all implicit information of wind speed data and obtain accuracy prediction results. To be specific, ensemble empirical mode decomposition (EEMD) is firstly applied to remove the noise in the original wind speed data. Secondly, we build a novel deep learning model based on the attention mechanism and convolutional neural network (CNN). The proposed forecasting model can focus the important part of wind speed data and reduce the high computational complexity of CNN. Finally, a full connect neural network layer is employed to obtain wind speed forecasting results. In order to validate the performance of the proposed model, we take the wind speed data of the M2 tower at 20-meter height of the National Wind Power Technology Center of the United States as an example. The experimental results demonstrate that the forecasting errors of the proposed model are smaller than other comparative models. And the Diebold-Mariano test confirms that the proposed model exhibits a significant difference in performance compared with the comparison models. [ABSTRACT FROM AUTHOR]

Wind energy has attracted much attention because it is sustainable and renewable energy with a much smaller impact on the environment than fossil fuels. Accurate wind speed forecasting can pave the way to the goal of sustainable development. However, many papers only use wind speed series but ignore weather factors, which lead to poor forecasting results. In this paper, we propose an ensemble system that considers historical wind speed series and other weather factors to make wind speed forecasting. The proposed system integrates noise reduction, clustering, and multi-objective optimization. Firstly, the historical wind speed series are decomposed by complementary ensemble empirical mode decomposition (CEEMD). Secondly, the samples are clustered by self-organizing map (SOM) according to the weather factors. Finally, a regularized extreme learning machine (RELM) model is trained to forecast the wind speed for each cluster, and multi-objective grey wolf optimizer (MOGWO) is employed to optimize the parameters in the model. An empirical study using three datasets from the M2 tower of the national wind power technology center (NWTC) of the national renewable energy laboratory (NREL) illustrates that the proposed system performs better than other comparative models in terms of four performance indicators. • The proposed system not only considers historical wind speed data, but also considers other weather factors. • SOM is successfully applied to cluster the samples, and the samples are clustered according to the weather factors. • Based on CEEMD and ACF, a new time series de-noised method is proposed. • Design four experiments based on actual wind farms to examine the effectiveness of the proposed system. [ABSTRACT FROM AUTHOR]