Because of the uncertainty and randomness of wind speed, wind power has characteristics such as nonlinearity and multiple frequencies. Accurate prediction of wind power is one effective means of improving wind power integration. Because the traditional single model cannot fully characterize the fluctuating characteristics of wind power, scholars have attempted to build other prediction models based on empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) to tackle this problem. However, the prediction accuracy of these models is affected by modal aliasing and illusive components. Aimed at these defects, this paper proposes a multi-frequency combination prediction model based on variational mode decomposition (VMD). We use a back propagation neural network (BPNN), autoregressive moving average (ARMA) model, and least squares support vector machine (LS-SVM) to predict high, intermediate, and low frequency components, respectively. Based on the predicted values of each component, the BPNN is applied to combine them into a final wind power prediction value. Finally, the prediction performance of the single prediction models (ARMA, BPNN, LS-SVM) and the decomposition prediction models (EMD and EEMD) are used to compare with the proposed VMD model according to the evaluation indices such as average absolute error, mean square error, and root mean square error to validate its feasibility and accuracy. The results show that the prediction accuracy of the proposed VMD model is higher.

Due to the existing large-scale grid-connected photovoltaic (PV) power generation installations, accurate PV power forecasting is critical to the safe and economical operation of electric power systems. In this study, a hybrid short-term forecasting method based on the Variational Mode Decomposition (VMD) technique, the Deep Belief Network (DBN) and the Auto-Regressive Moving Average Model (ARMA) is proposed to deal with the problem of forecasting accuracy. The DBN model combines a forward unsupervised greedy layer-by-layer training algorithm with a reverse Back-Projection (BP) fine-tuning algorithm, making full use of feature extraction advantages of the deep architecture and showing good performance in generalized predictive analysis. To better analyze the time series of historical data, VMD decomposes time series data into an ensemble of components with different frequencies; this improves the shortcomings of decomposition from Empirical Mode Decomposition (EMD) and Ensemble Empirical Mode Decomposition (EEMD) processes. Classification is achieved via the spectrum characteristics of modal components, the high-frequency Intrinsic Mode Functions (IMFs) components are predicted using the DBN, and the low-frequency IMFs components are predicted using the ARMA. Eventually, the forecasting result is generated by reconstructing the predicted component values. To demonstrate the effectiveness of the proposed method, it is tested based on the practical information of PV power generation data from a real case study in Yunnan. The proposed approach is compared, respectively, with the single prediction models and the decomposition-combined prediction models. The evaluation of the forecasting performance is carried out with the normalized absolute average error, normalized root-mean-square error and Hill inequality coefficient; the results are subsequently compared with real-world scenarios. The proposed approach outperforms the single prediction models and the combined forecasting methods, demonstrating its favorable accuracy and reliability.

The ring-closing reactions based on chemical bond metathesis enable the efficient construction of a wide variety of cyclic systems which receive broad interest from medicinal and organic communities. However, the analogous reaction with C–N bond metathesis as a strategic fundamental step remains an unanswered challenge. Herein, we report the design of a new fundamental metallic C–N bond metathesis reaction that enables the palladium-catalyzed ring-closing reaction of aminodienes with aminals. The reactions proceed efficiently under mild conditions and exhibit broad substrate generality and functional group compatibility, leading to a wide variety of 5- to 16-membered N-heterocycles bearing diverse frameworks and functional groups.

A series of titanate nanotube-supported metal catalysts (M/TNTs, M = Rh, Au orAu–Rh) were facilely synthesized. The effects of different Au contents, reduction processes and sequence of loading metals on their catalytic performances in the hydroformylation of vinyl acetate were comparatively investigated. The results showed that some Au and Rh formed bimetallic particles. Furthermore, the presence of Au in catalysts could significantly improve the selectivity of reaction for aldehydes. Compared with the monometallic catalysts (Rh0.33/TNTs-1 and Au0.49/TNTs-2), the resultant bimetallic catalysts exhibited significantly higher selectivity for aldehydes as well as higher TOF values in the hydroformylation of vinyl acetate. Among them, Au0.52/Rh0.32/TNTs-12 displayed the best catalytic performance. The corresponding selectivity for aldehydes was as high as 88.67%and the turnover frequency (TOF) reached up to 3500 h−1. In addition, for the reduction of Rh3+ and Au3+ ions, the photo-reduction and ethanol-reduction were the optimal techniques under the present conditions, respectively.