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.

An efficient strategy for facilitating the crosscoupling of two radicals has been established via the coordination of a radical with a metal catalyst. This strategy provides a remarkable ability to harness the reactivity of nitrile-containing azoalkanes and enables a novel cascade reaction with nitrilecontaining azoalkanes and propargylic alcohols to be established. By using this reaction, a range of acetylenic and allenic amides were obtained that provides a versatile platform for further derivatizations.

The TiO2-based nanotubes (TNTs, B–TNTs) of different surface acidities and their supported Rh catalysts were designed and synthesized. The catalysts were characterized by X-ray diffraction (XRD), scanning electron microscopy (SEM), transmission electron microscopy (TEM), X-ray photoelectron spectrometer (XPS), tempera–ture–programmed desorption of ammonia (NH3–TPD), atomic emission spectrometer (ICP), and Brunauer–Emmett–Tellerv (BET) surface-area analyzers. Images of SEM and TEM showed that the boron-decorated TiO2 nanotubes (B–TNTs) had a perfect multiwalled tubular structure; their length was up to hundreds of nanometers and inner diameter was about 7 nm. The results of NH3-TPD analyses showed that B–TNTs had a stronger acid site compared with TNTs. For Rh/TNTs and Rh/B–TNTs, Rh nanoparticles highly dispersed on B–TNTs were about 2.79 nm in average diameter and much smaller than those on TNTs, which were about 4.94 nm. The catalytic performances of catalysts for the hydroformylation of 2-methyl-3-butennitrile (2M3BN) were also evaluated, and results showed that the existence of B in Rh/B–TNTs had a great influence on the catalytic performance of the catalysts. The Rh/B–TNTs displayed higher catalytic activity, selectivity for aldehydes, and stability than the Rh/TNTs.

Power system load forecasting is an important part of power system scheduling. Since the power system load is easily affected by environmental factors such as weather and time, it has high volatility and multi-frequency. In order to improve the prediction accuracy, this paper proposes a load forecasting method based on variational mode decomposition (VMD) and feature correlation analysis. Firstly, the original load sequence is decomposed using VMD to obtain a series of intrinsic mode function (IMF), it is referred to below as a modal component, and they are divided into high frequency, intermediate frequency, and low frequency signals according to their fluctuation characteristics. Then, the feature information related to the power system load change is collected, and the correlation between each IMF and each feature information is analyzed using the maximum relevance minimum redundancy (mRMR) based on the mutual information to obtain the best feature set of each IMF. Finally, each component is input into the prediction model together with its feature set, in which back propagation neural network (BPNN) is used to predict high-frequency components, least square-support vector machine (LS-SVM) is used to predict intermediate and low frequency components, and BPNN is also used to integrate the prediction results to obtain the final load prediction value, and compare the prediction results of method in this paper with that of the prediction models such as autoregressive moving average model (ARMA), LS-SVM, BPNN, empirical mode decomposition (EMD), ensemble empirical mode decomposition (EEMD), and VMD. This paper carries out an example analysis based on the data of Xi’an Power Grid Corporation, and the results show that the prediction accuracy of method in this paper is higher.

What is the most favorite and original chemistry developed in your research group?Palladium-catalyzed aminal chemistry. How do you supervise your students?Discuss with my students and encourage them to think about where the scientific problem is. What is the most important personality for scientific research?Honesty, curiosity and passion. What are your hobbies?Reading and playing badminton. Who influences you mostly in your life?My parents, who always inspire me to go forward. What is your favorite journal(s)?Journals that fairly review the manuscripts and publish inspiring, innovative research results.