•A new improved RBF neural network model is proposed.•VMD-WT is used to extract features and remove noise of wind speed data.•The PCA-BP method is proposed to screen out the model input vectors.•The ...validity and applicability of the model are tested by data from two wind farms.
Accurate short-term wind power forecasting is significant for rational dispatching of the power grid and ensuring the power supply quality. In order to enhance the accuracy of short-term wind speed prediction, a hybrid model based on VMD-WT and PCA-BP-RBF neural network is proposed. In data pre-processing period, the non-stationary wind speed sequence is decomposed into a number of relatively stationary intrinsic mode functions (IMF) by variational mode decomposition (VMD); then WT algorithm is used to perform secondary denoising on each IMF. At the same time, several factors affecting wind speed are introduced, from which the input features that participated in the prediction are selected by PCA-BP method. Next, the RBF neural network is utilized to predict each IMF. Finally, all IMF prediction results are aggregated to obtain the final wind speed value. Combining the data of Spanish and Chinese wind farms, the experiment results show that: (1) compared with EMD, VMD-WT can better solve the problems of modal aliasing and endpoint effect, which can make the periodic characteristics of each IMF more obvious, then promote the forecasting performance; (2) using PCA-BP method to filter the model input data, the redundant and irrelevant information is eliminated, the complexity of the model is reduced, and the predictive performance of RBF model is improved; (3) compared with other traditional models, the hybrid model proposed in this paper has greatly improved the accuracy in short-term wind speed forecasting.
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In recent years, the global wind power construction is accelerating. Although wind power is a clean energy without pollution, its volatility and irregularity have a great impact on wind power ...integration. Therefore, scholars pay more and more attention to the ultra-short-term prediction of wind speed. At present, the popular wind speed prediction model usually combines wind speed decomposition algorithm, machine learning algorithm, and intelligent optimization algorithm. The general wind speed decomposition algorithm cannot use the information contained in the factors affecting wind speed. Besides, the current popular optimization algorithms, such as gray wolf optimization algorithm, have strong convergence and better optimization effect, but their structure is complex and their operation complexity is large. And the PSO algorithm has simple structure and fast operation speed. To solve the above problems, a novel combination prediction model is proposed in this paper. This model uses VMD to decompose the wind speed into high-frequency signal and low-frequency signal and then uses principal component analysis and spectral clustering to extract and classify the influencing factors. In addition, aiming at the problem of slow convergence speed in the later stage of PSO iteration, an adaptive improved PSO is proposed. Finally, this paper also designs a rolling train method to adjust the size of training samples. Through four experiments of wind speed series in two periods, it is proved that the combined prediction model proposed in this paper has the following advantages: the model fully extracts the information of wind speed and influencing factors; the improved PSO algorithm has better optimization effect; rolling training method effectively improves the prediction ability of the model; the combined forecasting model has good adaptability and competitiveness.
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The eigenmodes based on paraxial accelerating beams in nonlinear atomic vapors with Kerr and cubic-quintic nonlinearities are demonstrated from mathematical models and numerical simulations. Upon ...adjusting the generation and propagation conditions, these nonlinear accelerating beams exhibit different evolution properties. We show numerically that the adopted beams can propagate robustly in the medium regardless of its absorption properties. The shape and peak intensity of the main lobes of these beams, based on the fact that they are the eigenmodes of the nonlinear Schrödinger equation in atomic media, are preserved for a significantly long propagation distance. If such beams are not the modes of the system, they are subject to the under-healing or over-healing effect, which damages the shape of the self-accelerating beams. In a numerical investigation, we also discuss the interactions between truncated accelerating beams, which readily generate non-accelerating solitons and soliton pairs.
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With the characteristics of randomness, fluctuation, nonlinearity and uncertainty, wind speed affects the stability of wind power system. In order to improve the safety and stability of wind power ...system, accurate and effective wind speed prediction is essential. In the paper, a novel wind speed prediction method based on wind speed characteristics is proposed. Firstly, VMD is used to decompose wind speed into the nonlinear part, the linear part and the noise part. Nonlinear part reflects the nonlinear characteristic of wind speed, linear part embodies the linear process of wind speed formation, noise part is the error (ER) sequence decomposed wind speed by VMD. According to the characteristics of different parts, different models are built, PCA-RBF model is built for the nonlinear part, ARMA model under the MCMC framework is built for the linear part, and probability distribution is fitted for the noise part. These three parts are combined to establish VMD-PRBF-ARMA-E model to make off-line deterministic prediction and uncertainty prediction. Then the superiority of VMD-PRBF-ARMA-E model is verified by comparing with other nonlinear models and time series models. At last, based on off-line scheme, VMD-PRBF-ARMA-E model is used to make real-time wind speed prediction. The deterministic prediction of VMD-PRBF-ARMA-E model has high accuracy, and can reflect the characteristics of wind speed well and truly, which can provide a scientific basis for the power grid dispatching department, and help to ensure the stability of wind power system.
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Nowadays, graph theory is an important analysis tool in mathematics and computer science. Because of the inherent simplicity of graph theory, it can be used to model many different physical and ...abstract systems such as transportation and communication networks, models for business administration, political science, and psychology and so on. The purpose of this book is not only to present the latest state and development tendencies of graph theory, but to bring the reader far enough along the way to enable him to embark on the research problems of his own. Taking into account the large amount of knowledge about graph theory and practice presented in the book, it has two major parts: theoretical researches and applications. The book is also intended for both graduate and postgraduate students in fields such as mathematics, computer science, system sciences, biology, engineering, cybernetics, and social sciences, and as a reference for software professionals and practitioners.
In the field of flexible electronics manufacturing, inkjet printing technology is a research hotspot, and it is key to developing low-temperature curing conductive inks that meet printing ...requirements and have suitable functions. Herein, methylphenylamino silicon oil (N75) and epoxy-modified silicon oil (SE35) were successfully synthesized through functional silicon monomers, and they were used to prepare silicone resin 1030H with nano SiO
. 1030H silicone resin was used as the resin binder for silver conductive ink. The silver conductive ink we prepared with 1030H has good dispersion performance with a particle size of 50-100 nm, as well as good storage stability and excellent adhesion. Additionally, the printing performance and conductivity of the silver conductive ink prepared with n,n-dimethylformamide (DMF): proprylene glycol monomethyl ether (PM) (1:1) as solvent are better than those of the silver conductive ink prepared by DMF and PM solvent. Cured at a low temperature of 160 °C, the resistivity of 1030H-Ag-82%-3 conductive ink is 6.87 × 10
Ω·m, and that of 1030H-Ag-92%-3 conductive ink is 0.564 × 10
Ω·m, so the low-temperature curing silver conductive ink has high conductivity. The low-temperature curing silver conductive ink we prepared meets the printing requirements and has potential for practical applications.
In order to meet the needs of wind speed prediction in wind farms, we consider the influence of random atmospheric disturbances on wind variations. Considering a simplified fluid convection mode, a ...Lorenz system can be employed as an atmospheric disturbance model. Here Lorenz disturbance is defined as the European norm of the solutions of the Lorenz equation. Grey generating and accumulated generating models are employed to explore the relationship between wind speed and its related disturbance series. We conclude that a linear or quadric polynomial generating model are optimal through the verification of short-term wind speed prediction in the Sotavento wind farm. The new proposed model not only greatly improves the precision of short-term wind speed prediction, but also has great significance for the maintenance and stability of wind power system operation.
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•A new hybrid prediction system is proposed to predict wind speed.•Simplex and chaotic mapping optimization cuckoo algorithm are proposed.•An uncertainty prediction model based on Monte Carlo method ...is proposed.•The energy theory is proposed to optimize the decomposition mode number.•Theories proofs and experiments verify the effectiveness of the model.
Wind energy has strong volatility and intermittent. Accurate wind speed prediction can not only improve the safety of the system, but also optimize dispatch and reduce economic losses. However, previous studies tend to ignore the influence of virtual components and lack effective identification of wind speed characteristics and a robust interval prediction scheme, resulting in poor results. To bridge these gaps, this paper proposes an energy theory method to solve the problem of modal over-decomposition. The study also combines effective modal recognition, uses different prediction methods according to modal characteristics and proposes a set of new optimization algorithms to improve nonlinear prediction capabilities. Finally, based on Monte Carlo theory, a set of interval prediction schemes that can adapt to different error characteristics are proposed. Under the verification of wind speed data in Changma, China and Sotavento, Spain. The mean absolute percentage error of wind speed deterministic prediction reaches 4.22% and 5.82%, respectively. The coverage rate of wind speed uncertainty prediction meets different confidence requirements, and the average interval width is still less than 2.5 m/s at 90% confidence. The results show that the forecasting system proposed in this paper is significantly better than all the comparative forecasting schemes, which can reduce the risk of fluctuations and improve the stability and safety of the wind power system.
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Wind power, as a potential new energy generation technology, is gradually developing towards to the mainstream energy in the world. However, the inherent random volatility of wind brings severe ...challenges to the safe operation of the grid and the reliability of power supply, one of the effective ways to solve the problem is to improve the accuracy of wind speed prediction. However, most of wind speed prediction model cannot well mine the inherent regularity of wind speed data. Therefore, this paper introduces variational mode decomposition (VMD) algorithm. And the Short-term Wind Speed Prediction Model based on GA-ANN improved by VMD is proposed, which can effectively improve the accuracy of wind speed prediction. Firstly, hierarchical cluster method in this paper is employed to extract the historical data with high similarity to the predicted day. And then the appropriate number of decompositions K is selected by judging the value of sample entropy, so that the extracted historical data is decomposed into K subsequences by the variational mode decomposition. Next, with the global optimization ability of genetic algorithm, the artificial neural network is optimized to improve the forecasting performance. Finally, the short-term wind speed forecasting model based on GA-ANN improved by VMD is employed to predict the wind speed of each subsequence and superimposed them to obtain the final wind speed prediction sequence. The results in this paper show that (1) the model can find the periodic fluctuation of wind speed through historical data by hierarchical cluster method, so that significantly improving the accuracy of short-term wind speed prediction; (2) for the wind speed prediction, the error value of GA-ANN model is smaller than that of BP neural network; (3) in view of the inherent nature of the wind, the model proposed in this paper can use VMD to decompose the wind speed signal to obtain different scale fluctuations or trends, so as to fully exploit the potential information of wind speed, and obtain more accurate prediction results. The research work can help the relevant departments of the power system to accurately assess the risk of power grid operation, make a reasonable generation plan, effectively reduce the cost of power operation, and then greatly promote the development of green energy.
•Proposing a new windspeedprediction model named HC-VMD-GA-BPmodel.•Using hierarchical cluster methodtoselect the historical wind speed data.•VMD is introduced to decomposethe original wind speed sequence.•Artificial neural networkisoptimized by GA to improve the prediction performance.•The HC-VMD-GA-BP model is comparedwith other models to verify itseffectiveness and high prediction accuracy.
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As an emerging clean energy, wind energy has become an important part of energy development all over the world. One of the major ways to use wind energy is wind power. Accurate wind power forecasting ...is significant to the wind energy development and utilization, and the power systems safe and stable operation. Due to the fluctuation and randomness of wind energy, improving the accuracy of ultra-short-term wind energy prediction has become the key to wind energy development and utilization, and it is also the focus of wind energy development research in various countries. Therefore, this paper proposes a new combination model based on complementary empirical mode decomposition (CEEMD), T-S fuzzy neural network (FNN) optimized by improved genetic algorithm (IGA) and Markov error correction to improve the accuracy of ultra-short-term wind power prediction. First, the CEEMD is used to decompose the wind data into several components; then, the trained IGA-FNN model is used to individually predict each modal component to improve accuracy and stability; finally, the prediction results of all modal components are superimposed and the Markov process is used for error correction to obtain the final prediction result. The empirical results show that the mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) of the proposed model is 15.59%, 17.95% and 6.94%, respectively. The empirical result proves that compared with the BPNN, Elman NN, and FNN, the prediction results MAE of the proposed method is reduced by 68 0.6%, 61.7%, 59.2%, the RMSE is reduced by 70.7%, 65.0%, 63.9%, the MAPE is reduced by 75.5%, 67.6%, 60.4%. The prediction accuracy of the proposed method is significantly higher, and it is available for wind power development and utilization.
•A novel wind speed prediction method is proposed, which is beneficial to development of wind energy.•The CEEMD decomposition method is used for decomposition which integrate the advantages of EMD and EEMD algorithms.•The GA method is improved to optimize the FNN, and the combination prediction model is used for wind prediction.•Introducing the Markov chain to correct the prediction error of wind speed.
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