Wind power prediction based on wavelet denoising and improved slime mold algorithm optimized support vector machine

2021 ◽  
pp. 0309524X2110568
Author(s):  
Lian Lian ◽  
Kan He
Keyword(s):  
Support Vector Machine ◽  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Power Grid ◽  
Wavelet Denoising ◽  
Slime Mold ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction

The accuracy of wind power prediction directly affects the operation cost of power grid and is the result of power grid supply and demand balance. Therefore, how to improve the prediction accuracy of wind power is very important. In order to improve the prediction accuracy of wind power, a prediction model based on wavelet denoising and improved slime mold algorithm optimized support vector machine is proposed. The wavelet denoising algorithm is used to denoise the wind power data, and then the support vector machine is used as the prediction model. Because the prediction results of support vector machine are greatly affected by model parameters, an improved slime mold optimization algorithm with random inertia weight mechanism is used to determine the best penalty factor and kernel function parameters in support vector machine model. The effectiveness of the proposed prediction model is verified by using two groups actually collected wind power data. Seven prediction models are selected as the comparison model. Through the comparison between the predicted value and the actual value, the prediction error and its histogram distribution, the performance indicators, the Pearson’s correlation coefficient, the DM test, box-plot distribution, the results show that the proposed prediction model has high prediction accuracy.

scholarly journals Ultra-Short-Term Multistep Prediction of Wind Power Based on Representative Unit Method

2018 ◽  
Vol 2018 ◽  
pp. 1-11
Author(s):  
Mao Yang ◽  
Lei Liu ◽  
Yang Cui ◽  
Xin Su
Keyword(s):  
Prediction Model ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Large Scale ◽  
Power Grid ◽  
Support Vector ◽  
Power Prediction ◽  
Short Term ◽  
Wind Power Prediction ◽  
Wide Range

With the continuous expansion of wind power grid scale, wind power prediction is an important means to reduce the adverse impact of large-scale grid integration on power grid: the higher prediction accuracy, the better safety, and economy of grid operation. The existing research shows that the quality of input sample data directly affects the accuracy of wind power prediction. By the analysis of measured power data in wind farms, this paper proposes an ultra-short-term multistep prediction model of wind power based on representative unit method, which can fully excavate data information and select reasonable data samples. It uses the similarity measure of time series in data mining, spectral clustering, and correlation coefficient to select the representative units. The least squares support vector machine (LSSVM) model is used as a prediction model for outputs of the representative units. The power of the whole wind farm is obtained by statistical upscaling method. And the number of representative units has a certain impact on prediction accuracy. The case study shows that this method can effectively improve the prediction accuracy, and it can be used as pretreatment method of data. It has a wide range of adaptability.

Wind power prediction method based on hybrid kernel function support vector machine

2017 ◽  
Vol 42 (3) ◽  
pp. 252-264 ◽  
Author(s):  
Zhongda Tian ◽  
Shujiang Li ◽  
Yanhong Wang ◽  
Xiangdong Wang
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Kernel Function ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Support Vector ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Hybrid Kernel Function ◽  
Hybrid Kernel

The prediction accuracy of wind power affects the operation cost of the power grid, which is a direct result of the supply and demand balance of the grid. Therefore, how to improve the prediction accuracy of wind power is very important. Considering the prediction accuracy of current prediction methods is not high, a wind power prediction method based on a hybrid kernel function support vector machine is proposed. On the basis of the exhibited characteristics of different kernel functions, the hybrid kernel function is a linear combination of the radial basis function and the polynomial kernel function. The hybrid kernel function is selected as the kernel function of support vector machine. The global kernel function is used to fit the correlation of the distant sample data, while the partial kernel function is used to fit the correlation of the data in neighboring fields. The generalization performance of the support vector machine model is improved. At the same time, an improved particle swarm optimization algorithm is introduced to determine the optimal parameters of the hybrid kernel function and support vector machine prediction model. Finally, the built prediction model is used to predict the wind power. The simulation results demonstrate that the proposed prediction method has better prediction accuracy for wind power.

scholarly journals Short-Term Wind Power Prediction Based on Improved Chicken Algorithm Optimization Support Vector Machine

2019 ◽  
Vol 11 (2) ◽  
pp. 512 ◽  
Author(s):  
Chao Fu ◽  
Guo-Quan Li ◽  
Kuo-Ping Lin ◽  
Hui-Juan Zhang
Keyword(s):  
Support Vector Machine ◽  
Renewable Energy ◽  
Wind Power ◽  
Support Vector ◽  
Algorithm Optimization ◽  
Power Prediction ◽  
Short Term ◽  
Swarm Optimization ◽  
Wind Power Prediction ◽  
Svm Model

Renewable energy technologies are essential contributors to sustainable energy including renewable energy sources. Wind energy is one of the important renewable energy resources. Therefore, efficient and consistent utilization of wind energy has been an important issue. The wind speed has the characteristics of intermittence and instability. If the wind power is directly connected to the grid, it will impact the voltage and frequency of the power system. Short-term wind power prediction can reduce the impact of wind power on the power grid and the stability of power system operation is guaranteed. In this study, the improved chicken swarm algorithm optimization support vector machine (ICSO-SVM) model is proposed to predict the wind power. The traditional chicken swarm optimization algorithm (CSO) easily falls into a local optimum when solving high-dimensional problems due to its own characteristics. So the CSO algorithm is improved and the ICSO algorithm is developed. In order to verify the validity of the ICSO-SVM model, the following work has been done. (1) The particle swarm optimization (PSO), ICSO, CSO and differential evolution algorithm (DE) are tested respectively by four standard testing functions, and the results are compared. (2) The ICSO-SVM and CSO-SVM models are tested respectively by two sets of wind power data. This study draws the following conclusions: (1) the PSO, CSO, DE and ICSO algorithms are tested by the four standard test functions and the test data are analyzed. By comparing it with the other three optimization algorithms, the ICSO algorithm has the best convergence effect. (2) The number of training samples has an obvious impact on the prediction results. The average relative error percentage and root mean square error (RMSE) values of the ICSO model are smaller than those of CSO-SVM model. Therefore, the ICSO-SVM model can efficiently provide credible short-term predictions for wind power forecasting.

Application of the Combination Prediction Model in Forecasting the Short-Term Wind Power

2015 ◽  
Vol 733 ◽  
pp. 893-897
Author(s):  
Peng Yu Zhang
Keyword(s):  
Neural Network ◽  
Prediction Model ◽  
Wind Power ◽  
Pso Algorithm ◽  
Support Vector ◽  
Generalized Regression Neural Network ◽  
Power Prediction ◽  
Short Term ◽  
Combination Forecasting ◽  
Wind Power Prediction

The accuracy of short-term wind power forecast is important for the power system operation. Based on the real-time wind power data, a wind power prediction model using wavelet neural network (WNN) is proposed. In order to overcome such disadvantages of WNN as easily falling into local minimum, this paper put forward using Particle Swarm Optimization (PSO) algorithm to optimize the weight and threshold of WNN. It’s advisable to use Support Vector Machine (SVM) to comparatively do prediction and put two outcomes as input vector for Generalized Regression Neural Network (GRNN) to do nonlinear combination forecasting. Simulation shows that combination prediction model can improve the accuracy of the short-term wind power prediction.

Research on SVM Wind Power Prediction Based on Heuristic Algorithm

2013 ◽  
Vol 333-335 ◽  
pp. 1233-1238
Author(s):  
Jing Wang ◽  
Yu Zhang ◽  
Kun Xia ◽  
Qiang Qiang Wang
Keyword(s):  
Wind Power ◽  
Heuristic Algorithm ◽  
Prediction Accuracy ◽  
Heuristic Algorithms ◽  
Support Vector ◽  
Search Efficiency ◽  
Power Prediction ◽  
Wind Power Prediction ◽  
Svm Model

With the disadvantages of volatility, intermittent and randomness of wind power, a research on constructing a fairly accurate prediction model is imperative to improve the quality of power system. Considering the optimization ability of heuristic algorithm and the regression ability of support vector machine, a HA-SVM model is constructed.Case study shows that, compared with other heuristic algorithms, the search efficiency and speed of differential evolution are good, and the prediction accuracy of the model is high.

A Method for Forecasting Short-Term Wind Speed Based on EMD and SVM

2013 ◽  
Vol 392 ◽  
pp. 622-627 ◽  
Author(s):  
Xiao Jing Dang ◽  
Hao Yong Chen ◽  
Xiao Ming Jin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Support Vector ◽  
Intrinsic Mode Functions ◽  
Power Prediction ◽  
Forecasting Accuracy ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Svm Model

In this paper, a method for wind speed forecasting based on Empirical Mode Decomposition and Support Vector machine is proposed. Compared with the approach based on Support Vector machine only, the method in this paper use EMD to decompose the data of wind power into several independent intrinsic mode functions (IMF),then model each component with the SVM model and get the final value of the overall wind power prediction. Experiments show the efficiency of the approach with a higher forecasting accuracy.

scholarly journals Application of Ensemble Empirical Mode Decomposition based Support Vector Regression Model for Wind Power Prediction

2020 ◽  
Vol 22 (1) ◽  
pp. 11-16
Author(s):  
Irene Karijadi ◽  
Ig. Jaka Mulyana
Keyword(s):  
Prediction Model ◽  
Support Vector Regression ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Ensemble Empirical Mode Decomposition ◽  
System Stability ◽  
Support Vector ◽  
Power Prediction ◽  
Mode Decomposition ◽  
Wind Power Prediction

Improving accuracy of wind power prediction is important to maintain power system stability. However, wind power prediction is difficult due to randomness and high volatility characteristics. This study applies a hybrid algorithm that combines ensemble empirical mode decomposition (EEMD) and support vector regression (SVR) to develop a prediction model for wind power prediction. Ensemble empirical mode decomposition is employed to decompose original data into several Intrinsic Mode Functions (IMF). Finally, a prediction model using support vector regression is built for each IMF individually, and the prediction result of all IMFs is combined to obtain an aggregated output of wind power Numerical testing demonstrated that the proposed method can accurately predict the wind power in Belgian.

scholarly journals Short-term wind power prediction based on hybrid variational mode decomposition and least squares support vector machine optimized by improved salp swarm algorithm model

2021 ◽  
Vol 2125 (1) ◽  
pp. 012012
Author(s):  
Zhongde Su ◽  
Huacai Lu
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Variational Mode Decomposition ◽  
Power Prediction ◽  
Short Term ◽  
Salp Swarm Algorithm ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Swarm Algorithm

Abstract To improve the accuracy of wind power prediction, a short-term wind power prediction model based on variational mode decomposition (VMD) and improved salp swarm algorithm (ISSA) optimized least squares support vector machine (LSSVM) is proposed. In the model, the variational modal decomposition is used to decompose the wind power sequence into multiple eigenmode components with limited bandwidth. The improved salp swarm algorithm is employed to tune the regularization parameter and kernel parameter in LSSVM. The proposed wind power prediction strategy using mean one-hour historical wind power data collected from a wind farm located in zhejiang, China. Compared with other prediction models illustrate the better prediction performance of VMD-ISSA-LSSVM.

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