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 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.

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.

Wind Power Forecast Based on Artificial Bee Colony Algorithm

2015 ◽  
Vol 738-739 ◽  
pp. 417-422 ◽  
Author(s):  
Xin Zhang ◽  
Guo Chu Chen
Keyword(s):  
Support Vector Machine ◽  
Wind Power ◽  
Support Vector ◽  
Optimal Parameters ◽  
Power Prediction ◽  
Bee Colony ◽  
Forecast Precision ◽  
Wind Power Forecast ◽  
Wind Power Prediction ◽  
Svm Model

In view of the traditional support vector machine (SVM) model in wind power prediction parameter selection problems, this paper introduced a model which using artificial colony algorithm to seek the optimal parameters of support vector machine. The experimental results show that the SVM model of artificial swarm optimization application and prediction is effective, makes the forecast precision is improved.

scholarly journals Ultra-short-term wind speed forecasting based on support vector machine with combined kernel function and similar data

2019 ◽  
Vol 2019 (1) ◽  
Author(s):  
Jian He ◽  
Jingle Xu
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Wind Power ◽  
Kernel Functions ◽  
Support Vector ◽  
Similar Data ◽  
Power Prediction ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Wind Power Prediction

Abstract The accuracy of wind power prediction is very important for the stable operation of a power system. Ultra-short-term wind speed forecasting is an effective way to ensure real-time and accurate wind power prediction. In this paper, a short-term wind speed forecasting method based on a support vector machine with a combined kernel function and similar data is proposed. Similar training data are selected based on the wind tendency, and a combination of two kinds of kernel functions is applied in forecasting using a support vector machine. The forecasting results for a wind farm in Ningxia Province indicate that a combination of kernel functions with complementary advantages outperforms each single function, and forecasting models based on grouped wind data with a similar tendency could reduce the forecasting error. Furthermore, more accurate wind forecasting results ensure better wind power prediction.

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.

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.

scholarly journals A Review on Hybrid Empirical Mode Decomposition Models for Wind Speed and Wind Power Prediction

Energies ◽  
2019 ◽  
Vol 12 (2) ◽  
pp. 254 ◽  
Author(s):  
Neeraj Bokde ◽  
Andrés Feijóo ◽  
Daniel Villanueva ◽  
Kishore Kulat
Keyword(s):  
Wind Speed ◽  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Prediction Models ◽  
Wind Farms ◽  
Accurate Prediction ◽  
Intrinsic Mode Functions ◽  
Power Prediction ◽  
Planning And Scheduling ◽  
Mode Decomposition

Reliable and accurate planning and scheduling of wind farms and power grids to ensure sustainable use of wind energy can be better achieved with the use of precise and accurate prediction models. However, due to the highly chaotic, intermittent and stochastic behavior of wind, which means a high level of difficulty when predicting wind speed and, consequently, wind power, the evolution of models capable of narrating data of such a complexity is an emerging area of research. A thorough review of literature, present research overviews, and information about possible expansions and extensions of models play a significant role in the enhancement of the potential of accurate prediction models. The last few decades have experienced a remarkable breakthrough in the development of accurate prediction models. Among various physical, statistical and artificial intelligent models developed over this period, the models hybridized with pre-processing or/and post-processing methods have seen promising prediction results in wind applications. The present review is focused on hybrid empirical mode decomposition (EMD) or ensemble empirical mode decomposition (EEMD) models with their advantages, timely growth and possible future in wind speed and power forecasting. Over the years, the practice of EEMD based hybrid models in wind data predictions has risen steadily and has become popular because of the robust and accurate nature of this approach. In addition, this review is focused on distinct attributes including the evolution of EMD based methods, novel techniques of treating Intrinsic Mode Functions (IMFs) generated with EMD/EEMD and overview of suitable error measures for such studies.

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.

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