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.

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 A Hybrid Method for Ultrashort-Term Wind Power Prediction considering Meteorological Features and Seasonal Information

2020 ◽  
Vol 2020 ◽  
pp. 1-12
Author(s):  
Baobin Zhou ◽  
Che Liu ◽  
Jianjing Li ◽  
Bo Sun ◽  
Jun Yang ◽  
...  
Keyword(s):  
Wind Power ◽  
Hybrid Method ◽  
Short Term Memory ◽  
Moving Average ◽  
Principal Component ◽  
Ensemble Empirical Mode Decomposition ◽  
Power Prediction ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Meteorological Features

High-precision wind power prediction is important for the planning, economics, and security maintenance of a power grid. Meteorological features and seasonal information are strongly related to wind power prediction. This paper proposes a hybrid method for ultrashort-term wind power prediction considering meteorological features (wind direction, wind speed, temperature, atmospheric pressure, and humidity) and seasonal information. The wind power data are decomposed into stationary subsequences using the ensemble empirical mode decomposition (EEMD). The principal component analysis (PCA) is used to reduce the redundant meteorological features and the algorithm complexity. With the stationary subsequences and extracted meteorological features data as inputs, the long short-term memory (LSTM) network is used to complete the wind power prediction. Finally, the seasonal autoregressive integrated moving average (SARIMA) is innovatively used to fit seasonal features (quarterly and monthly) of wind power and reconstruct the prediction results of LSTM. The proposed method is used to predict 15-minute wind power. In this study, three datasets were collected from a windfarm in Laizhou to validate the prediction performance of the proposed method. The experimental results showed that the prediction accuracy was significantly improved when meteorological features were considered and further improved with seasonal correction.

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.

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.

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