scholarly journals Erratum: “A novel combined forecasting model for short-term wind power prediction based on ensemble empirical mode decomposition and optimal virtual prediction” [J. Renewable Sustainable Energy 8, 013104 (2016)]

2016 ◽  
Vol 8 (1) ◽  
pp. 019902
Author(s):  
Kaipei Liu ◽  
Yachao Zhang ◽  
Liang Qin
Keyword(s):  
Wind Power ◽  
Empirical Mode Decomposition ◽  
Sustainable Energy ◽  
Ensemble Empirical Mode Decomposition ◽  
Forecasting Model ◽  
Power Prediction ◽  
Short Term ◽  
Mode Decomposition ◽  
Wind Power Prediction ◽  
Combined Forecasting

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 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.

scholarly journals Research on Short-Term Wind Power Prediction Based on Combined Forecasting Models

2016 ◽  
Vol 70 ◽  
pp. 09005
Author(s):  
Chi Zhang ◽  
Jie Zeng ◽  
Ning Xie ◽  
Ping Yang ◽  
Yujia Zhang ◽  
...  
Keyword(s):  
Wind Power ◽  
Power Prediction ◽  
Short Term ◽  
Forecasting Models ◽  
Wind Power Prediction ◽  
Combined Forecasting
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