Short-term wind speed forecasting model for wind farm based on wavelet decomposition

2008 ◽  
Author(s):  
Cao Lei ◽  
Li Ran
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Forecasting Model ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Multistep Wind Speed Forecasting Based on Wavelet and Gaussian Processes

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Niya Chen ◽  
Zheng Qian ◽  
Xiaofeng Meng
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Southern China ◽  
Energy Utilization ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Hourly Data ◽  
Short Term Forecasting ◽  
Gp Model

Accurate wind speed forecasts are necessary for the safety and economy of the renewable energy utilization. The wind speed forecasts can be obtained by statistical model based on historical data. In this paper, a novel W-GP model (wavelet decomposition based Gaussian process learning paradigm) is proposed for short-term wind speed forecasting. The nonstationary and nonlinear original wind speed series is first decomposed into a set of better-behaved constitutive subseries by wavelet decomposition. Then these sub-series are forecasted respectively by GP method, and the forecast results are summed to formulate an ensemble forecast for original wind speed series. Therefore, the previous process which obtains wind speed forecast result is named W-GP model. Finally, the proposed model is applied to short-term forecasting of the mean hourly and daily wind speed for a wind farm located in southern China. The prediction results indicate that the proposed W-GP model, which achieves a mean 13.34% improvement in RMSE (Root Mean Square Error) compared to persistence method for mean hourly data and a mean 7.71% improvement for mean daily wind speed data, shows the best forecasting accuracy among several forecasting models.

A Hybrid Short-Term Wind Speed Forecasting Model Based on Wavelet Decomposition and Extreme Learning Machine

2013 ◽  
Vol 860-863 ◽  
pp. 361-367 ◽  
Author(s):  
Yi Hui Zhang ◽  
He Wang ◽  
Zhi Jian Hu ◽  
Kai Wang ◽  
Yan Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Extreme Learning Machine ◽  
Wavelet Decomposition ◽  
Forecasting Model ◽  
Short Term ◽  
Machine Model ◽  
Wind Speed Prediction ◽  
Speed Prediction ◽  
Learning Machine ◽  
Short Term Prediction

This paper studied the short-term prediction of wind speed by means of wavelet decomposition and Extreme Learning Machine. Wind speed signal was decomposed into several sequences by wavelet decomposition to reduce the non-stationary. Secondly, the phase space reconstructed was used to mine sequences characteristics, and then an improved extreme learning machine model of each component was established. Finally, the results of each component forecast superimposed to get the final result. The simulation result verified that the hybrid model effectively improved the wind speed prediction accuracy.

scholarly journals Multi-time scale wind speed prediction based on WT-bi-LSTM

2020 ◽  
Vol 309 ◽  
pp. 05011
Author(s):  
Jinyong Xiang ◽  
Zhifeng Qiu ◽  
Qihan Hao ◽  
Huhui Cao
Keyword(s):  
Wind Speed ◽  
Wavelet Decomposition ◽  
Wind Farm ◽  
Short Term Memory ◽  
Short Term ◽  
Wind Speed Prediction ◽  
Proposed Model ◽  
Speed Prediction ◽  
Long Short Term Memory ◽  
Auto Regression

The accurate and reliable wind speed prediction can benefit the wind power forecasting and its consumption. As a continuous signal with the high autocorrelation, wind speed is closely related to the past and future moments. Therefore, to fully use the information of two direction, an auto-regression model based on the bi-directional long short term memory neural network model with wavelet decomposition (WT-bi-LSTM) is built to predict the wind speed at multi-time scales. The proposed model are validated by using the actual wind speed series from a wind farm in China. The validation results demonstrated that, compared with other four traditional models, the proposed strategy can effectively improve the accuracy of wind speed prediction.

Short-term wind speed forecasting in wind farm based on C-C and ELM

2017 ◽  
Author(s):  
Dongshuai Kang ◽  
Yingying Su ◽  
Xinghua Liu ◽  
Huabin Wang ◽  
Cuiying Li ◽  
...  
Keyword(s):  
Wind Speed ◽  
Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

scholarly journals Short-Term Wind Speed Forecasting Based on Hybrid Variational Mode Decomposition and Least Squares Support Vector Machine Optimized by Bat Algorithm Model

2019 ◽  
Vol 11 (3) ◽  
pp. 652 ◽  
Author(s):  
Qunli Wu ◽  
Huaxing Lin
Keyword(s):  
Support Vector Machine ◽  
Wind Speed ◽  
Least Squares ◽  
Bat Algorithm ◽  
Support Vector ◽  
Forecasting Model ◽  
Variational Mode Decomposition ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Mode Decomposition

With the integration of wind energy into electricity grids, wind speed forecasting plays an important role in energy generation planning, power grid integration and turbine maintenance scheduling. This study proposes a hybrid wind speed forecasting model to enhance prediction performance. Variational mode decomposition (VMD) was applied to decompose the original wind speed series into different sub-series with various frequencies. A least squares support vector machine (LSSVM) model with the pertinent parameters being optimized by a bat algorithm (BA) was established to forecast those sub-series extracted from VMD. The ultimate forecast of wind speed can be obtained by accumulating the prediction values of each sub-series. The results show that: (a) VMD-BA-LSSVM displays better capacity for the prediction of ultra short-term (15 min) and short-term (1 h) wind speed forecasting; (b) the proposed forecasting model was compared with wavelet decomposition (WD) and ensemble empirical mode decomposition (EEMD), and the results indicate that VMD has stronger decomposition ability than WD and EEMD, thus, significant improvements in forecasting accuracy were obtained with the proposed forecasting models compared with other forecasting methods.

scholarly journals Artificial Combined Model Based on Hybrid Nonlinear Neural Network Models and Statistics Linear Models—Research and Application for Wind Speed Forecasting

2018 ◽  
Vol 10 (12) ◽  
pp. 4601 ◽  
Author(s):  
Yuewei Liu ◽  
Shenghui Zhang ◽  
Xuejun Chen ◽  
Jianzhou Wang
Keyword(s):  
Neural Network ◽  
Time Series ◽  
Wind Speed ◽  
Wind Farm ◽  
Linear Models ◽  
Search Algorithm ◽  
Forecasting Model ◽  
Neural Network Models ◽  
Wind Speed Forecasting ◽  
Model Based

The use of wind power is rapidly increasing as an important part of power systems, but because of the intermittent and random nature of wind speed, system operators and researchers urgently need to find more reliable methods to forecast wind speed. Through research, it is found that the time series of wind speed demonstrate not only linear features but also nonlinear features. Hence, a combined forecasting model based on an improved cuckoo search algorithm optimizes weight, and several single models—linear model, hybrid nonlinear neural network, and fuzzy forecasting model—are developed in this paper to provide more trend change for time series of wind speed forecasting besides improving the forecasting accuracy. Furthermore, the effectiveness of the proposed model is proved by wind speed data from four wind farm sites and the results are more reliable and accurate than comparison models.

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