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.

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

A deep learning-based evolutionary model for short-term wind speed forecasting: A case study of the Lillgrund offshore wind farm

2021 ◽  
Vol 236 ◽  
pp. 114002
Author(s):  
Mehdi Neshat ◽  
Meysam Majidi Nezhad ◽  
Ehsan Abbasnejad ◽  
Seyedali Mirjalili ◽  
Lina Bertling Tjernberg ◽  
...  
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Farm ◽  
Evolutionary Model ◽  
Offshore Wind ◽  
Offshore Wind Farm ◽  
Short Term ◽  
Wind Speed Forecasting

Ultra-Short-Term Forecasting Based on Phase Space Reconstruction and Wind Power Wind

2015 ◽  
Vol 713-715 ◽  
pp. 1444-1447
Author(s):  
De Yin Du ◽  
Bao Fan Chen
Keyword(s):  
Phase Space ◽  
Wind Speed ◽  
Power Systems ◽  
Wind Power ◽  
Wind Farm ◽  
Wind Farms ◽  
Stable Operation ◽  
Short Term ◽  
Short Term Forecasting ◽  
Turbine Output

The amount of random variation of wind speed, wind turbine output power are volatile, a lot of wind power will be on the safe and stable operation of power systems and power quality pose serious challenges, so the wind farm wind speed and power generation forecast scheduling and management of wind farms play an important role. According wind with chaotic discuss the use of phase space CC method to reconstruct the chaotic time series, and the phase space of a wind farm 10 units were reconstructed using the weighted first order local prediction model to obtain short-term within 1h wind forecast values obtained using the power curve conversion method of generating power for each unit. By examples show that the proposed method is feasible and effective.

scholarly journals Short-term wind speed forecasting system using deep learning for wind turbine applications

2020 ◽  
Vol 10 (6) ◽  
pp. 5779
Author(s):  
Gokhan Erdemir ◽  
Aydin Tarik Zengin ◽  
Tahir Cetin Akinci
Keyword(s):  
Deep Learning ◽  
Wind Speed ◽  
Wind Turbine ◽  
Wind Turbines ◽  
Learning Algorithm ◽  
Integrated System ◽  
Short Term ◽  
Wind Speed Forecasting ◽  
Short Term Forecasting

It is very important to accurately detect wind direction and speed for wind energy that is one of the essential sustainable energy sources. Studies on the wind speed forecasting are generally carried out for long-term predictions. One of the main reasons for the long-term forecasts is the correct planning of the area where the wind turbine will be built due to the high investment costs and long-term returns. Besides that, short-term forecasting is another important point for the efficient use of wind turbines. In addition to estimating only average values, making instant and dynamic short-term forecasts are necessary to control wind turbines. In this study, short-term forecasting of the changes in wind speed between 1-20 minutes using deep learning was performed. Wind speed data was obtained instantaneously from the feedback of the emulated wind turbine's generator. These dynamically changing data was used as an input of the deep learning algorithm. Each new data from the generator was used as both test and training input in the proposed approach. In this way, the model accuracy and enhancement were provided simultaneously. The proposed approach was turned into a modular independent integrated system to work in various wind turbine applications. It was observed that the system can predict wind speed dynamically with around 3% error in the applications in the test setup applications.

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