scholarly journals Forecasting the wind power generation in China by seasonal grey forecasting model based on collaborative optimization

2021 ◽  
Author(s):  
Aodi Sui ◽  
Wuyong Qian
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Prediction Method ◽  
Small Sample ◽  
Wind Power Generation ◽  
Collaborative Optimization ◽  
Grey Prediction ◽  
Model Based ◽  
Proposed Model ◽  
The Future

Renewable energy represented by wind energy plays an increasingly important role in China's national energy system. The accurate prediction of wind power generation is of great significance to China's energy planning and power grid dispatch. However, due to the late development of the wind power industry in China and the lag of power enterprise information, there are little historical data available at present. Therefore, the traditional large sample prediction method is difficult to be applied to the forecasting of wind power generation in China. For this kind of small sample and poor information problem, the grey prediction method can give a good solution. Thus, given the seasonal and long memory characteristics of the seasonal wind power generation, this paper constructs a seasonal discrete grey prediction model based on collaborative optimization. On the one hand, the model is based on moving average filtering algorithm to realize the recognition of seasonal and trend features. On the other hand, based on the optimization of fractional order and initial value, the collaborative optimization of trend and season is realized. To verify the practicability and accuracy of the proposed model, this paper uses the model to predict the quarterly wind power generation of China from 2012Q1 to 2020Q1, and compares the prediction results with the prediction results of the traditional GM(1,1) model, SGM(1,1) model and Holt-Winters model. The results are shown that the proposed model has a strong ability to capture the trend and seasonal fluctuation characteristics of wind power generation. And the long-term forecasts are valid if the existing wind power expansion capacity policy is maintained in the next four years. Based on the forecast of China’s wind power generation from 2021Q2 to 2024Q2 in the future, it is predicted that China's wind power generation will reach 239.09 TWh in the future, which will be beneficial to the realization of China's energy-saving and emission reduction targets.

Prediction of Wind Speed, Potential Wind Power, and the Associated Uncertainties for Offshore Wind Farm Using Deep Learning

2020 ◽  
Author(s):  
Do-Eun Choe ◽  
Gary Talor ◽  
Changkyu Kim
Keyword(s):  
Power Generation ◽  
Deep Learning ◽  
Wind Speed ◽  
Wind Power ◽  
Wind Power Generation ◽  
Offshore Wind ◽  
Weather Data ◽  
Local Wind ◽  
Offshore Wind Power ◽  
The Future

Abstract Floating offshore wind turbines hold great potential for future solutions to the growing demand for renewable energy production. Thereafter, the prediction of the offshore wind power generation became critical in locating and designing wind farms and turbines. The purpose of this research is to improve the prediction of the offshore wind power generation by the prediction of local wind speed using a Deep Learning technique. In this paper, the future local wind speed is predicted based on the historical weather data collected from National Oceanic and Atmospheric Administration. Then, the prediction of the wind power generation is performed using the traditional methods using the future wind speed data predicted using Deep Learning. The network layers are designed using both Long Short-Term Memory (LSTM) and Bi-directional LSTM (BLSTM), known to be effective on capturing long-term time-dependency. The selected networks are fine-tuned, trained using a part of the weather data, and tested using the other part of the data. To evaluate the performance of the networks, a parameter study has been performed to find the relationships among: length of the training data, prediction accuracy, and length of the future prediction that is reliable given desired prediction accuracy and the training size.

scholarly journals Research on short-term wind power Prediction of GRU based on similar days

2021 ◽  
Vol 2087 (1) ◽  
pp. 012089
Author(s):  
Yong Lin ◽  
Haiing Zhang ◽  
JiYan Liu ◽  
WenJie Ju ◽  
JinYou Wang ◽  
...  
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Prediction Accuracy ◽  
Prediction Method ◽  
Wind Power Generation ◽  
Power Prediction ◽  
Plot Analysis ◽  
Wind Power Prediction ◽  
Linear Smoothing ◽  
Grey Relation

Abstract As the proportion of wind power generation continues to increase, accurate forecasting of wind power output is of great significance to the smooth operation of the entire power grid. However, due to the greater impact of environmental factors, wind power generation has strong randomness, and it becomes difficult to accurately predict the power generation. Thus, a new hybrid model for wind power generation prediction combining GRU neural networks and similar days’ characters analysis is proposed to address solve this problem. The prediction method employs grey relation analysis to screen similar days, which not only reduces the amount of data required to train the model, reduces the computational complexity, and improves the training speed, but also improves the prediction accuracy based on the selected datasets. In addition, this method also filters and processes the data through box-plot analysis and linear smoothing, which further improves the prediction accuracy of the model. The results show that compared with a single GRU network, the MAE of this method has dropped by 1.89, RMSE has dropped by 1.9, and MAPE has dropped by 11.07%. Obviously, the prediction model based on similar days extraction has obvious advantages.

Novel Synchronous Generator for Wind Power Generation

2014 ◽  
Vol 2 ◽  
pp. 170-173
Author(s):  
Tsuyoshi Higuchi ◽  
Yuichi Yokoi
Keyword(s):  
Power Generation ◽  
Wind Power ◽  
Synchronous Generator ◽  
Wind Power Generation
Sign in / Sign up

Export Citation Format

Share Document