ARIMA METHODOLOGY APPLIED TO DEVELOP A VERY SHORT-TERM WIND POWER FORECAST MODEL FOR THE PALMAS WIND FARM (BRAZIL)

Focusing on short-term wind power forecast, a method based on the combination of Genetic Algorithm (GA) and Extreme Learning Machine (ELM) has been proposed. Firstly, the GA was used to prepossess the data and effectively extract the input of model in feature space. Basis on this, the ELM was used to establish the forecast model for short-term wind power. Then, the GA was used to optimize the activation function of hidden layer nodes, the offset, the input weights, and the regularization coefficient of extreme learning, thus obtaining the GA-ELM algorithm. Finally, the GA-ELM was applied to the short-term wind power forecast for a certain area. Compared with single ELM, Elman algorithms, the experimental results show that the GA-ELM algorithm has higher prediction accuracy and better ability for generalization.

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Wind power forecast based on improved Long Short Term Memory network

Energy ◽

10.1016/j.energy.2019.116300 ◽

2019 ◽

Vol 189 ◽

pp. 116300 ◽

Cited By ~ 9

Author(s):

Li Han ◽

Huitian Jing ◽

Rongchang Zhang ◽

Zhiyu Gao

Keyword(s):

Wind Power ◽

Short Term Memory ◽

Short Term ◽

Term Memory ◽

Wind Power Forecast ◽

Memory Network ◽

Long Short Term Memory

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Short-Term Spatio-Temporal Wind Power Forecast in Robust Look-ahead Power System Dispatch

IEEE Transactions on Smart Grid ◽

10.1109/tsg.2013.2282300 ◽

2014 ◽

Vol 5 (1) ◽

pp. 511-520 ◽

Cited By ~ 128

Author(s):

Le Xie ◽

Yingzhong Gu ◽

Xinxin Zhu ◽

Marc G. Genton

Keyword(s):

Power System ◽

Wind Power ◽

Short Term ◽

Look Ahead ◽

Wind Power Forecast ◽

Spatio Temporal

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Optimal Operation Planning of Wind Farm Installed BESS Using Wind Power Forecast Data of Wind Turbine Generators Considering Forecast Error

International Journal of Emerging Electric Power Systems ◽

10.1515/ijeeps-2012-0045 ◽

2013 ◽

Vol 14 (3) ◽

pp. 207-218 ◽

Cited By ~ 13

Author(s):

Kazuki Ogimi ◽

Shota Kamiyama ◽

Michael Palmer ◽

Atsushi Yona ◽

Tomonobu Senju ◽

...

Keyword(s):

Wind Turbine ◽

Wind Power ◽

Wind Farm ◽

Forecast Error ◽

Energy Resource ◽

Optimal Operation ◽

Turbine Generators ◽

Wind Turbine Generators ◽

Wind Power Forecast ◽

Forecast Data

Abstract In order to solve the problems of global warming and depletion of energy resource, renewable energy systems such as wind generation are getting attention. However, wind power fluctuates due to variation of wind speed, and it is difficult to perfectly forecast wind power. This paper describes a method to use power forecast data of wind turbine generators considering wind power forecast error for optimal operation. The purpose in this paper is to smooth the output power fluctuation of a wind farm and to obtain more beneficial electrical power for selling.

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Wind Power Short-Term Forcasting of BP Neural Network Based on the Small-World Optimization

Advanced Materials Research ◽

10.4028/www.scientific.net/amr.933.384 ◽

2014 ◽

Vol 933 ◽

pp. 384-389

Author(s):

Xin Zhao ◽

Shuang Xin Wang

Keyword(s):

Neural Network ◽

Wind Speed ◽

Wind Power ◽

Bp Neural Network ◽

Wind Direction ◽

Wind Farm ◽

Prediction Method ◽

Small World ◽

Power Method ◽

Short Term

Wind power short-term forcasting of BP neural network based on the small-world optimization is proposed. First, the initial data collected from wind farm are revised, and the unreasonable data are found out and revised. Second, the small-world optimization BP neural network model is proposed, and the model is used on the prediction method of wind speed and wind direction, and the prediction method of power. Finally, by simulation analysis, the NMAE and NRMSE of the power method are smaller than those of the wind speed and wind direction method when the wind power data of one hour later are predicted. When the power method are used to forecast the data one hour later, NMAE is 5.39% and NRMSE is 6.98%.

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