Abstract
Improving the accuracy of wind power forecasting is an important measure to deal with the uncertainty and volatility of wind power. Wind speed and wind direction are the most important factors affecting the power generation of wind turbines. In this paper, we propose a wind power forecasting method that combines the sparrow search algorithm (SSA) with the deep extreme learning machine (DELM). Based on the DELM model, the length of the time series’ influence on the performance of the neural network is validated through the comparison of the forecast error indexes, and the optimal time series length of the wind power is determined. The sparrow search algorithm is used to optimize its parameters to solve the problem of random changes in model input weights and thresholds. The proposed SSA-DELM model is validated using the measured data of a certain wind turbine, and various forecasting indexes are compared with several current wind power forecasting methods. The experimental results show that the proposed model has better performance in ultra-short-term wind power forecasting, and its coefficient of determination (R²), mean absolute error (MAE), and root mean square error (RMSE) are 0.927, 69.803, and 115.446, respectively.
Highlights
At present, countries all over the world are paying more attention to the development and use of renewable energy such as wind energy, solar energy, and geothermal energy [1]
The method of optimizing the length of the time series is explained in detail; The sparrow search algorithm is combined with the deep extreme learning machine to forecast wind power for the first time
Compared with DE-deep extreme learning machine (DELM), particle swarm optimization algorithm (PSO)-DELM, and WADELM, the model proposed in this paper reduces root mean square error (RMSE) indicators by 1.726%, 0.686%, and 0.609%, respectively
Summary
Countries all over the world are paying more attention to the development and use of renewable energy such as wind energy, solar energy, and geothermal energy [1]. Literature [30] proposed a short-term wind power prediction method based on a whale algorithm optimization support vector machine. This model overcomes the shortcomings of support vector machines that are easy to fall into local minima and uses a whale algorithm to optimize the penalty coefficient and kernel parameters of SVM. The method of optimizing the length of the time series is explained in detail; The sparrow search algorithm is combined with the deep extreme learning machine to forecast wind power for the first time.
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