Abstract
Wind power prediction is the key technology to the safe dispatch and stable operation of power system with large-scale integration of wind power. In this work, based on the historical data of wind power, wind speed and temperature, the autoregressive moving average (ARMA) prediction model and the support vector machine (SVM) prediction model are established, particle swarm optimization (PSO) algorithm is involved for parameter optimization of SVM model. Furthermore, a hybrid PSO-SVM-ARMA prediction model based on ARMA and PSO-SVM model is illustrated for wind power prediction, and the covariance minimization method and PSO are employed to find the optimal weights. Moreover, with the basis of clustering theory, time series are clustered to examine the effective dataset for wind power prediction, and a clustered hybrid PSO-SVM-ARMA (C-PSO-SVM-ARMA) wind power prediction model is prospectively proposed. In case study, different prediction models are carried out and the prediction performance is examined based on different evaluation indices, the C-PSO-SVM-ARMA model shows better performance for wind power prediction with computational efficiency and satisfying precision.
Highlights
Wind power is facing a rapid development in recent 10 years
The C-particle swarm optimization (PSO)-support vector machine (SVM)-autoregressive moving average (ARMA) model can realize that the characteristics of the training dataset are similar to those of the predicted dataset based on clustering theory, and the effective dataset for prediction can ensure the prediction accuracy, while the computation complexity of the prediction algorithm is acceptable
According to Table. 4, I-PSO-SVM-ARMA and C-PSO-SVM-ARMA models are comparable for each cluster, and they are overall better than PSO-SVM-ARMA model from the comparison of mean value of root mean squared error (RMSE)
Summary
Wind power is facing a rapid development in recent 10 years. The latest GWEC report shows that the new installation of wind power of onshore and offshore is expected to be more than 55 GW each year until 2023 [1]. Taking into account the stochastic characteristics of wind power, power system needs to meet the safe dispatch and stable operation of power grid while ensuring wind power consumption as much as possible To solve these problems, one solution is to increase the reserve capacity of classical generators. Heuristic technologies including support vector machine (SVM), artificial neural network (ANN), ant colony algorithm, and fuzzy logic algorithm are widely involved to wind power prediction [12]–[17]. Among these methods, SVM can provide prediction result based on limited set of information, it is useful when the parameters are optimized by other intelligent methods.
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