Yongqian Liu

Publications (2)0 Total impact

• Conference Proceeding: Genetic algorithm-piecewise support vector machine model for short term wind power prediction

  Jie Shi, Yongping Yang, Peng Wang, Yongqian Liu, Shuang Han
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  ABSTRACT: Short term wind power prediction is one of the effective ways to cope with the operational problems caused by the variability of the wind energy resource when a large penetration of wind power is integrated into electric power systems. In this paper, a model combining a Genetic Algorithm with a Piecewise Support Vector Machine (GA-PSVM) is developed that improves the precision of short term wind power prediction systems based on power curves of the wind turbine generator systems. A Genetic Algorithm (GA) is used to search automatically for the parameters of a Piecewise Support Vector Machine (PSVM) model. The resulting GA-PSVM model can be used to predict wind power generation from one to six hours ahead. Operational data from a wind farm in North China are used to evaluate the proposed model. The results show that the mean relative errors (MRE) of GA-PSVM model are 2.03% lower than that of the standard SVM model applied to the same data set.
  Intelligent Control and Automation (WCICA), 2010 8th World Congress on; 08/2010
• Conference Proceeding: The Comparison of BP Network and RBF Network in Wind Power Prediction Application

  Shuang Han, Yongping Yang, Yongqian Liu
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  ABSTRACT: Wind power prediction is of great importance for the safety and stabilization of grids. Based on historical data, the application of BP and RBF network in 3 hours wind power prediction are compared. The comparisons are of network structure, network training speed and prediction results. Combined with BP and RBF network, two prediction routes were put forward to predict wind farm power. The results show that, for both BP and RBF network, the relative power prediction error is 11%-14% for each turbine and 8%-10% for the whole wind farm. The training speed and prediction precision of RBF network are superior to those of BP network and the best result is gotten by RBF network. RBF network is suitable for online wind power prediction.
  Bio-Inspired Computing: Theories and Applications, 2007. BIC-TA 2007. Second International Conference on; 10/2007