中文摘要：

在最近的年里，风力量预言的精确性着急地被学习了并且改善了满足力量系统操作的要求。在这份报纸，基于的关联向量机器(RVM ) 当模特儿被建立为给定的信心水平预言风力量和它的间隔。一个 NWP 改进模块就 NWP 错误的特征而言被论述。而且，二个参数优化算法被使用进一步改进预言模型并且比较每表演。为了拿三，作为例子在中国弯屈农场，分别地，二个基于 RVM 的模型的表演由基因算法(GA ) 和群优化(PSO ) 基于基因 algorithmartificial 与预言相比的粒子优化了神经网络(GAANN ) 和支持向量机器。结果证明建议模型与 PSORVM 与 GARVM 模型和更多的有效计算一起有更好的预言精确性。

英文摘要：

In recent years, the accuracy of the wind power prediction has been urgently studied and improved to sat- isfy the requirements of power system operation. In this paper, the relevance vector machine （RVM）-based models are established to predict the wind power and its interval for a given confidence level. An NWP improvement module is presented considering the characteristic of NWP error. Moreover, two parameter optimization algorithms are applied to further improve the prediction model and to compare each performance. To take three wind farms in China as examples, the performance of two RVM-based models optimized, respectively, by genetic algorithm （GA） and particle swarm optimization （PSO） are compared with predictions based on a genetic algorithm-artificial neural network （GA-ANN） and support vector machine. Results show that the proposed models have better prediction accuracy with GA-RVM model and more efficient calcu- lation with PSO-RVM.