三种主流风电场功率预测算法适应性对比研究
摘要：风电的间歇性和波动性给风电功率预测带来了较大难度,而地形和气象上的复杂性使单一功率预测算法很难适应不同的风电场。以3种主流风电功率预测算法为研究对象进行比较研究,分别是遗传算法优化的BP神经网络(GA-BP)、径向基函数神经网络(RBF)和支持向量机(SVM),帮助研究人员针对不同风电场的地形和气候特征选择最适合的预测模型,从而提高短期功率预测精度。以中国地形和气候特征不同的3个风电场为例,从预测精度、计算效率、模型适应性3个角度对比分析3种模型在不同气候、不同地形条件下的适应性。结果表明,RBF和SVM预测效果整体优于GA-BP模型,但在不同季节、不同地形条件下3种模型各具优势,以月份为单位建立功率预测模型,可以提高短期功率预测精度。
Abstract:The intermittence and variability of wind power are the main challenges for perfect predicts. Besides, the meteorological and topological complexity makes it even harder to apply any prediction methods in specific case. In this paper, there are three mainstream wind power prediction methods to be discussed on their performance on different time and spatial scale, which are BP neural network optimized by genetic algorithm (GA-BP), radial basis function neural network (RBF) and support vector machines (SVM). The research helps users select the most suitable algorithm towards different terrains and climates, so the predicted accuracy is improved. Taking three wind farms in China with different terrains and climates as examples, the accuracy, computational efficiency and adaptability of three models are compared. Results show that RBF and SVM generally have better predicted accuracy than GA-BP model. Nevertheless, three models show advantages in different seasons and terrains. What's more, predicted model built with month as time interval can increase the accuracy of short-term wind power prediction.
标题：三种主流风电场功率预测算法适应性对比研究
英文标题：Adaptability Comparison of Three Mainstream Short-term Wind Power Prediction Methods
作者：张慧玲, 高小力, 刘永前, 阎洁, 韩爽,
关键词：风电场短期功率预测, 适应性, GA-BP神经网络, RBF神经网络, 支持向量机,
发表日期：2015-12-09
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