Abstract: Focused on the issue that interference would exist in the analysis and prediction of airport energy data because of the outliers, a prediction model based on improved Fuzzy Support Vector Regression (FSVR) was established for the demand of airport energy. Firstly, a fuzzy statistical method was selected to make an analysis on test sample sets, parameters and the outputs of models, and a basic membership function form consistent with the data distribution would be derived from this analysis. Secondly, relearning of membership function would be performed with respect to expert experiences, then the parameter values a and b of the normal membership function, the boundary parameter values of semi-trapezoid membership function and the parameter values p and d of triangular membership function would gradually be refined and improved, so as to eliminate or reduce the outliers which were not conducive to data mining and reserved the key points. Finally, combined with Support Vector Regression (SVR) algorithm, a prediction model was established and its feasibility was verified subsequently. The experimental result shows that, compared with Back Propagation (BP) neural network, the prediction accuracy of the FSVR increases 2.66% and the recognition rate of outliers increases 3.72%.

Key words: airport energy demand prediction, Fuzzy Support Vector Regression (FSVR), Support Vector Machine (SVM), fuzzy membership, outlier

摘要： 针对离群点在机场能源数据的预测和分析中存在干扰等问题,建立了一种基于改进模糊支持向量回归(FSVR)的机场能源需求预测模型。首先,采用模糊统计法对测试样本集、系统参数和模型输出进行分析,推导出符合其数据分布的基本隶属函数形式;其次,结合例证法、专家经验法对隶属函数进行"再学习",逐步修改和完善正态隶属函数a、b参数值,半梯形隶属函数边界参数值及三角隶属函数p、d参数值,以此消除或减少不利数据挖掘的离群点,同时保留有效关键点;最后,结合支持向量回归(SVR)算法,建立预测模型,并验证了该模型的可行性。实验结果表明,与反向传播(BP)神经网络方法相比,FSVR方法的预测准确率提高了2.66%,对离群点的识别率提高了3.72%。

关键词: 机场能源需求预测, 模糊支持向量回归, 支持向量机, 模糊隶属度, 离群点