CAJ | 학술논문

实行负荷预测是空气调节系统优化运行的基础，如何选择工程应用切实可行的方法，仍然是一个值得探讨和研究的问题。支持向量机(SVM)算法在解决小样本、非线性及高维模式识别中表现出许多特有的优势。本文将支持向量机算法引入空调负荷预测中，对深圳市夏季六、七月份的逐时空调负荷，分别用SVM模型和armax模型进行了训练和预测，结果表明SVM模型适用于空调负荷预测，具有很好的泛化能力。
실행부하예측시공기조절계통우화운행적기출，여하선택공정응용절실가행적방법，잉연시일개치득탐토화연구적문제。지지향량궤(SVM)산법재해결소양본、비선성급고유모식식별중표현출허다특유적우세。본문장지지향량궤산법인입공조부하예측중，대심수시하계륙、칠월빈적축시공조부하，분별용SVM모형화armax모형진행료훈련화예측，결과표명SVM모형괄용우공조부하예측，구유흔호적범화능력。
Air conditioning load prediction is the basis of the optimal operation of the air conditioning system. It is still an important problem that how to choose the method of practical engineering application. Support vector machine (SVM) algorithm shows many unique advantages in tackling small sample, nonlinear and high dimensional pattern recognition. In the present study, the support vector machine (SVM) algorithm was introduced into the air conditioning load prediction, an SVM model and armax model were used for predicting the hourly air conditioning load in June and July in Shenzen city. The results show that, the SVM model is effective for predicting the air conditioning load, and it possesses generalization ability.