CAJ | 학술논문

针对传统的最小二乘支持向量机(LSSVM)参数不易确定且单一预测模型精度不高的问题，提出了一种基于集合经验模态分解(EEMD)与LSSVM的组合预测模型。首先利用EEMD将历史数据分解成一系列相对比较平稳的分量序列，再对各子序列分别建立合适的预测模型。进一步通过贝叶斯证据框架来优化LSSVM的参数，用贝叶斯推理确定模型参数、正规化超参数和核参数。然后将各子序列预测结果进行叠加得到最终预测值。最后，将该预测模型用于某一家庭超短期负荷预测中，仿真结果表明，该模型取得了比单一模型更好的预测效果。
침대전통적최소이승지지향량궤(LSSVM)삼수불역학정차단일예측모형정도불고적문제，제출료일충기우집합경험모태분해(EEMD)여LSSVM적조합예측모형。수선이용EEMD장역사수거분해성일계렬상대비교평은적분량서렬，재대각자서렬분별건립합괄적예측모형。진일보통과패협사증거광가래우화LSSVM적삼수，용패협사추리학정모형삼수、정규화초삼수화핵삼수。연후장각자서렬예측결과진행첩가득도최종예측치。최후，장해예측모형용우모일가정초단기부하예측중，방진결과표명，해모형취득료비단일모형경호적예측효과。
To solve the problem of the uncertain parameters and the low precision of the single forecasting model for the traditional least squares support vector machine (LSSVM), a combined forecasting model based on ensemble empirical mode decomposition (EEMD) and the LSSVM is proposed.Firstly, the historical datais decomposed into a series of relatively stable component of the sequence by the EEMD, and then the appropriate forecasting model is established for each component of the sequence. The parameters of the LSSVM are optimized through the Bayesian evidence framework. Bayesian inferences are used to determine model parameters, regularization hyper-parameters and kernel parameters. The results of each component forecasting are superimposed to obtain the final forecasting result.Finally, a household ultra-short-term load data is used to validate the model, and the simulation results show that this model has achieved better forecasting result than a single model.