CAJ | 학술논문

针对中期电力负荷预测,提出一种具有自适应噪声的完整集成经验模态分解( CEEMDAN)-排列熵和泄漏积分回声状态网络( LIESN)的组合预测方法. CEEMDAN方法在负荷序列分解的每一阶段添加特定的白噪声,通过计算唯一的余量信号以获取各个模态分量,与EEMD方法相比,其分解过程是完整的. 为降低负荷非平稳性对预测精确度的影响以及减小计算规模,采用CEEM-DAN-排列熵方法将负荷时间序列分解为具有复杂度差异的不同子序列,通过分析各个子序列的内在特性,分别构建相应的LIESN预测模型,最终对预测结果进行叠加. 将该方法应用于不同地区的中期峰值电力负荷预测实例中,并与其他组合预测以及单一预测方法进行比较. 实验结果表明,所提出的方法有很高的预测精确度,显示出其有效性和应用潜力.
침대중기전력부하예측,제출일충구유자괄응조성적완정집성경험모태분해( CEEMDAN)-배렬적화설루적분회성상태망락( LIESN)적조합예측방법. CEEMDAN방법재부하서렬분해적매일계단첨가특정적백조성,통과계산유일적여량신호이획취각개모태분량,여EEMD방법상비,기분해과정시완정적. 위강저부하비평은성대예측정학도적영향이급감소계산규모,채용CEEM-DAN-배렬적방법장부하시간서렬분해위구유복잡도차이적불동자서렬,통과분석각개자서렬적내재특성,분별구건상응적LIESN예측모형,최종대예측결과진행첩가. 장해방법응용우불동지구적중기봉치전력부하예측실례중,병여기타조합예측이급단일예측방법진행비교. 실험결과표명,소제출적방법유흔고적예측정학도,현시출기유효성화응용잠력.
Based on complete ensemble empirical mode decomposition with adaptive noise( CEEMDAN)-permutation entropy and echo state network with leaky integrator neurons ( LIESN) , a kind of combined forecasting method was proposed for medium-term power load forecasting. In the CEEMDAN method, a particular white noise was added at each stage of the decomposition and a unique residue was computed to obtain each intrinsic model function( IMF) , compared with EEMD, the resulting decomposition is com-plete. In order to weaken the influence of non-stationary effects of the load series on the prediction accu-racy and reduce computation scale, the load time series was decomposed into a series of subsequences with obvious differences in complex degree by using CEEMDAN-permutation entropy, and the corre-sponding LIESN forecasting model was built respectively by analyzing the inner characteristics of each subsequence. Simultaneously, the ultimate forecasting results can be obtained by the superposition of the corresponding forecasting model. The proposed method was applied to electricity peak load forecasting in-stances in different areas and compared with other combined and single forecasting methods. Experiment results confirm that the proposed method has a high prediction precision, and show the effectiveness and applicability.