摘要
鉴于实际应用中多变量因素对混沌预测的影响,提出了多变量时间序列相空间重构方法,以此为基础建立多变量加权一阶局域混沌预测模型。引入等概率符号化极大联合熵求取延迟时间、最小香农熵法求取嵌入维数,实现多变量混沌预测模型子序列重构;对实际序列采用区间邻近点法确定预测中心点的邻近点,避免产生伪邻近点;最后用关联分析确定观测变量。将该模型应用于短期电力负荷预测,分析气温等影响因素与电力负荷的相关程度,引入气温时间序列作为另一观测变量,实验证明相对于单变量预测方法提高了预测精度。
In view of the influence of multi-variable on the chaotic prediction in practical application, a method for phase space reconstruction of multivariate time series is proposed, and a weighted one-rank local chaos forecasting model on multi-variable was established. The equal probability-based maximum joint entropy and the minimum Shannon entropy are introduced to get the delay time and the embedding dimension respectively, realizing the sub-sequence reconstruction to the chaotic prediction model. The nearest neighbor point method is used to determine the neighborhood of the prediction center to avoid false neighbors, and the correlation analysis is used to determine the observed variables. The model was applied to short-term load forecasting, and the temperature time series was introduced as another observation variable by the analysis of the impact of temperature and other factors related to electric load. The experimental results showed that the prediction accuracy was improved compared with the single variable forecasting method.
出处
《计量学报》
CSCD
北大核心
2018年第1期77-82,共6页
Acta Metrologica Sinica
基金
国家自然科学基金(61077071
51475405)
河北省自然科学基金(F2016203496
F2015203413
F2015203392)
河北省高层次人才项目(A2016002032)
关键词
计量学
短期电力负荷预测
加权一阶局域法
混沌预测
模型优化
等概率符号化
极大联合熵
香农熵
多变量预测
metrology
short-term load forecasting
weighted one-rank local region method
chaos prediction
model optimization
equal probability symbolization
maximum joint entropy
Shannon entropy
muhivariable prediction
作者简介
张淑清（1966-）,女,河北秦皇岛人,燕山大学教授,博士,主要研究方向为弱信号检测、智能信号处理和故障诊断等。zhshq-yd@163.com
