马尔科夫链在电力负荷组合预测中的应用

摘要

In view of the specific utilization scope and condition of each single forecasting model, a novel combinatorial forecasting algorithm with nonnegative time-varying based on Markov chain is proposed in this paper.Firstly, Markov chain is used to fit the law of status probability distribution of these filtered models, and then the estimating problem of the one-step status probabilities transition matrix is translated into constrained multivariate self-regression analysis model. Secondly, the combination weights of these filtered models are determined through the estimation of the one-step status probabilities transition matrix and the distribution of status probability. Results of calculation examples show that the forecasting results by the proposed model is accurate and the proposed method is practicable.%针对单一模型都有其特定的适用范围和条件,文中提出了一种基于马尔科夫链拟合的非负时变权重组合预测算法.该算法通过马尔科夫链对筛选出模型的状态概率分布变化规律进行拟合,将一步转移概率矩阵的估计问题转化为多元约束自回归模型,然后利用一步转移概率矩阵的估计和初始状态概率分布来确定组合权重.实例表明,该方法计算量小、精确度高、具有实用性.