基于马尔可夫链筛选组合预测模型的中长期负荷预测方法

在负荷预测的模型组合过程中，主要是根据历史数据的趋势恰当选择模型，再根据模型特点选择权重分配方法。针对灰色关联度满足要求的几种模型预测值分化较大的问题，从负荷数据的增长率无后效性这一特点出发，通过对原始数据增长率的分析，采用马尔可夫链划分区间，从几种满足精度要求的模型中筛选出两种进行组合预测，通过方差—协方差方法分配权重。经过该种方法的筛选，不仅可以更准确地选择组合预测模型的类型，而且具有较高精度。

Mid-long term load forecasting based on Markov chain screening combination forecasting models

It is important to choose the right model according to the trend of the historical data in the process of load forecast model combination. And then, a method is chosen to assign weights according to the features of the models. Even forecast models meet the requirements of the grey correlation degree, the forecast results still have large differences. To solve the question, this paper, according to the feature that the growth rate of load data is non-aftereffect property of Markov chain, and by analyzing the growth rate of load data, uses Markov chain to divide intervals and screens two kinds from the models which have met the accuracy requirement, and adopts the method of variance- covariance to assign weights. Using this method of screening not only can accurately choose the models for combination forecast, but also has a high precision. This work is supported by National Natural Science Foundation of China (No. 51107143).