基于混合支持度Apriori算法的短期负荷预测模型

摘要

从海量数据中发现气象变化和节假日等外部因素对电力负荷变化影响的关联特征，有助于提高短期电力负荷预测的精确度。提出一种基于混合支持度Apriori算法的电力负荷预测模型，采用“分而治之”的思想对电力负荷预测的数据集市进行了划分，结合混合支持度Apriori算法挖掘与负荷相关的关联规则，根据挖掘出的关联规则进行预测。通过仿真结果和误差分析，验证了方法的高效性，提高了预测的精确度。%Power load changes are influenced by exterior factors such as climate change and holiday etc,those correlation of the features can be found from the huge data, which helps to improve the accuracy of the short-term power load forecasting. This paper presents a power load forecasting model based on mixed support of Apriori algorithm. The thought of "divide and rule" is adopt to classify the power load forecasting of data mart. The mixed support for the Apriori algorithm is combined to mining the related association rules. According to the simulation of those association rules, simulation results and the error analysis are given, furthermore, the precision of prediction are improved, and also the effective of method is proved.