基于Hadoop的公共建筑能耗数据挖掘方法

Data Mining Method for Public Buildings Energy Consumption Based on Hadoop

针对建筑能耗数据无法有效利用这一问题,提出利用Hadoop分布式架构,结合建筑基本信息对公共建筑能耗数据进行数据挖掘的方法.对基于Hadoop的公共建筑能耗数据挖掘系统进行了初步设计,并对系统的基本架构和各模块的功能进行了设计和说明.同时,对Apriori算法和C4.5算法实现Map Reduce分布式设计.以山东省100栋办公建筑制冷期的空调系统耗电量为例进行实验分析,得到6类建筑信息属性对空调系统能耗的影响规律,并生成空调系统耗电量判定树,可判别建筑空调系统耗电量等级,并对样本建筑的节能改造提供具有针对性的建议.

The utilization of building energy consumption data is still inefficient. According to this problem, in this paper, a new method based on Hadoop for data mining of public buildings energy consumption combining with building information is proposed. The paper designs the data mining system of public building energy consumption based on Hadoop, and performs designs and illustrations to the basic framework and functional modules. Apriori algorithm and C4.5 algorithm are implemented distributively using Map Reduce programming model. The paper takes 100 office buildings in Shandong Province as examples to analyse the data of air conditioning system energy consumption. The experimental conclusions are the influence rules of 6 kinds of building information on air conditioning system energy consumption. Moreover, the experiment obtains the decision tree of air conditioning system energy consumption. According to the decision tree, we can distinguish the energy consumption level of air conditioning system, and offer targeted advice on energy saving renovation of sample buildings.