Energy performance evaluation for benchmarking school buildings using dynamic clustering analysis and particle swarm optimization

Benchmarking the energy performance of buildings has received increasing attention as striving for energy efficiency through more effective energy management has become a major concern of governments. Various methods for classifying building energy performance have been developed, and the clustering technique is considered one of the best approaches. This paper proposes a method utilizing dynamic clustering to analyze the electricity consumption patterns of buildings to decide the optimal cluster number and allocate the buildings to corresponding clusters for energy benchmarking. For the evaluation of number of clusters, this article has employed the inter–intra clustering method with particle swarm optimization algorithm. The electricity consumption data were collected through an energy survey performed in 30 junior high schools in Taipei, Taiwan. In a traditional method, the 30 schools would be grouped into one same cluster and the energy benchmarking report an average value of 541.4 kWh/year per student. The proposed method that took different electricity consumption patterns of the schools into consideration produced more detailed results as follows: the optimal cluster number was 3 with an inter–intra index value of 0.708, and the energy benchmarking index of these three clusters read, respectively, 362, 512, and 851 kWh/year per student.

Practical application : The study proposed an innovative dynamic clustering technique to decide the optimal cluster number and allocate the assessed buildings. The results showed that compared to a traditional approach that tended to group assessed buildings into one cluster, the proposed method was able to classify the buildings into three clusters for further benchmarking. This method can be used by governments and large corporations. For example, in Hong Kong, primary schools are grouped into one cluster for energy benchmarking. Using the proposed method can further classify primary schools into more clusters; benchmarking index can then be developed for each cluster.