Assessment of potential energy saving using cluster analysis: A case study of lighting systems in buildings

Abstract

The promulgation of a building energy code needs an accurate estimation of potential energy savings to ensure that its implementation can achieve the goal set. Generally, the estimation is carried out using the mean value averaged from sample buildings as a representative for the whole sector. However, the actual data of building energy performance may not be described by a single normal distribution. In this study, cluster analysis is thus introduced to estimate potential energy savings in lighting systems in buildings, in comparison with the general averaging approach. The study covers analyses of both simulated and actual data. The simulated data of lighting power density (LPD) is generated from Gaussian distributions for 36 cases with different means, variances, and mixing proportions for the investigation. For actual data analysis, LPD values of commercial buildings are extracted from an energy audit database. It is found that the clustering technique yields more accurate energy saving estimation of the actual values (0–11% error) than that computed by the general approach (1–100% error). The proposed clustering method can therefore be used to estimate the potential energy savings for lighting systems in buildings with high accuracy.