ENERGY PERFORMANCE AND BENCHMARKING OF HOTEL BUILDINGS IN SINGAPORE

Abstract

In the commercial sector, hotels are one of the most energy demanding building types in Singapore despite constituting only a small portion of the building stock. The intensive use of energy and the production of greenhouse gases in hotel buildings has brought attention to the need for sustainability and energy efficiency in this sector. Hence, in order to support the development of energy efficiency plans for hotel buildings, the development of benchmarks for comparison between hotels is necessary. This study focus on the establishment of energy performance benchmarks for hotel buildings in Singapore. It examined the physical, operational and environmental factors that affect energy use in hotels so as to provide some understanding on hotel energy consumption. The primary objectives of this thesis include (1) To provide a comprehensive understanding on hotels’ energy performance and energy benchmarking through extensive literature review; (2) To create a building energy benchmark by applying regression techniques on physical, operational and environmental factors; (3) To propose a new method to classify hotel buildings using clustering method. During the development of the energy benchmarks, the dataset was filtered to remove outliers. Hotels that failed to report or have inadequate information were excluded from this study. Statistical techniques were performed on 93 hotels to discover key drivers affecting total energy consumed in buildings and the primary normalisation factor. Regression model based on the identified drivers was developed. Two different classification methods were applied and compared against each other. The main findings of the study include (1) The total energy consumption of hotels are strongly related to the number of people in the building, number of hotel rooms and gross floor area; (2) It is sufficient to use gross floor area as the only variable in the regression model as it can explain more than 93% of the variation in total energy consumption; (3) Classification of hotel buildings using K-means clustering method create more reasonable class boundaries and uniform class bands than the traditional cumulative frequency method.