A holistic utility bill analysis method for baselining whole commercial building energy consumption in Singapore |
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| Affiliation: | 1. Department of Mechanical Engineering, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;2. Department of Architecture, Massachusetts Institute of Technology, Cambridge, MA 02139, USA;1. University of Colorado, Department of Civil, Environmental and Architectural Engineering, Boulder, CO 80309, USA;2. National Renewable Energy Laboratory, Golden, CO 80401, USA;3. 1325 NE Going Street, Portland, OR 97211, USA;1. School of Environmental Science and Technology, Tianjin University, Tianjin 300072, China;2. School of Energy and Environmental Engineering, Hebei University of Technology, Tianjin 300401, China;1. School of Computational Science and Engineering, Georgia Institute of Technology, Atlanta, GA 30332, United States;2. School of City and Regional Planning, Georgia Institute of Technology, Atlanta, GA 30332, United States;3. School of Public Policy, Georgia Institute of Technology, Atlanta, GA 30332, United States;4. School of Sustainable Engineering and the Built Environment, Arizona State University, 660 S. College Avenue, Tempe, AZ, United States;5. Georgia Institute of Technology, Atlanta, GA 30332, United States;1. Department of Architecture Built Environment and Construction Engineering (ABC), Politecnico di Milano, Milano, Italy;2. DEIM, Dipartimento di Energia, Ingegneria Dell’Informazione e Modelli Matematici, Università di Palermo, Italy |
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| Abstract: | The methodology for baseline building energy consumption is well established for energy saving calculation in the temperate zone both for performance-based energy retrofitting contracts and measurement and verification (M&V) projects. In most cases, statistical regression models based on utility bills and outdoor dry-bulb temperature have been applied to baseline monthly and annual whole building energy use. This paper presents a holistic utility bills analysis method for baseline whole building energy consumption in the tropical region. Six commercial buildings in Singapore were selected for case studies. Correlationships between the climate data, which are monthly mean outdoor dry-bulb temperature (T0), relative humidity (RH) and global solar radiation (GSR), and whole building energy consumption are derived. A deep prediction study based monthly mean outdoor dry-bulb temperature (T0) and whole building energy consumption is stated. The result shows that variations of the energy consumption in most of these buildings are contributed by T0 and can be well predicted at 90% confidence level only with it. The analysis of such kind of model is especially useful for building managers, owners and ESCOs to track and baseline energy use during pre-retrofit and post-retrofit periods in the tropical condition. |
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