A holistic utility bill analysis method for baselining whole commercial building energy consumption in Singapore Bing Dong * , Siew Eang Lee, Majid Haji Sapar Department of Building, School of Design and Environment, National University of Singapore, 4 Architecture Drive, 117566 Singapore Received 31 March 2004; received in revised form 25 May 2004; accepted 5 June 2004 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 (T 0 ), 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 (T 0 ) and whole building energy consumption is stated. The result shows that variations of the energy consumption in most of these buildings are contributed by T 0 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. # 2004 Elsevier B.V. All rights reserved. Keywords: Baseline model; Regression analysis; Climate data; Singapore 1. Introduction Energy in the form of electricity is used in building to operate equipment for the safety, efficiency and comfort of its occupants and users. Such equipment includes emer- gency systems, air-conditioning, lighting, transportation, office systems and other appliances [1]. Previous building energy research carried by Building and Construction (BCA) in Singapore shows that the energy consumptions in building accounts for approximately 57% of the whole electricity consumption in Singapore. It is a major energy user in the nation. One of the cheapest and useful ways to reduce such high consumption is by energy retrofitting. An important element in any energy retrofitting program is to verify savings from energy conservation measures (ECMs). here are many approaches to measure retrofit energy sav- ings. Fels et al. (1986) utilized variable-base degree-day method to estimate residential retrofitting energy use. Haberl [2] applied this method to the whole campus level. Kissock et al. [3] developed a regression methodology to measure retrofitting energy use in commercial buildings. Krarti et al. [4] utilized neural networks to estimate energy and demand savings from retrofits of commercial buildings. Dhar et al. (1999) generalized the Fourier series approach to model hourly energy use in commercial buildings. In addition, in most practical cases, utility bill data are used because they are widely available and inexpensive to obtain and process [5]. Furthermore, a crucial factor in verifying energy savings is to develop a baseline model for pre-retrofit energy use. Fels and Keating [6] pointed out that a simple approach to set up the baseline model is to use a regression-based model. Hence, the choice of model variables becomes important. The study carried out by Fels (1986), Kissock (1993) and Katipamula et al. [7] have clearly shown that outdoor dry- bulb temperature was the most important regressor variable, especially at monthly time level. Reddy et al. [5] presented a www.elsevier.com/locate/enbuild Energy and Buildings 37 (2005) 167–174 * Corresponding author. Tel.: +65 68743514. E-mail address: g0203869@nus.edu.sg (B. Dong). 0378-7788/$ – see front matter # 2004 Elsevier B.V. All rights reserved. doi:10.1016/j.enbuild.2004.06.011