Abstract
Estimating building energy consumption is difficult because it deals with complex interactions among uncertain weather conditions, occupant behaviors, and building characteristics. To facilitate estimation, this study employs a benchmarking methodology to obtain energy baseline for sample buildings. Utilizing a scientific simulation tool, this study attempts to develop energy consumption baselines of two typical concrete residences in Taiwan, and subsequently allows a simplified energy consumption prediction process at an early design stage of building development. Using weather data of three metropolitan cities as testbeds, annual energy consumption of two types of modern residences are determined through a series of simulation sessions with different building settings. The impacts of key building characteristics, including building insulation, air tightness, orientation, location, and residence type, are carefully investigated. Sample utility bills are then collected to validate the simulated results, resulting in three adjustment parameters for normalization, including ‘number of residents’, ‘total floor area’, and ‘air conditioning comfort level’, for justification of occupant behaviors in different living conditions. Study results not only provide valuable benchmarking data serving as references for performance evaluation of different energy-saving strategies, but also show how effective extended building insulation, enhanced air tightness, and prudent selection of residence location and orientation can be for successful implementation of building sustainability in tropical and subtropical regions.
Highlights
Sustainable development and reduction of energy consumption have been the center of attention in many countries due to the joint effects of climate change, urban heat island effect, rising cost of energy, and environmental concerns [1]
Creating geometric models, assigning building properties, and applying climate and weather data are the three primary steps required for estimating annual energy consumption using simulation
From the data derived from simulation, this study suggests a four-level air conditioning comfort level setting (PAC_comf) that could be asserted to meet different AC comfort level requirements: (a) for maximum AC comfort, PAC_comf is set at “1”; (b) for moderate AC comfort, PAC_comf is set at “0.8563”(AC on at 27 ◦C and operates for 14 h); (c) for less than moderate AC comfort, PAC_comf is set at “0.7125” (AC on at 28 ◦C and operates for 10 h); and (d) for least AC comfort, PAC_comf is set at “ 0.5688” (1–0.4312) (AC on at 30 ◦C and operates for 6 h)
Summary
Sustainable development and reduction of energy consumption have been the center of attention in many countries due to the joint effects of climate change, urban heat island effect, rising cost of energy, and environmental concerns [1]. Taiwan started to adopt residential building energy standards in 1997, presenting an official commitment to achieve sustainable development. Other than struggling with uncertainties encountered in 3D simulation, a benchmarking system with predetermined baseline consumption data of sample buildings can be employed to predict how a real building with similar characteristics should perform. This kind of benchmarking prediction usually has to be normalized in consideration of the different settings between the sample building and the one being predicted [22,23]. Weather Data—Major Cities 1981~2010. updated every 10 years, data from Central Weather Bureau 2010 (Taiwan)
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