Building Energy Retrofits using Ant Colony Optimisation

Bamdad, Keivan and Cholette, Michael E. and Guan, Lisa and Bell, John (2017) Building Energy Retrofits using Ant Colony Optimisation. In: Healthy Buildings Europe Conference 2017, 2-5 July 2017, Lublin, Poland.


Building retrofits offer an effective solution to enhance energy efficiency in buildings. However, identifying the most effective retrofit remains a challenge due to numerous possible retrofit measures which may have different impacts on building energy consumption. Simulation-based optimization methods (coupling building simulation software with an optimisation algorithm) are powerful tools to identify the best retrofit for a building. In this research, nine variables of a typical Australian office building are optimised under three different sets of building simulation assumptions using Ant Colony Optimisation for Mixed Variables (ACOMV). Results show that up to 19.1% energy savings can be achieved after applying optimal retrofit measures. Results also indicate that incorrect building simulation assumptions (e.g. internal loads) can considerably reduce energy savings obtained by optimisation method. This research provides new design insight into the importance of simulation assumption in building energy optimisation.

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Item Type: Conference or Workshop Item (Commonwealth Reporting Category E) (Paper)
Refereed: Yes
Item Status: Live Archive
Faculty/School / Institute/Centre: Current - USQ Other
Faculty/School / Institute/Centre: Current - USQ Other
Date Deposited: 17 Sep 2020 04:42
Last Modified: 30 Sep 2020 06:00
Uncontrolled Keywords: Ant Colony Optimisation; Building Retrofits; Simulation-Based-Optimisation
Fields of Research (2008): 09 Engineering > 0912 Materials Engineering > 091299 Materials Engineering not elsewhere classified
Fields of Research (2020): 40 ENGINEERING > 4016 Materials engineering > 401699 Materials engineering not elsewhere classified
Socio-Economic Objectives (2008): B Economic Development > 85 Energy > 8505 Renewable Energy > 850599 Renewable Energy not elsewhere classified
Socio-Economic Objectives (2020): 17 ENERGY > 1708 Renewable energy > 170899 Renewable energy not elsewhere classified

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