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(BIM-4057) Leveraging BIM, Automation, and Machine Learning for Future Cost Assessment of Energy Conservation Measures

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Level: Advanced
TCM Section(s):
7.3. Cost Estimating and Budgeting
5.1. Asset Cost Accounting
Venue: 2023 AACE International Conference & Expo

Abstract: The existential threat of climate change has been a major source of public concern over the past several decades. The recent climate legislation seeks to accelerate carbon reduction efforts across various sectors and incentivizes leaders to act. The building sector alone is responsible for over 40% of carbon emissions, therefore, the inclusion of high performing design strategies while maintaining cost efficiency is required for successful implementation.

This study builds upon the concepts and analysis previously established within "Impact Assessment of Energy Conservation Measures on Building Energy Consumption, Carbon Emission, and Adaptation Cost Using Future Weather Data", presented in July 2022 at ANNSIM Conference and published by SIMBUILD [1]. The previous study analyzed the current and future energy consumption and carbon emissions of an office building in various US climate zones, using future weather data, to assess three energy conservation measures, their implementation cost, and payback period(s). This paper focuses on the role that BIM, automation, and machine learning plays in deriving energy results along with associated costs through simulation and the use of historical data. Showing how these three methods for building design can facilitate an integrated approach to project delivery to yield better building performance and cost efficacy will provide a clear summary of effective measures to address climate change in the AEC.