MACHINE LEARNING ALGORITHM COMPARISONS IN THE FIELD OF LOW-ENERGY BUILDING DESIGN AND OPERATION

Date

2023-08-14

Authors

Birdsell, Blair

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Abstract

This work spans multiple disciplines to focus on innovation in data-driven decision-making in building performance, utilizing machine learning algorithms. It provides performance benchmarks and analysis that can guide industry best-practice in building design and operations. Specific outcomes from this work have broad application such as the optimization of building energy efficiency, bettering occupant comfort, potential energy saving retrofits, and improving overall building performance. Data-driven decisions are made by evaluating and comparing data from various sources, including building simulations, historical records, and sensor measurements. Through the application of machine learning tools, this data is transformed into a foundation for effective decision-making by building designers and operators or incorporated into intelligent building management systems. Gradient Boosted Trees when applied to building energy performance demonstrated robust prediction characteristics across different data types and problem configurations as well as being highly accurate and computationally efficient. In predicting building performance time series, the research documents neural network architectures containing convolutional layers as having the ability to forecast short-term high-frequency variations in airflow and predict monthly minimal and maximum airflow values with high accuracy. These results support their inclusion and application in building management systems for high-performance smart buildings.

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Keywords

Machine learning, Building simulation

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