Toward the application of a machine learning framework for building life cycle energy assessment

The construction industry in the United States consumes more than 50% of the global energy supply per year, suggesting that significant efforts may be needed to reduce building energy demand and its carbon footprint. During their lifespans, buildings consume embodied energy (EE) and operational energy (OE). Building professionals, therefore, conduct life cycle energy assessments (LCEA) to quantify and understand the paradox and interconnectedness between EE and OE. Traditionally, simulation-based optimization techniques were used for design space exploration to identify a building design with the fewest energy implications. However, literature shows these data-driven approaches are often error-prone, time-consuming, and computationally expensive, and they fail to provide real-time feedback to the user. Moreover, EE and OE assessment tools are disjointed and suffer from interoperability issues. These limitations restrict design space exploration, which eventually hinders the design decision-making process. Over the last few years, the increased availability of building data has made machine learning (ML) techniques a popular choice for building performance assessments. Several articles have developed prediction models to assess or optimize OE. While this work is significant, studies utilizing ML techniques for building LCEA are lacking, mainly due to the unavailability of a large-scale LCEA database. In this paper, we propose a methodology to (1) generate a simulation-based building energy dataset for different building typologies using a parametric framework, (2) utilize the synthetically generated database to develop an ML-based prediction model to predict EE and OE, and (3) test the model using a case study. The case study results show that the model achieves high prediction performance using minimal inputs available during the early design phase. The results further indicate that ML techniques can be used by building designers with no or limited LCE expertise to instantaneously estimate building LCE performance and help them select design options with minimal LCE consumption.

Automated summary

The construction industry in the United States consumes more than 50% of global energy supply annually, highlighting the need for efforts to reduce building energy demand and carbon footprint. Building professionals use life cycle energy assessments (LCEA) to understand the interaction between embodied energy (EE) and operational energy (OE) in buildings. Traditional simulation-based optimization techniques have limitations such as errors, time consumption, and lack of real-time feedback, while machine learning (ML) techniques have shown potential for building performance assessments.