PREDICTING ENERGY USE INTENSITY OF US HOTEL BUILDINGS USING CBECS MICRODATA

Abstract

In the United States, the commercial sector consumes 21% of the total energy use. With 14%, the ~47,000 hotels are considered to be the third main source of energy consumption in the commercial sector. Stakeholders in the hotel industry have shown significant interest in reducing energy consumption in hotel buildings. However, determining the primary factors that contribute to overall energy consumption is important to develop efficient and effective retrofitting strategies. To address this question, this study focuses on identifying the variables in hotels that contribute majorly to their energy consumption. To achieve that, this study utilizes different machine learning approaches for estimating the source of Energy Use Intensity (EUI) for US hotel buildings based on Commercial Building Energy Consumption Survey (CBECS) 2018 microdata. The findings derived from this research can significantly contribute to the optimization of retrofitting strategies and building design in hotel buildings, as well as the development of effective electrification strategies. Ultimately, this knowledge will empower decision makers to make informed choices that enhance energy efficiency and sustainability in hotel buildings.