@inproceedings{3,
  author = {DiAndra Phillip and Jin Chen and Fani Maksakuli and Arber Ruci and E'edresha Sturdivant and Zhigang Zhu},
  title = {Improving Building Energy Efficiency through Data Analysis},
  abstract = {For many lawmakers, energy-efficient buildings have been the main focus in large cities across the United States. Buildings consume the largest amount of energy and produce the highest amounts of greenhouse emissions. This is especially true for New York City (NYC)’s public and private buildings, which alone emit more than two-thirds of the city’s total greenhouse emissions. Therefore, improvements in building energy efficiency have become an essential target to reduce the amount of greenhouse gas emissions and fossil fuel consumption. NYC’s buildings’ historical energy consumption data was used in machine learning models to determine their ENERGY STAR scores for time series analysis and future pre- diction. Machine learning models were used to predict future energy use and answer the question of how to incorporate machine learning for effective decision-making to optimize energy usage within the largest buildings in a city. The results show that grouping buildings by property type, rather than by location, provides better predictions for ENERGY STAR scores.},
  year = {2023},
  journal = {The 14th ACM International Conference on Future Energy Systems (e-Energy ’23 Companion)},
  chapter = {1},
  pages = {7},
  month = {06},
  url = {https://par.nsf.gov/biblio/10440682},
  doi = {10.1145/3599733.3600244},
}