Heng Quan

Buildings produce a large share of New York's greenhouse gas emissions, but predicting future energy demand — essential for reducing those emissions — has been hampered by missing data on how buildings currently use energy.

Semiha Ergan in NYU Tandon’s Civil and Urban Engineering (CUE) Department, is pursuing research that addresses the problem from two directions. Both projects, conducted with CUE Ph.D. student Heng Quan, apply machine learning to forecast building energy use, including short-term (day-ahead) predictions to support grid peak management and longer-term (monthly) projections of how climate change may affect energy demand and building-grid interactions.

First, Ergan and Quan introduced STARS (Synthetic-to-real Transfer for At-scale Robust Short-term forecasting), which predicts 24-hour-ahead electricity use across buildings in New York State. The practical problem, Ergan said, is that most buildings lack the historic submetered sensor data that conventional forecasting models require.

"In reality, most buildings only have monthly electricity use data,” said Ergan, noting that detailed sensor data remains rare even in buildings subject to energy reporting requirements like New York City’s Local Law 84 and 87.

STARS sidesteps that limitation by training on thousands of simulated building profiles from the U.S. Department of Energy's ComStock library, then transferring what it learns to real buildings. Tested against actual consumption data from 101 New York State buildings, the model achieved 12.07 percent error in summer and 11.44 percent in winter, below the roughly 30 percent threshold that industry guidelines consider well-calibrated.

The 24-hour forecast window is designed for demand response programs. During heat waves, grid operators need day-ahead predictions to coordinate building energy use — pre-cooling spaces before peak hours, for example — to prevent blackouts. "Eventually, this will help advance grid efficiency and citizen comfort,” said Ergan.

In complementary work with Quan, Ergan is examining how climate change will impact New York City buildings’ energy use over longer timeframes. They developed a physics-based machine learning model to address the poor extrapolation performance of purely data-driven methods.

The model is trained on real building energy consumption data from NYC Local Law 84 covering over 1,000 buildings and projects monthly energy use under warming scenarios of 2 to 4 degrees Fahrenheit. By incorporating physics-based knowledge into the machine learning framework, it enables robust projections even without historical data from the future climate conditions buildings will face.

Their methodology revealed, for example, that a 4-degree increase could raise summer energy use by an average of 7.6 percent.

Together, the two projects address building energy use from complementary time scales: short-term forecasting enables day-ahead grid coordination during peak demand events, while long-term climate projections inform infrastructure planning and policy. Both ultimately support greenhouse gas reduction goals, as operational energy use converts directly to emissions.

The research was funded by NYU Tandon's IDC Innovation Hub.

Heng Quan and Semiha Ergan. 2025. Sim-to-Real Transfer Learning for Large-Scale Short-Term Building Energy Forecasting in Sustainable Cities. In Proceedings of the 12th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation (BuildSys '25). Association for Computing Machinery, New York, NY, USA, 86–95. https://doi.org/10.1145/3736425.3772006