Uploaded May 2026 | Updated September 2026, 2 weeks ago
Buildings are a big contributor to global greenhouse gas emissions. With climate change straining municipal energy grids, predicting future energy demand becomes evermore important. Unfortunately, efforts to mitigate both these issues 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. Together with CUE Ph.D. student Heng Quan, they used 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.
To learn more about this and other NYU research visit nyu.edu/news
Buildings are a big contributor to global greenhouse gas emissions. With climate change straining municipal energy grids, predicting future energy demand becomes evermore important. Unfortunately, efforts to mitigate both these issues 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. Together with CUE Ph.D. student Heng Quan, they used 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.
To learn more about this and other NYU research visit nyu.edu/news
