EMeister Model Predictive Control: NYC demonstrations
Multi-Objective optimization of portfolios of large commercial buildings:
Extend EMeister model predictive control ("MPC") to portfolios of buildings -- to create the scale, coordination, optimization, and reliability for grid-scale performance in these important programs.
With the University of Colorado Boulder, Pennsylvania State University, Syracuse University, and the Illinois Institute of Technology.
Sponsored by the Solar Energy Technology Office of the U.S. Department of Energy.
Multi-Objective deep reinforcement learning for grid-interactive efficient buildings.
Addresses the most important challenge to broad market deployment of MPC – reducing the time, expense, and expertise needed to create building energy models.
With the National Renewable Energy Laboratory and the University of Colorado Boulder.
Sponsored by the Building Technology Office of the U.S. Department of Energy.
More information here