A deep Q-network variant with forecasting and battery-degradation-aware rewards schedules a building's stationary storage and electric vehicles jointly, claiming large cost savings over a MILP baseline.
Title resolution pending
1 Pith paper cite this work. Polarity classification is still indexing.
1
Pith paper citing it
fields
eess.SY 1years
2025 1verdicts
REJECT 1representative citing papers
citing papers explorer
-
Deep reinforcement learning-based joint real-time energy scheduling for green buildings with heterogeneous battery energy storage devices
A deep Q-network variant with forecasting and battery-degradation-aware rewards schedules a building's stationary storage and electric vehicles jointly, claiming large cost savings over a MILP baseline.