Can Zeng

Application Research of Deep Reinforcement Learning in Low-carbon Scheduling Mechanism for Power Grid Construction Tasks

This paper proposes a low-carbon scheduling and carbon emission prediction framework for power grid construction machinery by integrating an improved genetic algorithm (IGA), deep reinforcement learning (DRL), and a gated recurrent unit (GRU) neural network. The proposed approach is applicable to engineering scenarios...