fig9

From: An integrated energy system scheduling method considering year-round load variations based on deep reinforcement learning

Figure 9. The impact of clustering and expert knowledge on the output of different devices. (A) Output of different devices in TD3base scenario; (B) Output of different devices in TD3c2 scenario; (C) Output of different devices in TD3c2-wpk scenario; (D) Output of different devices in MPC scenario. TD3base: This primary scenario does not employ load clustering and does not incorporate expert knowledge into the reward function; TD3c2: scenario with clustering under heating electricity load; TD3c2-wpk: scenario with clustering and expert knowledge under heating electricity load; MPC: operational optimization using model predictive control algorithms; HP: heat pump; BESS: battery electricity energy storage; PV: photovoltaic; GE: gas engine; TESS: thermal energy storage system.