arXiv:2607. 27914v1 Announce Type: new Abstract: Multi-zone variable-air-volume control must balance thermal comfort, indoor air quality, and electricity use across several continuous actuators.
By Takumi Shioda, Kohei Terashima, Tatsuo Nagai
arXiv:2607. 12856v1 Announce Type: new Abstract: Buildings are expected to shift cooling loads in response to grid conditions.
By Takumi Shioda, Kohei Terashima, Tatsuo Nagai
arXiv:2608. 09453v1 Announce Type: cross Abstract: On--off cycling is the main cause of compressor wear in residential heat pumps, yet reinforcement learning (RL) controllers for buildings typically optimise only energy cost and thermal comfort, ignoring how much the learned policy cycles.
By Faizan Ahmed, Aniket Dixit, James Brusey
Buildings account for roughly one-third of global energy consumption and CO$_2$ emissions. Optimizing indoor climate systems plays a critical role for urban climate mitigation aligned with UN Sustainable Development Goals 11 and 13.
arXiv:2409. 19716v2 Announce Type: replace-cross Abstract: Constrained Reinforcement Learning (RL) has emerged as a significant research area within RL, where integrating constraints with rewards is crucial for enhancing safety and performance across diverse control tasks.
By Baohe Zhang, Lilli Frison, Thomas Brox, Joschka B\"odecker
The paper introduces NOMAD‑RL, a reinforcement learning controller for HVAC systems that learns to adapt across diverse thermal zones via a universal thermostat interface. It employs an adaptive domain randomization scheme using physics‑informed normalizing flows to generate realistic, multimodal training data, enabling the recurrent policy to handle partial observability. Experiments show NOMAD‑RL outperforms constant‑setpoint PID and non‑randomized RL, and rivals well‑tuned model predictive control, especially in multi‑zone scenarios.
By Pablo Boitel, Kun Zhang