A Hybrid LSTM–PPO–RBC Edge Controller for Building HVAC: Sim-to-Real Validation on a Raspberry Pi 4

Deep reinforcement learning (DRL) controllers for heating, ventilation and air conditioning (HVAC) are rarely deployed on real edge hardware or evaluated across climates, and published DRL-HVAC studies seldom report a physical deployment. The contribution here is integration rather than a new algorithm: we build an end-to-end Sim-to-Real deployment of a recurrent proximal policy optimisation (PPO) agent, whose policy network contains a long short-term memory (LSTM) core and is supervised by a rule-based safety shield via the Simplex architecture on a Raspberry Pi 4 and ESP32 edge stack. The recurrent policy is trained once on a Kuala Lumpur EnergyPlus/Sinergym profile of the five-zone VAV archetype and then evaluated without retraining on five further climate profiles (New York, Tokyo, Egypt, Iceland and Saudi Arabia), reaching a comfort-zone compliance rate (CZCR) of 100.00% on New York and Kuala Lumpur, 99.50% on Tokyo, 99.65% on Iceland, 97.24% on Saudi Arabia and 93.50% on Egypt (single-run training). A ten-seed re-run on the training climate confirms 100.00% CZCR and 0 violations in every seed, with energy varying by a 2.3% coefficient of variation (mean 29,332.68 ± 673.30 kWh). The cross-climate transfer figures above remain single-run. A matched-climate PPO baseline, retrained directly on Kuala Lumpur rather than zero-shot transferred from New York, also reaches 100.00% CZCR at 13.2% less energy than the hybrid, showing that the hybrid’s energy premium on this climate is larger, not smaller, once the training climate confound in the original baseline comparison is removed. A two-sided safety shield (23.2–25.8 ∘C triggers) is shown, against an archived upper-bound-only configuration, to reduce overcool violations from 4124 (88.20% CZCR) to zero on the same climate. On a physical benchtop hardware-in-the-loop (HIL) testbed, three repeated 30 min trials, selected post hoc as the cleanest window from each of three evening sessions (the same three sessions’ full-length CZCR values are 98.02%, 97.22%, and 91.50%), record a 100.00% CZCR within the selected windows and a predicted mean vote (PMV) of 0.043 ± 0.023, and the system self-heals from a 12 min 42 s router outage with a +0.06 ∘C thermal drift. On-device inference takes 112.9 ms on the Raspberry Pi 4 (n=1392 logged invocations, with 95th/99th-percentile latency of 116.3 ms/120.4 ms), roughly four orders of magnitude below the 15 min control step. A separate set of three trials, bracketed by the testbed’s own “30 min Test” start/end log events rather than selected after the fact, records a lower but more representative 86.42–92.59% CZCR (aggregate 90.12%). These are benchtop feasibility results from a single disclosed training run and a short physical trial. They represent a step toward narrowing, rather than a closed, Sim-to-Real and edge deployment gap in practical DRL-HVAC.