  Heating and cooling systems in buildings account for 31\% of global energy
use, much of which are regulated by Rule Based Controllers (RBCs) that neither
maximise energy efficiency nor minimise emissions by interacting optimally with
the grid. Control via Reinforcement Learning (RL) has been shown to
significantly improve building energy efficiency, but existing solutions
require access to building-specific simulators or data that cannot be expected
for every building in the world. In response, we show it is possible to obtain
emission-reducing policies without such knowledge a priori--a paradigm we call
zero-shot building control. We combine ideas from system identification and
model-based RL to create PEARL (Probabilistic Emission-Abating Reinforcement
Learning) and show that a short period of active exploration is all that is
required to build a performant model. In experiments across three varied
building energy simulations, we show PEARL outperforms an existing RBC once,
and popular RL baselines in all cases, reducing building emissions by as much
as 31\% whilst maintaining thermal comfort. Our source code is available online
via https://enjeeneer.io/projects/pearl .
