We present an adaptive setback thermostat (AST) which switches between two temperature setpoints - one optimized for user comfort and one for saving energy. The AST operates locally in an office room and makes its decisions based on how the room is (expected to be) used. Core issues, in decreasing order of importance, are user comfort, user friendliness (ease of installation and use) and to reduce energy costs. It is argued why a reinforcement learning approach may not be the best solution, and the shown how to reformulate the problem using a simple heuristic where reward maximization is replaced by explicit prediction of user arrivals, using temporal difference learning.
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