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Load control (LC) of distributed populations of air conditioners (ACs) can provide effective demand-side ancillary services while reducing emissions and network operating costs. Pilot trials with ACs typically deploy model-free, open-loop strategies, which cannot deliver the full potential of LC as a network resource. Seeking more advanced strategies, much research in recent years has targeted the development of accurate models and LC approaches for this type of loads. Most existing approaches, however, are restricted to scenarios involving large numbers of ACs, which may not work in small populations, or require two-way communications with the controlled devices, which may come at high costs in widely distributed populations. This paper exploits a previously developed dynamic model for the aggregate demand of populations of ACs to design a simple controller readily implementable in such LC scenarios. The proposed feedback scheme broadcasts thermostat set-point offset changes to the ACs, and requires no direct communications from the devices to the central controller, using instead readings of total aggregate demand from a common power distribution connection point, which may include demand of uncontrolled loads. The scheme is validated on a numerical case study constructed by simulating a distributed population of ACs using real power and temperature data from a 70-house residential precinct, and is shown to deliver robust fast load following performance. The simulation results highlight the practical potential of the proposed model and feedback control scheme for analysing and shaping demand response of ACs using standard control techniques.