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An intelligent building automation system can manage many devices in order to balance the energy savings and comfort of the inhabitants. The strategy controlling devices has to be adaptive and learn to match users' needs. Based on the Amigo framework, we developed a simulator of a household. We added lights, devices, pheromone controlled inhabitants, physical model of heat loss, etc. The system runs real-time and a simple strategy was used to control heat, lights and other devices. In this simulator, we plan to evaluate several different control strategies in term of energy efficiency and user comfort. We also proposed an adaptive control strategy based on the neural networks induced on data from sensors and user interaction signals. We built an experimental KNX-bus platform demonstrating the feasibility of our concept.