Artificial Neural Network Based Predictive Control applied to Residential Buildings

We propose a supervisory ANN-MPC controller for residential buildings, that is able to simultaneously minimize the energy consumption and cost and to maintain the desired thermal comfort, while taking into account occupant expectations and physical constraints like the energy price and the power limitations.

The model, applied to a building with an automatic HVAC system controller, forecast temperature and humidity variations inside each building zones and enables the controller to better track the target conditions.

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