In France, buildings account for a large part of the energy consumption and carbon emissions. Both are mainly due to heating, ventilation and air-conditioning (HVAC) systems. Because older, oversized or poorly maintained systems may be using more energy and costing more to operate than necessary, new management approaches are needed. In addition, energy efficiency can be improved in central heating and cooling systems by introducing zoned operation. So, the present work deals with the predictive control of multizone HVAC systems in non-residential buildings. First, a real non-residential building located in Perpignan (south of France) has been modelled using the EnergyPlus software. We used the predicted mean vote (PMV) index as a thermal comfort indicator and developed low-order ANN-based models to be used as controller's internal models. A genetic algorithm allowed the optimization problem to be solved. In order to appraise the proposed management strategy, it has been compared to basic scheduling techniques. Using the proposed strategy, the operation of all the HVAC subsystems is optimized by computing the right time to turn them on and off, in both heating and cooling modes. Energy consumption is minimized and thermal comfort requirements are met. So, the simulation results highlight the pertinence of a predicitive approach for multizone HVAC systems management. time index (–) predicted mean vote in the room j of the building (–) difference between the heat produced and the heat lost in the room j of the building (Wm−2) occupants’ metabolic activity in the room j of the building (Wm−2) external work in the room j of the building (Wm−2) heat loss by diffusion through the skin in the room j of the building (Wm−2) heat loss by sweating in the room j of the building (Wm−2) heat loss by latent respiration in the room j of the building (Wm−2) heat loss by dry respiration in the room j of the building (Wm−2) heat loss by radiance in the room j of the building (Wm−2) heat loss by convection in the room j of the building (Wm−2) air temperature in the room j of the building (°C) radiant temperature in the room j of the building (°C) relative humidity in the room j of the building (%) air speed in the room j of the building (ms−1) clothing thermal insulation in the room j of the building (clo) outdoor temperature at 6a.m. (°C) outdoor temperature (°C) consumption of electrical power in the room j of the building (kW) occupancy in the room j of the building (–) HVAC temperature set-point in the room j of the building (°C) HVAC temperature set-point in the room l of the building (°C) HVAC temperature set-point in the room m of the building (°C) value of T j sp allowing to obtain PMV j sp in the room j of the building (°C) PMV set-point in the room j of the building (in the present study, PMV j sp = 0 ) (–) right time to turn the HVAC susbsytem in the room j of the building on or off (–) vector bringing the optimal HVAC switching times together (–) forecast horizon (–) thermal comfort threshold (in the present study, PMV j min = − 0.5 ) (–) thermal comfort threshold (in the present study, PMV j min = + 0.5 ) (–) crossover fraction (genetic algorithm) (–) amount of mutation (genetic algorithm) (–) sum over all the output neurons of the magnitude of the correlation between V e and E e,o (–) partial derivative of C with respect to each of the incoming weigths of the candidate unit (–) value of the candidate unit for example e (–) averaged value of V over all the training examples (–) residual output error measured at neuron o (–) averaged value of E o over all the training examples (–) correlation between the value of the candidate unit and neuron o (–) derivative of the candidate's activation function with respect to the sum of its inputs (for example e) (–) input received by the candidate unit from unit i (for example e) (–) 
Predictive control of multizone heating, ventilation and air-conditioning systems in non-residential buildings