A method of smart building climatization management, for optimizing energy consumption (Ee, Eg) given a temporal resolution (Δt), a temporal horizon (t max ) and a starting interior temperature, the method comprising: automatically retrieving weather forecast data, weather historic data and interior temperature (Tint) historic data; segmenting the historic data according to a plurality of time-of-day-independent semantic states; training a first set of regression models (Reg1) with the segmented historic data; computing ON/OFF periods of a HVAC system (ON_OFF_MV) according to at least the first set of regression models (Reg1) and the weather forecast data based on interior temperature simulations depending on the time-of-day-independent semantic states; the method being characterized in that the step of computing ON/OFF periods of a HVAC system (ON_OFF_MV) further comprises simulating interior temperature (Tint) with a first starting time and, if interior temperature (Tint) does not reach the minimum comfort temperature boundary (Tsp min ) during building occupation, iteratively simulating interior temperature (Tint) with a second starting time, wherein the second starting time is prior to the first starting time by a difference equal to the temporal resolution (Δt), and so on until the temporal horizon (t max ) is reached or a minimum comfort temperature boundary is satisfied, the method further comprising: training a second set of regression models (Reg2) with the segmented historic data; computing a temperature set point array (Tsp(t)) according to a meta-heuristic algorithm that uses said second set of regression models (Reg2) based on simulations of energy consumption for different temperature set point values E(Tsp(t)) and on the computed ON/OFF periods of the HVAC system (ON_OFF_MV). automatically retrieving weather forecast data, weather historic data and interior temperature (Tint) historic data; segmenting the historic data according to a plurality of time-of-day-independent semantic states; training a first set of regression models (Reg1) with the segmented historic data; computing ON/OFF periods of a HVAC system (ON_OFF_MV) according to at least the first set of regression models (Reg1) and the weather forecast data based on interior temperature simulations depending on the time-of-day-independent semantic states; the method being characterized in that the step of computing ON/OFF periods of a HVAC system (ON_OFF_MV) further comprises simulating interior temperature (Tint) with a first starting time and, if interior temperature (Tint) does not reach the minimum comfort temperature boundary (Tsp min ) during building occupation, iteratively simulating interior temperature (Tint) with a second starting time, wherein the second starting time is prior to the first starting time by a difference equal to the temporal resolution (Δt), and so on until the temporal horizon (t max ) is reached or a minimum comfort temperature boundary is satisfied, training a second set of regression models (Reg2) with the segmented historic data; computing a temperature set point array (Tsp(t)) according to a meta-heuristic algorithm that uses said second set of regression models (Reg2) based on simulations of energy consumption for different temperature set point values E(Tsp(t)) and on the computed ON/OFF periods of the HVAC system (ON_OFF_MV). <EOS>