A method for improving predictive models of a neural network used for controlling a temperature in an area, the method comprising: storing a first predictive model comprising a first set of weights of a neural network and a second predictive model comprising a second set of weights of a neural network in a memory of an environment controller (505, 100); determining by a processing unit of the environment controller a plurality of consecutive temperature measurements in the area; determining by the processing unit of the environment controller a plurality of consecutive humidity level measurements in the area; executing by the processing unit of the environment controller a neural network inference engine using the first set of weights of the first predictive model for inferring a temperature increase value based on inputs, the inputs comprising the plurality of consecutive temperature measurements and the plurality of consecutive humidity level measurements; executing by the processing unit of the environment controller the neural network inference engine using the second set of weights of the second predictive model for inferring a temperature decrease value based on the inputs; calculating by the processing unit of the environment controller a temperature adjustment value based on the temperature increase value and the temperature decrease value; transmitting by the processing unit of the environment controller at least one command to at least one controlled appliance for adjusting the temperature in the area according to the temperature adjustment value; receiving by the processing unit of the environment controller a user preference related to the temperature in the area, the user preference being transmitted by a user device (400, 545); determining by the processing unit of the environment controller based on the received user preference a value of a first reinforcement signal and a value of a second reinforcement signal; executing by the processing unit of the environment controller a neural network training engine implementing reinforcement training to update the first set of weights of the first predictive model based on the inputs, the temperature increase value and the value of the first reinforcement signal; executing by the processing unit of the environment controller the neural network training engine implementing reinforcement training to update the second set of weights of the second predictive model based on the inputs, the temperature decrease value and the value of the second reinforcement signal; and storing the updated first and second sets of weights of the respective first and second predictive models in the memory of the environment controller (565, 100). storing a first predictive model comprising a first set of weights of a neural network and a second predictive model comprising a second set of weights of a neural network in a memory of an environment controller (505, 100); determining by a processing unit of the environment controller a plurality of consecutive temperature measurements in the area; determining by the processing unit of the environment controller a plurality of consecutive humidity level measurements in the area; executing by the processing unit of the environment controller a neural network inference engine using the first set of weights of the first predictive model for inferring a temperature increase value based on inputs, the inputs comprising the plurality of consecutive temperature measurements and the plurality of consecutive humidity level measurements; executing by the processing unit of the environment controller the neural network inference engine using the second set of weights of the second predictive model for inferring a temperature decrease value based on the inputs; calculating by the processing unit of the environment controller a temperature adjustment value based on the temperature increase value and the temperature decrease value; transmitting by the processing unit of the environment controller at least one command to at least one controlled appliance for adjusting the temperature in the area according to the temperature adjustment value; receiving by the processing unit of the environment controller a user preference related to the temperature in the area, the user preference being transmitted by a user device (400, 545); determining by the processing unit of the environment controller based on the received user preference a value of a first reinforcement signal and a value of a second reinforcement signal; executing by the processing unit of the environment controller a neural network training engine implementing reinforcement training to update the first set of weights of the first predictive model based on the inputs, the temperature increase value and the value of the first reinforcement signal; executing by the processing unit of the environment controller the neural network training engine implementing reinforcement training to update the second set of weights of the second predictive model based on the inputs, the temperature decrease value and the value of the second reinforcement signal; and storing the updated first and second sets of weights of the respective first and second predictive models in the memory of the environment controller (565, 100).