Method and system for controlling the temperature of a room

1. Method for controlling the temperature of a room ( 20 ) provided with a temperature control device ( 10 ), in particular for keeping the temperature of the room ( 20 ) within a comfort temperature range defined by a lower and an upper hysteresis temperature (T L , T H ), characterized in that

setting the temperature of the room ( 20 ) within the comfort temperature range by means of the temperature control device ( 10 ), then

switching off the temperature control device ( 10 ) at the switch-off time (t ki ) and measuring the change in the temperature of the room ( 20 ) at least until the measured saturation temperature (T sat(O) ), can be measured when changing the direction of the temperature change, is reached, and a first temperature time series (O 1 ) is generated from the measured temperature data, then using a neural network and using the first temperature time series (O 1 ), creating a predicted first temperature time series (P 1 ) indicating the future change of the first temperature time series (O 1 ),

switching on the temperature control device ( 10 ) at the switch-on time (t be ) and measuring the change in temperature of the room ( 20 ), at least until the measured saturation temperature (T sat(O) ), can be measured when changing the direction of the temperature change, is reached, and a second temperature time series (O 2 ) is generated from the measured temperature data, then using a neural network and using the second temperature time series (O 2 ), creating a predicted second temperature time series (P 2 ) indicating the future change of the second temperature time series (O 2 ),

determining predicted saturation temperature values (T sat(P) ) for the elements of the predicted second temperature time series (P 2 ), as switch-off times (t ki ), using the neural network and the first temperature time series (O 1 ) and selecting from the determined predicted saturation temperature values (T sat(P) ) the closest to the corresponding hysteresis temperature (T H , T L ),

determining a switch-off time (t ki ) from the element of the predicted second temperature time series (P 2 ) corresponding to the selected predicted saturation temperature value (T sat(P) *) and then switching off the temperature control device ( 10 ) at the determined switch-off time (t ki ),

determining predicted saturation temperature values (T sat(P) ) for the elements of the predicted first temperature time series (P 1 ), as switch-on times (t be ), using the neural network and the second temperature time series (O 2 ) and selecting from the determined predicted saturation temperature values (T sat(P) ) the closest to the corresponding hysteresis temperature (TH, TL),

determining a switch-on time (t be ) from the element of the predicted first temperature time series (P 1 ) corresponding to the selected predicted saturation temperature value (T sat(P) *) and then switching on the temperature control device ( 10 ) at the determined switch-on time (t be ).

2. Method according to claim 1 , characterized in that determining temperature tolerance ranges (ΔT tol ) in the vicinity of the lower and upper hysteresis temperatures (T L , T H ) and from the predicted saturation temperature values (T sat(P) ) determined by the neural network, the predicted saturation temperature (T sat(P) *) closest to the corresponding hysteresis temperature (T L , T H ) is selected within the appropriate temperature tolerance range (ΔT tol ).

3. Method according to claim 1 , characterized in that a long short-term memory (LSTM) or a 1-dimensional convolutional neural network architecture is used to determine the predicted saturation temperature values (T sat(P) ).

4. Method according to claim 1 , characterized in that a long short-term memory (LSTM) neural network architecture is used to generate the predicted first and second temperature time series (P 1 , P 2 ).

5. A system ( 100 ) for carrying out the method according to claim 1 and comprising a temperature sensor ( 12 ) for measuring the internal temperature of the room ( 20 ) and transmitting the measured temperature data, a control module ( 14 ) for switching on and off the temperature control device ( 10 ), and a remote central IT unit ( 300 ) in communication with the control module ( 14 ) and the temperature sensor ( 12 ) via a digital communication channel ( 200 ), adapted to implement a neural network, which central IT unit ( 300 ) is configured to generate a control signal and transmit it to the control module ( 14 ) based on the temperature data received from the temperature sensor ( 12 ), and the control module ( 14 ) is configured to store and execute the received control signal.

6. System ( 100 ) according to claim 5 , characterized in that the digital communication channel ( 200 ) is implemented within a global information network.

7. System ( 100 ) according to claim 5 , characterized in that the temperature sensor ( 12 ) and the control module ( 14 ) are provided as a single unit.

8. The system ( 100 ) according to claim 7 wherein the single unit is a thermostat ( 16 ).

9. System ( 100 ) according to claim 6 wherein the global information network is Internet.