Method for controlling the temperature of a room provided with a temperature control device, in particular for keeping the temperature of the room 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 within the comfort temperature range by means of the temperature control device, then - switching off the temperature control device at a switch-off time (t ki) and measuring the change in the temperature of the room at least until the measured saturation temperature (T sat), which can be measured when the direction of the temperature change is changing, is reached, and a first temperature time series is generated from the measured temperature data, then using a neural network and using the first temperature time series, creating a predicted first temperature time series indicating the future change of the first temperature time series, - switching on the temperature control device at the switch-on time (t be) and measuring the change in temperature of the room, at least until the measured saturation temperature (T sat), which can be measured when the direction of the temperature change is changing, is reached, and a second temperature time series is generated from the measured temperature data, then using a neural network and using the second temperature time series, creating a predicted second temperature time series indicating the future change of the second temperature time series, - determining predicted saturation temperature values (T sat) for the elements of the predicted second temperature time series, as switch-off times (t ki), using the neural network and the first temperature time series and selecting from the determined predicted saturation temperature values (T sat) 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 corresponding to the selected predicted saturation temperature value (T sat *) and then switching off the temperature control device at the determined switch-off time (t ki), - determining predicted saturation temperature values (T sat) for the elements of the predicted first temperature time series, as switch-on times (t be), using the neural network and the second temperature time series and selecting from the determined predicted saturation temperature values (T sat) 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 corresponding to the selected predicted saturation temperature value (T sat *) and then switching on the temperature control device at the determined switch-on time (t be). - setting the temperature of the room within the comfort temperature range by means of the temperature control device, then - switching off the temperature control device at a switch-off time (t ki) and measuring the change in the temperature of the room at least until the measured saturation temperature (T sat), which can be measured when the direction of the temperature change is changing, is reached, and a first temperature time series is generated from the measured temperature data, then using a neural network and using the first temperature time series, creating a predicted first temperature time series indicating the future change of the first temperature time series, - switching on the temperature control device at the switch-on time (t be) and measuring the change in temperature of the room, at least until the measured saturation temperature (T sat), which can be measured when the direction of the temperature change is changing, is reached, and a second temperature time series is generated from the measured temperature data, then using a neural network and using the second temperature time series, creating a predicted second temperature time series indicating the future change of the second temperature time series, - determining predicted saturation temperature values (T sat) for the elements of the predicted second temperature time series, as switch-off times (t ki), using the neural network and the first temperature time series and selecting from the determined predicted saturation temperature values (T sat) 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 corresponding to the selected predicted saturation temperature value (T sat *) and then switching off the temperature control device at the determined switch-off time (t ki), - determining predicted saturation temperature values (T sat) for the elements of the predicted first temperature time series, as switch-on times (t be), using the neural network and the second temperature time series and selecting from the determined predicted saturation temperature values (T sat) 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 corresponding to the selected predicted saturation temperature value (T sat *) and then switching on the temperature control device at the determined switch-on time (t be).