The invention discloses a
tail end space
energy consumption prediction method based on building
total energy consumption, a medium and equipment, and the method comprises the steps of sequentially transmitting preprocessed sample data of N
tail end spaces at a moment t and first tau time steps into a
tail end space
energy consumption prediction model in each step of model training; enabling the model to obtain N tail end space
energy consumption prediction values at the t moment through N times of forward calculation, adding the N tail end space energy consumption prediction values to the actual
total energy consumption of the building to calculate a
loss function, and adjusting parameters of the tail end space energy consumption prediction model through back propagation of a
gradient descent method; repeating the training process on the sample data at all moments until the model converges to the prediction precision, and completing the training of the end space energy consumption prediction model; and predicting the energy consumption generated by the tail end space by using the obtained tail end space energy consumption prediction model through the controller parameters of the tail end devices, the temperature and
humidity sensors,
the Internet weather information, the people flow density, the power of the electric appliances and the lighting devices and the house structure parameters in the operation process of the building heating and ventilation
system. According to the invention, the training of the tail end space energy consumption prediction model is realized underthe condition that the tail end space energy consumption historical data is missing.