Load forecasting can increase the efficiency of modern energy systems with built-in measuring systems by providing a more accurate peak power shaving performance and thus more reliable control.An analysis of an integrated CO2 heat pump and chiller system with a hot water storage system is presented in this paper.Drastic power fluctuations,which can be reduced with load forecasting,are found in historical operation records.A model that aims to forecast the ventilation system heating demand is thus established on the basis of a long short-term memory(LSTM)network.The model can successfully forecast the one-hour-ahead power using records of the past 48 h of the system operation data and the ambient temperature.The mean absolute percentage error(MAPE)of the forecast results of the LSTM-based model is 10.70％,which is respectively 2.2％and 7.25％better than the MAPEs of the forecast results of the support vector regression based and persistence method based models.