Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network

doi:10.1007/s12204-021-2277-5

Abstract

Abstract: 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.

Key words: ventilation system| load forecasting| long short-term memory (LSTM)| walk-forward forecasting

摘要： 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.

关键词: ventilation system| load forecasting| long short-term memory (LSTM)| walk-forward forecasting

CLC Number:

TU 833

ZHANG Zhanluo (张战罗), ZHANG Zhinan (张执南), EIKEVIK Trygve Magne, SMITT Silje Marie. Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network[J]. J Shanghai Jiaotong Univ Sci, 2021, 26(2): 129-137.

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