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.
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Foundation item: the Special Program for Innovation Methodology of the Ministry of Science and Technology of China (No. 2016IM010100)
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Zhang, Z., Zhang, Z., Eikevik, T.M. et al. Ventilation System Heating Demand Forecasting Based on Long Short-Term Memory Network. J. Shanghai Jiaotong Univ. (Sci.) 26, 129–137 (2021). https://doi.org/10.1007/s12204-021-2277-5
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DOI: https://doi.org/10.1007/s12204-021-2277-5