J Shanghai Jiaotong Univ Sci

Journal of Shanghai Jiaotong University(Science) >

2021 , Vol. 26 >Issue 2: 129 - 137

DOI: https://doi.org/10.1007/s12204-021-2277-5

Energy Engineering, Mechanics & Materials

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

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  • (1. School of Mechanical Engineering, Shanghai Jiao Tong University, Shanghai 200240, China; 2. Student Innovation
    Center, Shanghai Jiao Tong University, Shanghai 200240, China; 3. Department of Energy and Process Engineering,
    Norwegian University of Science and Technology, Trondheim 7491, Norway)

Online published: 2021-03-24

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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

Cite this article

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]. Journal of Shanghai Jiaotong University(Science), 2021 , 26(2) : 129 -137 . DOI: 10.1007/s12204-021-2277-5

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