Abstract
Due to the increasing demands of energy conservation and emission reduction, the efficient control of indoor thermal environment aims at realizing thermal comfort with the least energy consumption. Taking the ground source heat pump system operated in an ultra-low energy public building as the case study, a physical model was established based on the measured datasets in order to provide databases for training data-driven modeling, which is a model-based predictive control (MPC) model established based on neural network. After calibration, predictions of thermal comfort and loads under different temperature settings were obtained in summer and winter season, based on multiple-dimensional input parameters of time, outdoor temperature and humidity, solar radiation and indoor humidity etc. Afterwards, hourly optimal temperature settings were recommended to take good use of the thermal inertia, and thus provide optimal references for the intelligent operation and control of the ground source heat pump system.
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