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A Model-Based Predictive Control Method for Thermal Environment in Low-Energy Buildings
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 an ultra-low energy public building as the case study, a physics-based model was established based on the measured datasets in order to provide databases for training data-driven modelling, which is a model-based predictive control (MPC) method established via neural network. After calibration, predictions of thermal comfort and loads under different temperature settings were obtained in summer and winter, based on input parameters of time, outdoor temperature and humidity, solar radiation and indoor humidity. 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.
A Model-Based Predictive Control Method for Thermal Environment in Low-Energy Buildings
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 an ultra-low energy public building as the case study, a physics-based model was established based on the measured datasets in order to provide databases for training data-driven modelling, which is a model-based predictive control (MPC) method established via neural network. After calibration, predictions of thermal comfort and loads under different temperature settings were obtained in summer and winter, based on input parameters of time, outdoor temperature and humidity, solar radiation and indoor humidity. 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.
A Model-Based Predictive Control Method for Thermal Environment in Low-Energy Buildings
Environ Sci Eng
Wang, Liangzhu Leon (editor) / Ge, Hua (editor) / Zhai, Zhiqiang John (editor) / Qi, Dahai (editor) / Ouf, Mohamed (editor) / Sun, Chanjuan (editor) / Wang, Dengjia (editor) / Chen, Yibo (author) / Berardi, Umberto (author) / Liang, Er (author)
International Conference on Building Energy and Environment ; 2022
Proceedings of the 5th International Conference on Building Energy and Environment ; Chapter: 155 ; 1499-1507
2023-09-05
9 pages
Article/Chapter (Book)
Electronic Resource
English
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