TR2016-053
A Humidity Integrated Building Thermal Model
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- "A Humidity Integrated Building Thermal Model", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525127, July 2016, pp. 1492-1499.BibTeX TR2016-053 PDF
- @inproceedings{Xu2016jul,
- author = {Xu, Jingyang and Nikovski, Daniel N.},
- title = {A Humidity Integrated Building Thermal Model},
- booktitle = {American Control Conference (ACC)},
- year = 2016,
- pages = {1492--1499},
- month = jul,
- doi = {10.1109/ACC.2016.7525127},
- url = {https://www.merl.com/publications/TR2016-053}
- }
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- "A Humidity Integrated Building Thermal Model", American Control Conference (ACC), DOI: 10.1109/ACC.2016.7525127, July 2016, pp. 1492-1499.
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Abstract:
A building thermal simulation model is proposed in this paper to predict temperature and humidity for short term response under varying environment inputs. This model is composite of a thermal circuit model to predict zone temperature and a enhanced BRE admittance model to predict zone humidity. Various environment factors such as weather, human activity, radiation, moisture absorption/desorption, ventilation, and condensation are considered. The training and prediction procedures are accelerated with approximations. In both laboratory and field tests, the model shows good performance on temperature and humidity estimation. To the best of our knowledge, it is the first time that a grey-box building thermal model can provide accurate humidity prediction with time step as short as 5 minutes.
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NEWS MERL makes a strong showing at the American Control Conference Date: July 6, 2016 - July 8, 2016
Where: American Control Conference (ACC)
MERL Contacts: Scott A. Bortoff; Petros T. Boufounos; Stefano Di Cairano; Abraham Goldsmith; Christopher R. Laughman; Daniel N. Nikovski; Arvind Raghunathan; Yebin Wang; Avishai Weiss
Research Areas: Control, Dynamical Systems, Machine LearningBrief- The premier American Control Conference (ACC) takes place in Boston July 6-8. This year MERL researchers will present a record 20 papers(!) at ACC, with several contributions, especially in autonomous vehicle path planning and in Model Predictive Control (MPC) theory and applications, including manufacturing machines, electric motors, satellite station keeping, and HVAC. Other important themes developed in MERL's presentations concern adaptation, learning, and optimization in control systems.