Performance, prediction, optimization, and user behavior of night ventilation

Performance, prediction, optimization, and user behavior of night ventilation Previous studies have demonstrated a potential reduction in cooling load and improvement in comfort from the implementation of night ventilation. This paper describes the performance, in terms of indoor environmental conditions, of three buildings from both the U.S. and India that use night ventilation as their primary cooling method. The research methods used the following approach: (1) Assess the cooling strategy in relation to the adaptive comfort model; (2) Develop a hybrid model, using both first principle equations and the collected data, to predict the instantaneous air and mass temperatures within each building and use the model to assess performance of the cooling strategy; (3) Determine an optimized ventilation control strategy for each building to minimize energy and maintain comfortable temperatures. (4) Develop a statistical model using collected data to predict the window opening pattern for occupants of a building using natural night ventilation. The study yielded the following results: (1) The buildings in the mild climate are successfully keeping the indoor temperature low, but also tend to be overcooling; (2) The night ventilation strategy has very little impact on indoor conditions of the buildings in the mild climate; (3) The impact of night ventilation is less significant when there is low internal loads and heavy mass; (4) The building in the hot and humid climate is keeping the indoor temperature within the comfort bounds for 88% of the year; (5) The night ventilation strategy has advantageous impact on indoor conditons of the building in the hot and humid climate, but not enough to cool the space on its own; (6) Model predictive control has the potential to further improve the performance of night ventilation. (7) Window opening behavior for the building using natural night ventilation is most heavily dependent on indoor air temperature and mass temperature. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Energy and Buildings Elsevier

Performance, prediction, optimization, and user behavior of night ventilation

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Publisher
Elsevier
Copyright
Copyright © 2018 Elsevier B.V.
ISSN
0378-7788
eISSN
1872-6178
D.O.I.
10.1016/j.enbuild.2018.01.026
Publisher site
See Article on Publisher Site

Abstract

Previous studies have demonstrated a potential reduction in cooling load and improvement in comfort from the implementation of night ventilation. This paper describes the performance, in terms of indoor environmental conditions, of three buildings from both the U.S. and India that use night ventilation as their primary cooling method. The research methods used the following approach: (1) Assess the cooling strategy in relation to the adaptive comfort model; (2) Develop a hybrid model, using both first principle equations and the collected data, to predict the instantaneous air and mass temperatures within each building and use the model to assess performance of the cooling strategy; (3) Determine an optimized ventilation control strategy for each building to minimize energy and maintain comfortable temperatures. (4) Develop a statistical model using collected data to predict the window opening pattern for occupants of a building using natural night ventilation. The study yielded the following results: (1) The buildings in the mild climate are successfully keeping the indoor temperature low, but also tend to be overcooling; (2) The night ventilation strategy has very little impact on indoor conditions of the buildings in the mild climate; (3) The impact of night ventilation is less significant when there is low internal loads and heavy mass; (4) The building in the hot and humid climate is keeping the indoor temperature within the comfort bounds for 88% of the year; (5) The night ventilation strategy has advantageous impact on indoor conditons of the building in the hot and humid climate, but not enough to cool the space on its own; (6) Model predictive control has the potential to further improve the performance of night ventilation. (7) Window opening behavior for the building using natural night ventilation is most heavily dependent on indoor air temperature and mass temperature.

Journal

Energy and BuildingsElsevier

Published: May 1, 2018

References

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