Abstract
The goal of this research is to determine the prominent problems and challenges of the low-load homes in the aspects of high performance ventilation systems and indoor air quality strategies. The authors will first categorize the residential buildings according to their load capacities. The characteristics of the energy-consumption mode that residents value the most will also be investigated. Data will be gathered through accessing the database of building permits, approval, and commissioning. Data for space heating and cooling load information and designed occupancy can also be collected through sensors. Big data analysis tools will be used to examine the relationship between the construction technology selections and the importance of certain design decision factors. Building Information Modeling (BIM) technology will be implemented to simulate the alternative strategies to conventional central ducted space conditioning systems that will provide thermal comfort for the occupants.
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Xie, H., Liang, T., Li, H., Shi, Y. (2015). Understanding Air Quality Challenges Through Simulation and Big Data Science for Low-Load Homes. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_59
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DOI: https://doi.org/10.1007/978-3-319-23862-3_59
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