Name
Title | ||
|---|---|---|
Understanding Air Quality Challenges Through Simulation and Big Data Science for Low-Load Homes. |
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. |
Year | DOI | Venue |
|---|---|---|
2015 | 10.1007/978-3-319-23862-3_59 | IScIDE |
Keywords | Field | DocType |
Big data science, Simulation, Low-Load homes, Air quality challenges | Ventilation (architecture),Simulation,Computer science,Occupancy,Thermal comfort,Air quality index,Cooling load,Architectural engineering,Building information modeling,Big data,Indoor air quality | Conference |
Volume | ISSN | Citations |
9243 | 0302-9743 | 0 |
PageRank | References | Authors |
0.34 | 1 | 4 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Haiyan Xie | 1 | 4 | 1.92 |
| Tingting Liang | 2 | 0 | 0.34 |
| Hui Li | 3 | 1 | 0.71 |
| Yao Shi | 4 | 124 | 13.96 |