By 2050, the world’s population is expected to reach 9.7 billion, with over half living in urban settlements (United Nations, 2015). Planning and designing new urban developments and improving existing infrastructure will create or reshape urban landscapes and will carry significant implications for energy consumption, infrastructure costs, and the urban microclimate on a larger scale. Researchers and industry professionals must recognize how changes in land use affect the urban microclimate and, therefore, building energy consumption. Built environment and microclimate studies commonly involve modeling or experimenting with mass and energy exchanges between natural and the built environment. Current methods to quantify these exchanges include the isolated use of microclimate and building energy simulation tools. However, current urban planning and building design processes lack a holistic and seamless approach to quantifying all thermodynamic interactions between natural and built environments; nor is there a method for communicating and visualizing the simulated building energy data. This dissertation has developed a coupling method to quantify the effects of the urban microclimate on building energy consumption. The coupling method was tested on a medium-sized office building and applied to a design case, a redevelopment project in Pittsburgh, PA. Three distinct approaches were used. First, to develop the coupling method, a study was conducted to quantify the importance of accurate microclimate model initialization for achieving simulation results that represent measured data. This initialization study was conducted for 24 cases in the Pittsburgh climate. The initialization study developed a rule-based method for estimating the number of ENVI-met simulations needed to predict the microclimate for an annual period. Second, a coupling method was developed to quantify these microclimate effects on building energy consumption. The Center for Sustainable Landscapes (CSL) building was used as a test-case for this coupling method to measure improvement in predicting building heating and cooling energy consumption. Results show that the coupling method, more than the TMY3 weather data used for energy simulations, can improve building energy consumption predictions for the winter and summer months. Third, to demonstrate industry implications, the coupling method was applied to a design case, the Lower Hill District Redevelopment, Pittsburgh, PA. Comparing the decoupled energy model and TMY3 weather data revealed a high degree of variation in the heating and cooling energy consumption. Overall results reinforced the hypothesis that building surface level coupling is not essential if the energy model accounts for the microclimate effects. A Design Decision Support (DDS) method was also developed as a tool for project stakeholders to communicate high-fidelity simulated energy data.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-2169 |
| Date | 01 February 2018 |
| Creators | Ramesh, Shalini |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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