Abstract
This paper addresses Challenge 3 of the SMC data challenge by leveraging data-driven tools to understand the relationships between our built environment and nature, and how this relationship impacts energy consumption. It presents detailed results to the research questions posed, along with the rationale for the tools used and limitations of the developed solutions.
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Acknowledgments
The authors would like to acknowledge the Pinnacle Scholar Program at the Stevens Institute of Technology for their support of this work. The authors would also like to thank Dr. Philip Odonkor for his mentorship and support of this work.
Support for DOI 10.13139/ORNLNCCS/1619243 dataset is provided by the U.S. Department of Energy, project SMC2020 under Contract DE-AC05-00OR22725. Project SMC2020 used resources of the Oak Ridge Leadership Computing Facility at Oak Ridge National Laboratory, which is supported by the Office of Science of the U.S. Department of Energy under Contract No. DE-AC05-00OR22725.
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APPENDIX
APPENDIX
This bounding box used to graphically bound our weather data was developed using the following coordinates: (41.858452, -87.641479), (41.858452, -87.617188), (41.891693, -87.641479), (41.891693, -87.617188).
Source code and comprehensive set of visualizations and animations have been open-sourced and can be accessed via our Github page.
Github Link - https://bit.ly/3hGEwo0
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Inneo, S., Wadler, D., Schneiderhan, J., Estevez, R. (2020). The Macro Impacts of Micro-Climates on the Energy Consumption of Urban Buildings. In: Nichols, J., Verastegui, B., Maccabe, A.‘., Hernandez, O., Parete-Koon, S., Ahearn, T. (eds) Driving Scientific and Engineering Discoveries Through the Convergence of HPC, Big Data and AI. SMC 2020. Communications in Computer and Information Science, vol 1315. Springer, Cham. https://doi.org/10.1007/978-3-030-63393-6_32
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DOI: https://doi.org/10.1007/978-3-030-63393-6_32
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