Urban Scale Modeling & Climate Change
This work is funded by the National Science Foundation:
Collaborative Research: Climate Impacts on the Urban Built Environment (Grant No. 2035150)
Collaborative Research: Growing Convergence Research: Convergence Around the Circular Economy (Grant No. 1934824)
Approaches to predicting building energy use range from linear regression to advanced machine learning approaches such as random forest and extreme gradient boosting. However, disparate methods, temporal resolutions, results, and recommendations have emerged. Furthermore, integrating climate change models into building energy modeling at urban scale is limited. The goal of this research is to select a machine learning model that provides good performance on predicting annual energy use of commercial buildings. Moreover, the predictive power of this model is used in concert with climate change scenarios.
Four prediction models (multiple linear regression, regression tree, random forest, and extreme gradient boosting) were developed based on the Commercial Building Energy Consumption Survey. Comparing the mean absolute error and root mean square error of these models shows that random forest model provided better predicting performance.
In the Commercial Building Energy Consumption Survey, climate regions have low spatial resolution. Therefore, to implement the random forest model for predicting commercial buildings energy use, new geographic regions with higher spatial resolution were created. Under the highest scenario established by the Intergovernmental Panel for Climate Change (RCP8.5), the majority of geographic regions within U.S. will experience increase in energy use intensity compared to 2012. However, the magnitude of increase varies for different regions which shows the importance of regional mitigation policies and plans for commercial buildings.
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Mohammadiziazi, R., Bilec, M.M.* (2021). “Integrating Climate Change with Urban Building Energy Modeling: Case of A Commercial Building Stock.” Proceedings of the 17th IBPSA Conference. https://doi.org/10.26868/25222708.2021.30869.
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