Urban Scale Modeling and Climate Change

Cities are responsible for 67% of global energy use. Building sector accounts for considerable amount of energy consumption, contributing to adverse environmental and climate change impacts. As the consequences of climate change increase, there is an increased focus on the building sector, a major negative contributor and a major positive solution provider. Currently, the knowledge regarding building energy use at the urban scale in accordance to climate change projections is scarce. Understanding urban building energy use provides perspective about a city’s energy demand which is beneficial for climate change reduction goals and policies, design of distributed energy resources, and utility planning.

In addition to use phase energy, new materials entering into urban building stocks because of maintenance and renovation, and wastes generated as result of demolition have significant environmental impacts. Therefore, there is an expressed need to analyze material stock and material flows of existing buildings at an urban scale. Knowledge of where and what types of building materials existing in a city may allow for more sustainable maintenance, demolition, and waste management.

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)

Urban Building Energy Model
Energy Use & Climate Change
Building Material Stock Analysis
Urban Building Energy Model

There is growing attention to urban building energy modeling; however, data scarcity and dependency on assumptions and secondary data are the identified challenges. In addition, focusing on commercial buildings that have more complex properties and systems will enhance understanding of building energy use at urban scale. The goal of this research is to propose a novel photogrammetry and image processing framework to retrieve essential envelope properties such as window to wall ratio, floor count, and wall materials to mitigate assumptions and secondary data. Other goals of this research were validating the simulation results based on actual energy use data and employing the urban building energy model to assess impacts of energy conservation strategies on energy use of commercial buildings at urban scale.

The results from the urban building energy model show that total energy use and energy use by different end uses correlate with the commercial building use types. The energy use intensity of the commercial building use types ranges from 74 and 1302 kWh/m2. Moreover, variations in the total energy use of commercial buildings with the same use type are because of unique characteristics or properties of buildings like window to wall ratio, orientation, floor counts, and wall materials.

The average energy use intensity of the commercial buildings can be reduced between 2% and 10% by implementing low to medium cost energy conservation strategies: adjusting setpoint temperature, upgrading to LED lighting, and reducing plug and process loads.

Energy Use & Climate Change

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.

Figure 6.jpg

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.

Associated Publications

Mohammadiziazi, R., Bilec, M.M.* (2022). “Building Material Stock Analysis Is Critical for Effective Circular Economy Strategies: A Comprehensive Review.” Environmental Research: Infrastructure and Sustainability. https://doi.org/10.1088/2634-4505/ac6d08.

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.

Mohammadiziazi, R., Copeland, S.+, Bilec, M.M.* (2021). “Urban building energy model: Database development, validation, and application for commercial building stock.” Energy and Buildings, 248:111175. https://doi.org/10.1016/j.enbuild.2021.111175

Mohammadiziazi, R., Bilec, M.M.* (2020). "Application of Machine Learning for Predicting Building Energy Use at Different Temporal and Spatial Resolution under Climate Change in USA." Buildings, 10 (8), 139. https://doi.org/10.3390/buildings10080139

Mohammadiziazi, R., Bilec, M.M.* (2019). "Developing a framework for urban building life cycle energy map with a focus on rapid visual inspection and image processing." Procedia CIRP, 80, 464-469. https://doi.org/10.1016/j.procir.2019.01.048