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Smart city article details
| Title | Spatial And Temporal Modeling Of Urban Building Energy Consumption Using Machine Learning And Open Data |
|---|---|
| ID_Doc | 52403 |
| Authors | Roth J.; Bailey A.; Choudhary S.; Jain R.K. |
| Year | 2019 |
| Published | Computing in Civil Engineering 2019: Smart Cities, Sustainability, and Resilience - Selected Papers from the ASCE International Conference on Computing in Civil Engineering 2019 |
| DOI | http://dx.doi.org/10.1061/9780784482445.059 |
| Abstract | Understanding the spatial and temporal distribution of energy consumption in cities is critical to facilitate the identification of potential energy saving opportunities and planning of new renewable and integrated district energy systems. Previous work analyzing urban building energy usage has been largely limited to either modeling of individual buildings at granular temporal scales (i.e., hourly or less) or an entire stock of urban buildings at the yearly temporal scale. While such analyses are valuable, their lack of both spatial and temporal granular modeling limits their applicability in planning and design of integrated district energy systems. This paper proposes a new urban building energy model that produces hourly demand profiles for the building stock of New York City (NYC) using only open publicly available data. First, we utilize a machine learning model to predict annual energy consumption of NYC's entire building stock from a subset of buildings that have publicly available annual energy usage data. We validate this part of the model using city-wide electricity data from New York Independent System Operator (NYISO). Results show that random forests have the best building-level prediction accuracy with a mean log squared error of 0.293. Next, we apply a novel optimization algorithm to construct temporal granular hourly profiles using the Department of Energy's commercial and residential simulation building reference sets, and the predicted annual energy values from the random forests model. Results indicate that we are able to achieve an error rate of ~10% (MAPE) in comparison to the overall hourly electricity profile of NYC. Moreover, we found that our iterative approach demonstrates that error rates diminish as buildings are added to the aggregated profile, which underscores the merits of applying our proposed method to model the entire building stock of a city rather than an individual building. In the end, our proposed method takes the first step of large-scale spatial and highly granular temporal characterization of urban building energy usage. © 2019 American Society of Civil Engineers. |
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