Hot temperatures drive excessive energy use for space-cooling in built environments. In a
building, a system operator could save costs by making better decisions under the uncertainties associated with urban temperature and future energy demands. In this paper, we assess the impact of urban weather modeling on energy cost, using a value of information (VoI) analysis, in a day-ahead (DA) electricity market. To do that, we combine two probabilistic models: (a) a model for forecasting urban temperature and (b) a model for forecasting hourly net electric load of a building given ambient urban temperature. We then quantify the impact of better urban weather modeling by propagating the uncertainty from the temperature model to the load forecasting model. We perform a numerical case study on residential building prototypes located in the city of Pittsburgh. The result indicates that using a better weather model could save 4.34-8.22% of the electricity costs for space-cooling.
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This content will become publicly available on May 18, 2025
Addressing the need for microclimate considerations in DOE reference building prototypes for urban energy simulation with a focus on urban shadow effects.
The U.S. Department of Energy (DOE) offers building reference prototypes for energy use modeling in commercial and residential buildings. However, these reference prototypes have traditionally been treated in isolation, neglecting the impact of neighboring objects on local microclimate. In urban energy models, where the intricate interaction of urban elements significantly shapes environmental conditions, it becomes more important to reconsider the conventional treatment of building reference prototypes. In this paper we aim to discern potential disparities in energy consumption estimations using DOE prototypes at an urban scale. The Urban Modeling Interface (UMI) was chosen as the simulation platform to incorporate the shadow effect from neighboring objects on building energy use across six scenarios with different shadow coverage by neighboring objects. We found that trees as neighboring structures can decrease cooling load by up to 29%. These results highlight the importance of considering the urban context in energy use estimation of buildings.
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- Award ID(s):
- 1855902
- NSF-PAR ID:
- 10517634
- Publisher / Repository:
- IBPSA-USA
- Date Published:
- Journal Name:
- Proceedings of the International Building Performance Simulation Association
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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