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This content will become publicly available on August 31, 2024

Title: Value of accurate urban weather prediction in the day-ahead energy market
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.  more » « less
Award ID(s):
1919453
NSF-PAR ID:
10456127
Author(s) / Creator(s):
; ;
Date Published:
Journal Name:
ICASP14 - 14th International Conference on Applications of Statistics and Probability in Civil Engineering
Format(s):
Medium: X
Sponsoring Org:
National Science Foundation
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