Smarter City Predictive Analytics using Generalized Additive Model

Bei Chen (IBM )

Friday 9th May, 2014 15:00-16:00 Maths 204


Establishing efficient energy and transportation systems are key
challenges for accommodating the fast-growing population living in cities.
Timely and accurate forecasting of energy and transportation systems can
effectively alleviate energy dependence, air pollution and traffic problems
of large cities. In this talk, I will present a class of big data
predictive algorithms for smarter city applications based on the
Generalized Additive Models (GAMs) (Tibshirani and Hastie, 1990). The first
application is short-term electricity load forecasting at various
aggregation levels in the electric grid, ranging from highly aggregated
series (national and regional demand), clusters of smart meters to
individual buildings. The second application focuses on multi-modal
transportation networks, including prediction of shared bike schemes and of
urban traffic. For both applications, I will discuss in detail methods for
real-time model selection from a large number of covariates and uncertainty
predictions. Also, I will highlight open problems in smarter city
predictive analytics for future research.

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