Program

Tuesday:

9:30-11:00 Session one: Introduction
Examples of using big data in the power industry including modelling demand patterns, forecasting demands, identifying customer profiles, data reduction techniques.

11:30-13:00 Session two: Identifying patterns in data and profiling
Clustering and data reduction techniques that allow information to be retained in smaller, more manageable datasets. Examples will include creating profiles for electricity demands for different customer types. Advanced regression models for allocating customers to different demand profiles with measures of uncertainty.

15:00-17:00 Optional practical sessions using R.
Worked examples of the techniques from Sessions one and two using real datasets.

Wednesday:

9:30-11:00 Session three : Spatial modelling
An introduction to Bayesian hierarchical modelling. Exploiting spatial dependence within data to borrow strength. Techniques for lattice and point referenced data. Mapping. Stationary and non-stationary models. Examples will include modelling spatio-temporal modelling of meteorological factors for demand forecasting and others.

11:30-13:00 Session four: Spatial-temporal modelling
Borrowing information over both space and time. Time series modelling Separable and non-separable models.

15:00-17:00 Optional practical sessions using R.
Worked examples of the techniques from Sessions three and four including visualising the results from spatio-temporal analyses using google maps and others.