A data mining approach for producing small area statistics-based load profiles for distribution network planning

The recent European Union and national level initiatives such as INSPIRE and PSI have increased the availability of public sector data, which provides interesting new opportunities to support decision making in electricity distribution network planning. With big amounts of available data, data mining methods can be utilised to produce improved spatial load models. We propose a data mining approach, which uses the Self-organizing map for producing representative small area level load profiles based on building characteristics, demographics and automated meter reading data. Furthermore, the k-nearest neighbour algorithm and a genetic algorithm based feature selection are used in order to find a parsimonious set of features that can be used in selecting proper load profile. As the load profiles are based on area level statistics, they can be used to estimate the future loads in different scenarios regarding changes in population and building stock, which is particularly advantageous in distribution network planning.