Clustering and Visualisation of Electricity Data to identify Demand Response Opportunities
Almir Mehanovic, Emil Sebastian Rømer, Jakob Hviid, Mikkel Baun Kjærgaard · 2016
Electricity grids are facing challenges due to peak consumption and renewable electricity generation. In this context, demand response offers a solution to many of the challenges, by enabling the integration of consumer side flexibility in grid management. Retail buildings are good candidates for providing flexible demand due to their volume and the stability of their loads. However, new methods are needed to efficiently identify demand response opportunities in retail buildings. In this poster we outline a data-driven method based on clustering and visualisation that generates day type profiles from raw electricity consumption data. The day type profiles among others enable analysis of the repeatability and seasonal variation of building loads. Proposing such a method is a step towards enabling a higher penetration of intelligent smart grid solutions in the retail sector.