The increasing use of renewable energy and the spread of smart energy services require detailed studies of the system load. The data from the advanced metering infrastructure assist these requirements by providing detailed picture of electricity consumption patterns and profiles. However, high resolution electricity data can cause computational challenges and privacy concerns. As a result, the data are often spatially aggregated. This paper investigates the impact of data aggregation on the data understanding and the electricity load characteristics. The study looks at the similarity among different groups combinations within the same aggregation level, the variation in the load diversity and peaks occurrences, and on the hourly electricity variations between the individual customer and its aggregated group. The study concludes that the individual customers' behaviors are lost with the increasing levels of aggregation, and that the similarity among groups on the same aggregation level increases with the aggregation level.

Data generated in the cities have great potential to assist in studies on urban metabolism and urban energy transition. Several cities around the world have already adopted the open data portals to share data and increase their capabilities. However, much of these data are of low resolution, diverse formats, and mostly lack real energy measurements of buildings. In this paper, we address this issue by using an example of Energy Hub data portal "NRGYHUB", an urban energy portal for open data for the city of Västerås, Sweden. Granular electricity, district heating and water consumption data were collected and matched to their corresponding buildings. The data are stored into a database and will be available for public through a GIS-driven interface. The challenges that were faced during the data access process are briefly described. The potential of NRGYHUB data portal as a tool to develop urban policies is discussed.