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A computational grid is a large scale federated infrastructure where users execute several types of applications with different submission rates. On the evaluation of solutions for grids, there are not much effort on using realistic workloads for experiments, and most of the time users' activities and applications are not well represented. In this work, we propose a user-based grid workload model which is based on clustering users according to their behaviour in the system and their applications. The results show that according to a new metric proposed, the model quality increases when using clustering and extracting models for the group of users with similar behaviour. Moreover, we compare our user-based modelling with a state-of-the-art system-based modelling approach. We show that by using our user-based model the system load can be easily changed by varying the number of users in the grid, creating different evaluation scenarios without affecting individual users' behaviour. On the other hand, varying the number of users in the system-based model does not affect the system load and change the way individual user's behave on the system, which can result in unrealistic users' activities.