High Performance Computing (HPC) resources are housed in large datacenters, which consume exorbitant amounts of energy and are quickly demanding attention from businesses as they result in high operating costs. On the other hand HPC environments have been very useful to researchers in many emerging areas in life sciences such as Bioinformatics and Medical Informatics. In an earlier work, we introduced a dynamic model for energy aware scheduling (EAS) in a HPC environment; the model is domain agnostic and incorporates both the deadline parameter as well as energy parameters for computationally intensive applications. Our proposed EAS model incorporates 2-phases. In the Offline Phase, we use a run profile based approach to generate the initial schedule. In the Online Phase a feedback mechanism is incorporated between the EAS Engine and the master scheduling process. As scheduled tasks are completed, actual execution times are used to adjust the resources required for scheduling remaining tasks using the least number of nodes while meeting a given deadline. In this paper we study the impact of the quality of initial schedule using different run profiles which is the starting point for the EAS algorithm on the number of adjustments which is critical to the overall energy optimization as every adjustment made has an overhead. The conducted experiments show that the proposed approach succeeded in meeting preset deadlines while minimizing the number of nodes; thus reducing overall energy utilized and that choosing the right profile in the Offline phase has an impact on the energy optimization achieved by the EAS algorithm.