To overcome the challenges due to increasing energy consumption of High Performance Computing systems, we propose an automatic tools-aided approach to tune dynamic applications. We select best system configurations for groups of instances of code regions with similar computational characteristics, and then predict the application behavior for unseen instances during production runs. Our approach reduces the tuning time and effort, and improves both energy-efficiency and performance by up to 20%.