Power consumption of disk systems is an important issue in scientific computing where data-intensive applications exercise disk storage extensively. While one can spin down idle disks when idleness is detected, spinning up them takes many cycles and consumes extra power. Therefore, it can be very useful in practice to improve disk reuse, that is, using the same set of disks as much as possible. If this can be achieved, unused disks can be held in the so called spin-down mode for longer durations of time, and this helps increase power savings. This paper proposes an approach for reducing disk power consumption by increasing disk reuse. The proposed approach restructures a given application code considering the disk layouts of the datasets it manipulates. We implemented this disk layout-conscious approach within a publicly-available compilation framework and compared it against a conventional data reuse optimization approach (which is also implemented using the same compiler) using six scientific applications that perform disk I/O. The results collected so far indicate that our layout-conscious approach and the conventional data reuse optimization approach reduce the disk energy consumption by 25.3% and 10.3%, respectively, on average, over the case where no disk power optimization is applied. The corresponding savings in total energy consumption (including CPU, memory and network energies) are 6.5% for the conventional approach and 16.5% for our disk layout-conscious approach. Our experimental evaluation also shows that the savings obtained are consistent with varying number of disks and alternate disk layouts.