Abstract: Due to the rapid development of big data in the data center, power consumption of storage system is a major issue in today’s datacenters. How to reduce the power consumption of storage systems has become an urgent issue and a hot research topic in the field of computer science. As the hard disk drive is the primary storage medium in today’s storage systems, modeling hard-disk power consumption is attracting more attention in the current state of research. The accurate power consumption model of disk can not only solve the problem of power matching in data center devices, but also estimate the accuracy of energy-efficient solutions. We develop a statistic-based hard-disk power modeling method that estimates the power consumption of storage workloads. The model makes up the weakness of traditional fine-grained model and it is more accurate than the coarse-grained model. In practical applications, it does not need to record the disk internal activities, and does not need to trace complex parameter. Our power estimation results are highly accurate, which means error of 3% and the model is applicable to the synchronous IO and asynchronous IO. Moreover, our model can also be applied to various online storage systems and data center.