With the development of general-purpose computing of GPUs (graphics processing units), power consumption measurements and optimization have become an essential issue in the green computing field. The current power consumption of GPUs is mainly measured by the hardware. However the programmers have had difficulty understanding the power consumption profile of the applications used to optimize and refactor before the compile phase. To solve this issue, power consumption models were proposed for GPU applications with regard to sparseness- branch and denseness-branch programs based on program slicing, respectively. The program slicing is fine-grained level that lies between the function and the instruction levels and has good feasibility and accuracy in the power consumption estimation. The power consumption prediction models for program slicing were proposed through no-linear regression and wavelet neural networks. To specific GPUs, the power prediction model based on no-linear regression is more precise than the prediction model based on wavelet neural networks. However the wavelet neural networks model has better generality to various kinds of GPUs. After analyzing the structure of the applications, the weighted power model for sparseness-branch programs was provided to achieve better effectiveness. The probability slicing power model for denseness-branch programs was also proposed to improve the accuracy that is based on the probability of the execution paths. The results indicate that the two different models can effectively predict the power consumption. And the average relative error between the predicted value and the measured value is less than 6%.