In this paper, we present our application of deep neural network to modeling piano performance, which imitates the expressive control of tempo, dynamics, articulations and pedaling from pianists. Our model consists of recurrent neural networks with hierarchical attention and conditional variational autoencoder. The model takes a sequence of note-level score features extracted from MusicXML as input and predicts piano performance features of the corresponding notes. To render musical expressions consistently over long-term sections, we first predict tempo and dynamics in measure-level and, based on the result, refine them in note-level. The evaluation through listening test shows that our model achieves a more human-like expressiveness compared to previous models. We also share the dataset we used for the experiment.