  Variational autoencoder (VAE) neural networks can be trained to generate
power system states that capture both marginal distribution and multivariate
dependencies of historical data. The coordinates of the latent space codes of
VAEs have been shown to correlate with conceptual features of the data, which
can be leveraged to synthesize targeted data with desired features. However,
the locations of the VAEs' latent space codes that correspond to specific
properties are not constrained. Additionally, the generation of data with
specific characteristics may require data with corresponding hard-to-get labels
fed into the generative model for training. In this paper, to make data
generation more controllable and efficient, an oriented variation autoencoder
(OVAE) is proposed to constrain the link between latent space code and
generated data in the form of a Spearman correlation, which provides increased
control over the data synthesis process. On this basis, an importance sampling
process is used to sample data in the latent space. Two cases are considered
for testing the performance of the OVAE model: the data set is fully labeled
with approximate information and the data set is incompletely labeled but with
more accurate information. The experimental results show that, in both cases,
the OVAE model correlates latent space codes with the generated data, and the
efficiency of generating targeted samples is significantly improved.
