  In modern distribution systems, load uncertainty can be fully captured by
micro-PMUs, which can record high-resolution data; however, in practice,
micro-PMUs are installed at limited locations in distribution networks due to
budgetary constraints. In contrast, smart meters are widely deployed but can
only measure relatively low-resolution energy consumption, which cannot
sufficiently reflect the actual instantaneous load volatility within each
sampling interval. In this paper, we have proposed a novel approach for
enriching load data for service transformers that only have low-resolution
smart meters. The key to our approach is to statistically recover the
high-resolution load data, which is masked by the low-resolution data, using
trained probabilistic models of service transformers that have both high and
low-resolution data sources, i.e, micro-PMUs and smart meters. The overall
framework consists of two steps: first, for the transformers with micro-PMUs, a
Gaussian Process is leveraged to capture the relationship between the
maximum/minimum load and average load within each low-resolution sampling
interval of smart meters; a Markov chain model is employed to characterize the
transition probability of known high-resolution load. Next, the trained models
are used as teachers for the transformers with only smart meters to decompose
known low-resolution load data into targeted high-resolution load data. The
enriched data can recover instantaneous load uncertainty and significantly
enhance distribution system observability and situational awareness. We have
verified the proposed approach using real high- and low-resolution load data.
