Multi-Quantile Recurrent Neural Network for Feeder-Level Probabilistic
Energy Disaggregation Considering Roof-Top Solar Energy
Abstract
The purpose of feeder-level energy disaggregation is to decouple the net
load measured at the feeder-head into various components. This
technology is vital for power system utilities since increased
visibility of controllable loads enables the realization of demand-side
management strategies. However, energy disaggregation at the feeder
level is difficult to realize since the high-penetration of embedded
generation masks the actual demand and different loads are highly
aggregated. In this paper, the solar energy at the grid supply point is
separated from the net load at first via either an unsupervised
upscaling method or the supervised gradient boosting regression tree
(GBRT) method. To deal with the uncertainty of the load components, the
probabilistic energy disaggregation models based on multi-quantile
recurrent neural network model (multi-quantile long short-term memory
(MQ-LSTM) model and multi-quantile gated recurrent unit (MQ-GRU) model)
are proposed to disaggregate the demand load into controlled loads
(TCLs), non-thermostatically controlled loads (non-TCLs), and
non-controllable loads. A variety of relevant information, including
feeder measurements, meteorological measurements, calendar information,
is adopted as the input features of the model. Instead of providing
point prediction, the probabilistic model estimates the conditional
quantiles and provides prediction intervals. A comprehensive case study
is implemented to compare the proposed model with other state-of-the-art
models (multi-quantile convolutional neural network (MQ-CNN), quantile
gradient boosting regression tree (Q-GBRT), Quantile Light gradient
boosting machine (Q-LGB)) from training time, reliability, sharpness,
and overall performance aspects. The result shows that the MQ-LSTM can
estimate reliable and sharp Prediction Intervals for target load
components. And it shows the best performance among all algorithms with
the shortest training time. Finally, a transfer learning algorithm is
proposed to overcome the difficulty to obtain enough training data, and
the model is pre-trained via synthetic data generated from a public
database and then tested on the local dataset. The result confirms that
the proposed energy disaggregation model is transferable and can be
applied to other feeders easily.