  Long-term load forecast (LTLF) for area distribution feeders is one of the
most critical tasks frequently performed in electric distribution utility
companies. For a specific planning area, cost-effective system upgrades can
only be planned out based on accurate feeder LTLF results. In our previous
research, we established a unique sequence prediction method which has the
tremendous advantage of combining area top-down, feeder bottom-up and
multi-year historical data all together for forecast and achieved a superior
performance over various traditional methods by real-world tests. However, the
previous method only focused on the forecast of the next one-year. In our
current work, we significantly improved this method: the forecast can now be
extended to a multi-year forecast window in the future; unsupervised learning
techniques are used to group feeders by their load composition features to
improve accuracy; we also propose a novel selective sequence learning mechanism
which uses Gated Recurrent Unit network to not only learn how to predict
sequence values but also learn to select the best-performing sequential
configuration for each individual feeder. The proposed method was tested on an
actual urban distribution system in West Canada. It was compared with
traditional methods and our previous sequence prediction method. It
demonstrates the best forecasting performance as well as the possibility of
using sequence prediction models for multi-year component-level load forecast.
