  Distribution feeder long-term load forecast (LTLF) is a critical task many
electric utility companies perform on an annual basis. The goal of this task is
to forecast the annual load of distribution feeders. The previous top-down and
bottom-up LTLF methods are unable to incorporate different levels of
information. This paper proposes a hybrid modeling method using sequence
prediction for this classic and important task. The proposed method can
seamlessly integrate top-down, bottom-up and sequential information hidden in
multi-year data. Two advanced sequence prediction models Long Short-Term Memory
(LSTM) and Gated Recurrent Unit (GRU) networks are investigated in this paper.
They successfully solve the vanishing and exploding gradient problems a
standard recurrent neural network has. This paper firstly explains the theories
of LSTM and GRU networks and then discusses the steps of feature selection,
feature engineering and model implementation in detail. In the end, a
real-world application example for a large urban grid in West Canada is
provided. LSTM and GRU networks under different sequential configurations and
traditional models including bottom-up, ARIMA and feed-forward neural network
are all implemented and compared in detail. The proposed method demonstrates
superior performance and great practicality.
