  Encoder-decoder-based recurrent neural network (RNN) has made significant
progress in sequence-to-sequence learning tasks such as machine translation and
conversational models. Recent works have shown the advantage of this type of
network in dealing with various time series forecasting tasks. The present
paper focuses on the problem of multi-horizon short-term load forecasting,
which plays a key role in the power system's planning and operation. Leveraging
the encoder-decoder RNN, we develop an attention model to select the relevant
features and similar temporal information adaptively. First, input features are
assigned with different weights by a feature selection attention layer, while
the updated historical features are encoded by a bi-directional long short-term
memory (BiLSTM) layer. Then, a decoder with hierarchical temporal attention
enables a similar day selection, which re-evaluates the importance of
historical information at each time step. Numerical results tested on the
dataset of the global energy forecasting competition 2014 show that our
proposed model significantly outperforms some existing forecasting schemes.
