Short-Term Load Forecasting with Missing Data using Dilated Recurrent Attention Networks
Permanent link
https://hdl.handle.net/10037/19949Date
2020-02-06Type
Conference objectKonferansebidrag
Abstract
The goal of this paper is to provide a robust RNN architecture against the bias from missing data.
We propose Dilated Recurrent Attention Networks (DRAN).
The proposed model has a stacked structure of multiple RNNs which layer of each having a different length of internal connections.
This structure allows incorporating previous information at different time scales.
DRAN updates its state by a weighted average of the layers. In order to focus more on the layer that carries reliable information against bias from missing data, it leverages attention mechanism which learns the distribution of attention weights among the layers. We report that our model outperforms conventional ones with respect to the forecast accuracy from two benchmark datasets, including a real-world electricity load dataset.