Attention-based Neural Load Forecasting: A Dynamic Feature Selection Approach

TL;DR

This paper introduces an attention-based neural network model for short-term load forecasting that adaptively selects relevant features and historical information, significantly improving prediction accuracy in power systems.

Contribution

The paper proposes a novel attention mechanism within an encoder-decoder RNN for dynamic feature selection in multi-horizon load forecasting.

Findings

Model outperforms existing schemes on energy forecasting data

Attention mechanism improves feature relevance identification

Enhanced forecasting accuracy demonstrated on real dataset

Abstract

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.