Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting

TL;DR

This paper introduces a novel hybrid neural network model that combines exponential smoothing with advanced RNN architectures, incorporating dynamic attention and contextual information to improve short-term load forecasting accuracy.

Contribution

The paper presents a new contextually enhanced hybrid RNN model with hierarchical dilations and attentive cells, outperforming existing models in short-term load forecasting tasks.

Findings

Outperforms traditional statistical models in accuracy.

Effectively captures seasonal and long-term dependencies.

Provides reliable point forecasts and predictive intervals.

Abstract

In this paper, we propose a new short-term load forecasting (STLF) model based on contextually enhanced hybrid and hierarchical architecture combining exponential smoothing (ES) and a recurrent neural network (RNN). The model is composed of two simultaneously trained tracks: the context track and the main track. The context track introduces additional information to the main track. It is extracted from representative series and dynamically modulated to adjust to the individual series forecasted by the main track. The RNN architecture consists of multiple recurrent layers stacked with hierarchical dilations and equipped with recently proposed attentive dilated recurrent cells. These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information. The model produces both point forecasts and predictive intervals. The experimental part of the work performed on 35 forecasting problems shows that the proposed model outperforms in terms of accuracy its predecessor as well as standard statistical models and state-of-the-art machine learning models.