A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting

TL;DR

This paper introduces a hybrid deep learning model combining exponential smoothing, dilated LSTM, and ensembling for improved mid-term electric load forecasting, demonstrating superior performance over classical and machine learning models.

Contribution

The work presents a novel hierarchical hybrid model integrating ETS, dilated LSTM, and ensembling for enhanced load forecasting accuracy.

Findings

Outperforms classical models like ARIMA and ETS

Achieves high accuracy on European electricity demand data

Effective long-term seasonal relationship capture

Abstract

This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting. The model combines exponential smoothing (ETS), advanced Long Short-Term Memory (LSTM) and ensembling. ETS extracts dynamically the main components of each individual time series and enables the model to learn their representation. Multi-layer LSTM is equipped with dilated recurrent skip connections and a spatial shortcut path from lower layers to allow the model to better capture long-term seasonal relationships and ensure more efficient training. A common learning procedure for LSTM and ETS, with a penalized pinball loss, leads to simultaneous optimization of data representation and forecasting performance. In addition, ensembling at three levels ensures a powerful regularization. A simulation study performed on the monthly electricity demand time series for 35 European countries confirmed the high performance of the proposed model and its competitiveness with classical models such as ARIMA and ETS as well as state-of-the-art models based on machine learning.