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

arXiv: https://arxiv.org/abs/2004.00508 · HTML: https://mlsanity.com/paper/2004.00508/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 29 Mar 2020.

## Built on by (most cited first, 7 of 7)

- [Deep Autoregressive Models with Spectral Attention](https://mlsanity.com/paper/2107.05984/) (2021)
- [ES-dRNN with Dynamic Attention for Short-Term Load Forecasting](https://mlsanity.com/paper/2203.00937/) (2022)
- [Randomized Neural Networks for Forecasting Time Series with Multiple Seasonality](https://mlsanity.com/paper/2107.01705/) (2021)
- [Neural Multi-Quantile Forecasting for Optimal Inventory Management](https://mlsanity.com/paper/2112.05673/) (2021)
- [Automated Few-Shot Time Series Forecasting based on Bi-level Programming](https://mlsanity.com/paper/2203.03328/) (2022)
- [Context-Aware Ensemble Learning for Time Series](https://mlsanity.com/paper/2211.16884/) (2022)
- [Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study](https://mlsanity.com/paper/2203.09170/) (2022)

## Abstract (arXiv)

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.