Quantum mechanics

Algorithm

Residual

Smoothing

Time series

Regularization (linguistics)

Convergence (economics)

Economics

Computer science

Physics

Politics

Political science

Exponential smoothing

Autoregressive integrated moving average

Artificial intelligence

Representation (politics)

Deep learning

Economic growth

Term (time)

Machine learning

Computer vision

IEEE Transactions on Neural Networks and Learning Systems

This work presents a hybrid and hierarchical deep learning model for midterm 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. Multilayer 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.

A Hybrid Residual Dilated LSTM and Exponential Smoothing Model for Midterm Electric Load Forecasting