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A Method of Trigonometric Modelling of Seasonal Variation Demonstrated with Multiple Sclerosis Relapse Data

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

Grzegorz Dudek, Pawel Pelka, Slawek Smyl

IEEE Transactions on Neural Networks and Learning Systems

|January 8, 2021

Summary

This study introduces a hybrid deep learning model for midterm electricity load forecasting. The novel approach combines exponential smoothing (ETS) and long short-term memory (LSTM) networks, outperforming traditional methods.

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Published on: December 9, 2015

Area of Science:

Artificial Intelligence
Machine Learning
Energy Systems

Background:

Accurate midterm load forecasting is crucial for efficient electricity grid management.
Existing models often struggle to capture complex temporal dependencies and seasonal patterns in electricity demand.
Deep learning offers potential for improved forecasting accuracy but requires specialized architectures.

Purpose of the Study:

To develop and evaluate a hybrid and hierarchical deep learning model for midterm electricity load forecasting.
To enhance the model's ability to capture long-term seasonal relationships and improve training efficiency.
To assess the model's performance against classical and state-of-the-art forecasting methods.

Main Methods:

A hybrid model integrating exponential smoothing (ETS) for time series component extraction and advanced long short-term memory (LSTM) networks.
LSTM architecture features dilated recurrent skip connections and spatial shortcut paths for improved learning of seasonal patterns.
A joint learning procedure using penalized pinball loss for simultaneous optimization of data representation and forecasting accuracy, with three-level ensembling for regularization.

Main Results:

The proposed hybrid ETS-LSTM model demonstrated high performance in midterm load forecasting.
The model effectively captured long-term seasonal relationships in monthly electricity demand data.
Simulation studies on 35 European countries showed the model's competitiveness against ARIMA, ETS, and other machine learning approaches.

Conclusions:

The hybrid and hierarchical deep learning model offers a powerful and accurate solution for midterm electricity load forecasting.
The integration of ETS and advanced LSTM with specific architectural enhancements significantly improves forecasting capabilities.
The model's superior performance and competitiveness highlight its potential for practical application in energy management.