Hybrid Modelling Approaches for Forecasting Energy Spot Prices in EPEC market

TL;DR

This paper explores hybrid modeling techniques combining statistical and neural network methods to improve energy spot price forecasting in the EPEC market, using data from 2013-2015.

Contribution

It introduces a hybrid approach integrating multiple models like ARMA, GARCH, and RNNs for energy price prediction, which is novel in this context.

Findings

Hybrid models outperform individual models in accuracy.

Neural networks improve forecast robustness.

Effective for short-term energy price prediction.

Abstract

In this work we considered several hybrid modelling approaches for forecasting energy spot prices in EPEC market. Hybridization is performed through combining a Naive model, Fourier analysis, ARMA and GARCH models, a mean-reversion and jump-diffusion model, and Recurrent Neural Networks (RNN). Training data was given in terms of electricity prices for 2013-2014 years, and test data as a year of 2015.