Reference:
J. Lago,
F. De Ridder, and
B. De Schutter,
"Forecasting spot electricity prices: Deep learning approaches and
empirical comparison of traditional algorithms," Applied
Energy, vol. 221, pp. 386-405, July 2018.
Abstract:
In this paper, a novel modeling framework for forecasting electricity
prices is proposed. While many predictive models have been already
proposed to perform this task, the area of deep learning algorithms
remains yet unexplored. To fill this scientific gap, we propose four
different deep learning models for predicting electricity prices and
we show how they lead to improvements in predictive accuracy. In
addition, we also consider that, despite the large number of proposed
methods for predicting electricity prices, an extensive benchmark is
still missing. To tackle that, we compare and analyze the accuracy of
27 common approaches for electricity price forecasting. Based on the
benchmark results, we show how the proposed deep learning models
outperform the state-of-the-art methods and obtain results that are
statistically significant. Finally, using the same results, we also
show that: (i) machine learning methods yield, in general, a better
accuracy than statistical models; (ii) moving average terms do not
improve the predictive accuracy; (iii) hybrid models do not outperform
their simpler counterparts.