Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms


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.


Downloads:
 * Online version of the paper

Bibtex entry:

@article{LagDeR:18-001,
        author={J. Lago and F. {D}e Ridder and B. {D}e Schutter},
        title={Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms},
        journal={Applied Energy},
        volume={221},
        pages={386--405},
        month=jul,
        year={2018},
        doi={j.apenergy.2018.02.069}
        }



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