ANN-Based Electricity Price Forecasting Under Special Consideration of Time Series Properties

Meier, Jan-Hendrik; Schneider, Stephan; Schmidt, Iwana; Schüller, Philip; Schönfeldt, Thies; Wanke, Bastian

doi:10.1007/978-3-030-13929-2_13

Cited by 3 publications

(1 citation statement)

References 38 publications

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“…In 2019, the main focus of the papers was the same as in 2018: 1) evaluating the performance of different deep recurrent networks (mostly LSTMs) [16,30,45,47,[82][83][84], 2) proposing new hybrid deep learning methods usually based on LSTMs and CNNs [17,28,36,82,[85][86][87], or 3) employing regular DNN models [15,46,88]. Similarly, as with most studies in 2018, the new studies were more limited than [12,59] as no comparisons with state-of-the-art statistical methods were made and long test datasets were seldom used.…”

Section: Deep Learningmentioning

confidence: 99%

Forecasting day-ahead electricity prices: A review of state-of-the-art algorithms, best practices and an open-access benchmark

Lago,

Marcjasz,

De Schutter

et al. 2020

Preprint

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While the field of electricity price forecasting has benefited from plenty of contributions in the last two decades, it arguably lacks a rigorous approach to evaluating new predictive algorithms. The latter are often compared using unique, not publicly available datasets and across too short and limited to one market test samples. The proposed new methods are rarely benchmarked against well established and well performing simpler models, the accuracy metrics are sometimes inadequate and testing the significance of differences in predictive performance is seldom conducted. Consequently, it is not clear which methods perform well nor what are the best practices when forecasting electricity prices. In this paper, we tackle these issues by performing a literature survey of state-of-the-art models, comparing state-of-the-art statistical and deep learning methods across multiple years and markets, and by putting forward a set of best practices. In addition, we make available the considered datasets, forecasts of the state-of-the-art models, and a specifically designed python toolbox, so that new algorithms can be rigorously evaluated in future studies.

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