CoChat
Sign Up
Cited by 645Energy Load and Power ForecastingStock Market Forecasting MethodsForecasting Techniques and Applications

The DOI matches its Crossref record; 95 of the 95 references it cites were found in a registry or an index.

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

Open access
Lago, Jesus, De Ridder, Fjo, De Schutter, Bart·2018·Applied Energy
DOI 10.1016/j.apenergy.2018.02.069
Open this paper in CoChat — free accountOpen PDF

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.

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/j.apenergy.2018.02.069

Cites

95 of 95 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 93 references

Research works printed but not linked: 2

  1. Theano Development Team. Theano: a Python framework for fast computation of mathematical expressions; 2016. Available from: arXiv eprint arXiv:1605.02688. · arXiv
  2. Andersson J. A general-purpose software framework for dynamic optimization [PhD thesis]. Kasteelpark Arenberg 10, 3001-Heverlee (Belgium): Arenberg Doctoral School, KU Leuven, Department of Electrical Engineering (ESAT/SCD) and Optimization in Engineering Center; October 2013.

Cited by

Sorted by citations

Items 601–645 of 645

Related

See all

Metadata

Authors
Lago, J., De Ridder, F., & De Schutter, B.
Affiliations
Flemish Institute for Technological Research (BE)
Corresponding author
Lago, Jesus
ORCID
Lago, Jesus
Venue
Applied Energy
Publisher
Elsevier BV
Published
2018-07
Volume
221
Pages
386-405
ISSN
0306-2619 (ISSN-L)
DOI
10.1016/j.apenergy.2018.02.069
OpenAlex
W2799827709
Cited by
642
References
93
Citations by year
2026: 64 · 2025: 80 · 2024: 74 · 2023: 75 · 2022: 83 · 2021: 84 · 2020: 100 · 2019: 65 · 2018: 16
FWCI
25.45
Type
journal article
Language
en
Update notices
erratum (2018-11-01)
Open access
Licence
Locations
  • Applied Energy (publishedVersion, cc-by-nc-nd)
  • RePEc: Research Papers in Economics (submittedVersion)
  • Research Repository (Delft University of Technology) (submittedVersion)
  • Data Archiving and Networked Services (DANS) (submittedVersion, cc-by-nc-nd)
  • Greater South Information System
Funders
European Union’s Horizon 2020 (675318)
Topics
  • Physical Sciences › Engineering › Electrical and Electronic Engineering › Energy Load and Power Forecasting
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Stock Market Forecasting Methods
  • Social Sciences › Decision Sciences › Management Science and Operations Research › Forecasting Techniques and Applications
Keywords
deep learning, hybrid forecasting model, prediction accuracy

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review