Electricity price forecasting on the day-ahead market using machine learning

The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the p...

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Vydané v:	Applied energy Ročník 313; s. 118752
Hlavní autori:	Tschora, Léonard, Pierre, Erwan, Plantevit, Marc, Robardet, Céline
Médium:	Journal Article
Jazyk:	English
Vydavateľské údaje:	Elsevier Ltd 01.05.2022 Elsevier
Predmet:	Artificial Intelligence Computational Engineering, Finance, and Science Computer Science Data Structures and Algorithms Economics and Finance electricity electricity costs Electricity price forecasting energy Explainable AI (XAI) Forecast evaluation Humanities and Social Sciences Machine Learning markets Open-access benchmark prediction prices weather Electricity price forecasting Forecast evaluation Open-access benchmark Explainable AI (XAI) Machine learning
ISSN:	0306-2619, 1872-9118
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Abstract	The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the prices of the next day is an important issue, to allow the development of intelligent uses of electricity. In this article, we investigate the capabilities of different machine learning techniques to accurately predict electricity prices. Specifically, we extend current state-of-the-art approaches by considering previously unused predictive features such as price histories of neighboring countries. We show that these features significantly improve the quality of forecasts, even in the current period when sudden changes are occurring. We also develop an analysis of the contribution of the different features in model prediction using Shap values, in order to shed light on how models make their prediction and to build user confidence in models. •Evaluation of several machine learning models for electricity price forecasting.•Methodology is rigorously detailed, the source and the data are made available.•New Machine Learning models are provided and achieved cutting edge results.•Performances of the models are increased by adding new features to the datasets.•XAI techniques are used to explain which features impact most the predictions.•Modeling multiple countries at once improves results.
AbstractList	The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the prices of the next day is an important issue, to allow the development of intelligent uses of electricity. In this article, we investigate the capabilities of different machine learning techniques to accurately predict electricity prices. Specifically, we extend current state-of-the-art approaches by considering previously unused predictive features such as price histories of neighboring countries. We show that these features significantly improve the quality of forecasts, even in the current period when sudden changes are occurring. We also develop an analysis of the contribution of the different features in model prediction using Shap values, in order to shed light on how models make their prediction and to build user confidence in models. •Evaluation of several machine learning models for electricity price forecasting.•Methodology is rigorously detailed, the source and the data are made available.•New Machine Learning models are provided and achieved cutting edge results.•Performances of the models are increased by adding new features to the datasets.•XAI techniques are used to explain which features impact most the predictions.•Modeling multiple countries at once improves results. The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own constraints (volume of production, dependence on the weather, or production inertia), and by the difficulty of its storage. Being able to predict the prices of the next day is an important issue, to allow the development of intelligent uses of electricity. In this article, we investigate the capabilities of different machine learning techniques to accurately predict electricity prices. Specifically, we extend current state-of-the-art approaches by considering previously unused predictive features such as price histories of neighboring countries. We show that these features significantly improve the quality of forecasts, even in the current period when sudden changes are occurring. We also develop an analysis of the contribution of the different features in model prediction using Shap values, in order to shed light on how models make their prediction and to build user confidence in models.
ArticleNumber	118752
Author	Tschora, Léonard Plantevit, Marc Pierre, Erwan Robardet, Céline
Author_xml	– sequence: 1 givenname: Léonard surname: Tschora fullname: Tschora, Léonard email: leonard.tschora@insa-lyon.fr organization: BCM Energy, FR-69006, Lyon, France – sequence: 2 givenname: Erwan surname: Pierre fullname: Pierre, Erwan email: erwan.pierre@bcmenergy.fr organization: BCM Energy, FR-69006, Lyon, France – sequence: 3 givenname: Marc surname: Plantevit fullname: Plantevit, Marc email: marc.plantevit@epita.fr organization: EPITA Research and Development Laboratory (LRDE), FR-94276 Le Kremlin-Bicêtre, France – sequence: 4 givenname: Céline orcidid: 0000-0002-8583-9408 surname: Robardet fullname: Robardet, Céline email: celine.robardet@insa-lyon.fr organization: Univ. Lyon, INSA Lyon, CNRS, LIRIS UMR5205, FR-69621, France
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Keywords	Electricity price forecasting Forecast evaluation Open-access benchmark Explainable AI (XAI) Machine learning
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Snippet	The price of electricity on the European market is very volatile. This is due both to its mode of production by different sources, each with its own...
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SubjectTerms	Artificial Intelligence Computational Engineering, Finance, and Science Computer Science Data Structures and Algorithms Economics and Finance electricity electricity costs Electricity price forecasting energy Explainable AI (XAI) Forecast evaluation Humanities and Social Sciences Machine Learning markets Open-access benchmark prediction prices weather
Title	Electricity price forecasting on the day-ahead market using machine learning
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