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Electricity price forecasts in agent-based energy system simulations

Nitsch, Felix and Schimeczek, Christoph (2020) Electricity price forecasts in agent-based energy system simulations. DLR-WAW Maschinelles Lernen 6, 27.-29.10.2020, Online.

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Abstract

We use a feed-forward and a long short-term model (LSTM) to predict electricity prices in an agent-based electricity market model. This model setup allows to consider uncertainties due to forecasting errors and competetion on energy markets in our modelling.

Item URL in elib:https://elib.dlr.de/137426/
Document Type:Conference or Workshop Item (Speech)
Title:Electricity price forecasts in agent-based energy system simulations
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iD
Nitsch, Felixfelix.nitsch (at) dlr.dehttps://orcid.org/0000-0002-9824-3371
Schimeczek, Christophchristoph.schimeczek (at) dlr.dehttps://orcid.org/0000-0002-0791-9365
Date:29 October 2020
Refereed publication:No
Open Access:Yes
Gold Open Access:No
In SCOPUS:No
In ISI Web of Science:No
Status:Published
Keywords:machine learning electricity market modelling
Event Title:DLR-WAW Maschinelles Lernen 6
Event Location:Online
Event Type:Workshop
Event Dates:27.-29.10.2020
HGF - Research field:Energy
HGF - Program:Technology, Innovation and Society
HGF - Program Themes:Renewable Energy and Material Resources for Sustainable Futures - Integrating at Different Scales
DLR - Research area:Energy
DLR - Program:E SY - Energy Systems Analysis
DLR - Research theme (Project):E - Systems Analysis and Technology Assessment (old)
Location: Stuttgart
Institutes and Institutions:Institute of Engineering Thermodynamics > Energy Systems Analysis
Deposited By: Nitsch, Felix
Deposited On:04 Dec 2020 13:21
Last Modified:04 Dec 2020 13:21

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