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, 2020-10-27 - 2020-10-29, 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:

Authors	Institution or Email of Authors	Author's ORCID iD	ORCID Put Code
Nitsch, Felix	felix.nitsch (at) dlr.de	https://orcid.org/0000-0002-9824-3371	UNSPECIFIED
Schimeczek, Christoph	christoph.schimeczek (at) dlr.de	https://orcid.org/0000-0002-0791-9365	UNSPECIFIED

Date:

29 October 2020

Refereed publication:

Open Access:

Yes

Gold Open Access:

In SCOPUS:

In ISI Web of Science:

Status:

Published

Keywords:

machine learning electricity market modelling

Event Title:

DLR-WAW Maschinelles Lernen 6

Event Location:

Online

Event Type:

Workshop

Event Start Date:

27 October 2020

Event End Date:

29 October 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:

24 Apr 2024 20:39

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