A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression

Kathrin Kefer; Roland Hanghofer; Patrick Kefer; Markus Stöger; Michael Affenzeller; Stephan Winkler; Stefan Wagner; Bernd Hofer

doi:10.1007/978-3-030-45093-9_49

A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression

Kathrin Kefer, Roland Hanghofer, Patrick Kefer, Markus Stöger, Michael Affenzeller, Stephan Winkler, Stefan Wagner, Bernd Hofer

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

3 Citations (Scopus)

Abstract

In recent years, renewable energy resources have become increasingly important. Due to the fluctuating and changing environment, these energy sources are not permanently available. At certain times, e.g. a photovoltaic (PV) power plant can only generate little or no electricity at all. This is why energy management systems (EMS), which store, use and distribute the available energy as optimally as possible, have been strongly promoted and further developed recently.

Original language	English
Title of host publication	Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers
Editors	Roberto Moreno-Díaz, Alexis Quesada-Arencibia, Franz Pichler
Publisher	Springer
Pages	405-412
Number of pages	8
ISBN (Print)	9783030450922
DOIs	https://doi.org/10.1007/978-3-030-45093-9_49
Publication status	Published - 2020
Event	17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 - Las Palmas de Gran Canaria, Spain Duration: 17 Feb 2019 → 22 Feb 2019

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	12013 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	17th International Conference on Computer Aided Systems Theory, EUROCAST 2019
Country/Territory	Spain
City	Las Palmas de Gran Canaria
Period	17.02.2019 → 22.02.2019

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

Access to Document

10.1007/978-3-030-45093-9_49

Cite this

Kefer, K., Hanghofer, R., Kefer, P., Stöger, M., Affenzeller, M., Winkler, S., Wagner, S., & Hofer, B. (2020). A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression. In R. Moreno-Díaz, A. Quesada-Arencibia, & F. Pichler (Eds.), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers (pp. 405-412). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12013 LNCS). Springer. https://doi.org/10.1007/978-3-030-45093-9_49

Kefer, Kathrin ; Hanghofer, Roland ; Kefer, Patrick et al. / A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression. Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. editor / Roberto Moreno-Díaz ; Alexis Quesada-Arencibia ; Franz Pichler. Springer, 2020. pp. 405-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

@inproceedings{84ff539184f34bcba163c9addd551ac3,

title = "A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression",

abstract = "In recent years, renewable energy resources have become increasingly important. Due to the fluctuating and changing environment, these energy sources are not permanently available. At certain times, e.g. a photovoltaic (PV) power plant can only generate little or no electricity at all. This is why energy management systems (EMS), which store, use and distribute the available energy as optimally as possible, have been strongly promoted and further developed recently.",

author = "Kathrin Kefer and Roland Hanghofer and Patrick Kefer and Markus St{\"o}ger and Michael Affenzeller and Stephan Winkler and Stefan Wagner and Bernd Hofer",

note = "Funding Information: This project is co-financed by the European Regional Development Fund and the Province of Upper Austria. It was carried out by Fronius International GmbH together with partner researches from the University of Applied Sciences Upper Austria. Publisher Copyright: {\textcopyright} 2020, Springer Nature Switzerland AG.; 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019 ; Conference date: 17-02-2019 Through 22-02-2019",

year = "2020",

doi = "10.1007/978-3-030-45093-9_49",

language = "English",

isbn = "9783030450922",

series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

publisher = "Springer",

pages = "405--412",

editor = "Roberto Moreno-D{\'i}az and Alexis Quesada-Arencibia and Franz Pichler",

booktitle = "Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers",

address = "Germany",

}

Kefer, K, Hanghofer, R, Kefer, P, Stöger, M, Affenzeller, M , Winkler, S , Wagner, S & Hofer, B 2020, A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression. in R Moreno-Díaz, A Quesada-Arencibia & F Pichler (eds), Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 12013 LNCS, Springer, pp. 405-412, 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019, Las Palmas de Gran Canaria, Spain, 17.02.2019. https://doi.org/10.1007/978-3-030-45093-9_49

A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression. / Kefer, Kathrin; Hanghofer, Roland; Kefer, Patrick et al.
Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. ed. / Roberto Moreno-Díaz; Alexis Quesada-Arencibia; Franz Pichler. Springer, 2020. p. 405-412 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 12013 LNCS).

Research output: Chapter in Book/Report/Conference proceedings › Conference contribution › peer-review

TY - GEN

T1 - A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression

AU - Kefer, Kathrin

AU - Hanghofer, Roland

AU - Kefer, Patrick

AU - Stöger, Markus

AU - Affenzeller, Michael

AU - Winkler, Stephan

AU - Wagner, Stefan

AU - Hofer, Bernd

N1 - Funding Information: This project is co-financed by the European Regional Development Fund and the Province of Upper Austria. It was carried out by Fronius International GmbH together with partner researches from the University of Applied Sciences Upper Austria. Publisher Copyright: © 2020, Springer Nature Switzerland AG.

PY - 2020

Y1 - 2020

N2 - In recent years, renewable energy resources have become increasingly important. Due to the fluctuating and changing environment, these energy sources are not permanently available. At certain times, e.g. a photovoltaic (PV) power plant can only generate little or no electricity at all. This is why energy management systems (EMS), which store, use and distribute the available energy as optimally as possible, have been strongly promoted and further developed recently.

AB - In recent years, renewable energy resources have become increasingly important. Due to the fluctuating and changing environment, these energy sources are not permanently available. At certain times, e.g. a photovoltaic (PV) power plant can only generate little or no electricity at all. This is why energy management systems (EMS), which store, use and distribute the available energy as optimally as possible, have been strongly promoted and further developed recently.

UR - http://www.scopus.com/inward/record.url?scp=85083966654&partnerID=8YFLogxK

U2 - 10.1007/978-3-030-45093-9_49

DO - 10.1007/978-3-030-45093-9_49

M3 - Conference contribution

AN - SCOPUS:85083966654

SN - 9783030450922

T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)

SP - 405

EP - 412

BT - Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers

A2 - Moreno-Díaz, Roberto

A2 - Quesada-Arencibia, Alexis

A2 - Pichler, Franz

PB - Springer

T2 - 17th International Conference on Computer Aided Systems Theory, EUROCAST 2019

Y2 - 17 February 2019 through 22 February 2019

ER -

Kefer K, Hanghofer R, Kefer P, Stöger M, Affenzeller M , Winkler S et al. A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression. In Moreno-Díaz R, Quesada-Arencibia A, Pichler F, editors, Computer Aided Systems Theory – EUROCAST 2019 - 17th International Conference, Revised Selected Papers. Springer. 2020. p. 405-412. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-030-45093-9_49

A Model-Based Learning Approach for Controlling the Energy Flows of a Residential Household Using Genetic Programming to Perform Symbolic Regression

Abstract

Publication series

Conference

UN SDGs

Access to Document

Other files and links

Fingerprint

Cite this