Simulation-Based Optimization of Residential Energy Flows Using White Box Modeling by Genetic Programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{KEFER:2022:EB,
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author = "Kathrin Kefer and Roland Hanghofer and
Patrick Kefer and Markus Stoeger and Bernd Hofer and
Michael Affenzeller and Stephan Winkler",
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title = "Simulation-Based Optimization of Residential Energy
Flows Using White Box Modeling by Genetic Programming",
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journal = "Energy and Buildings",
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year = "2022",
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volume = "258",
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pages = "111829",
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keywords = "genetic algorithms, genetic programming, Energy
management system, Symbolic regression",
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ISSN = "0378-7788",
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URL = "https://www.sciencedirect.com/science/article/pii/S0378778821011130",
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DOI = "doi:10.1016/j.enbuild.2021.111829",
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abstract = "The development of energy management systems that
optimize the electrical energy flows of residential
buildings has become important nowadays. The
optimization is formulated as a symbolic regression
problem that is solved by genetic programming, which
provides near optimal results while being highly
performant during application. Additionally, the
so-trained energy flow controllers are explainable and
therefore address three of the current major
disadvantages of most existing solutions. 260
controllers are trained to calculate the optimal
gridfeed-in value for an inverter and are evaluated for
their ability to minimize the energy costs and to
support grid stability and battery lifetime.
Additionally, they are compared to two existing energy
management systems, a rule-based self consumption
optimization and a linear model predictive controller.
It is shown that this energy management system can
significantly minimize energy costs compared to both
reference systems by up to 58.25percent, support grid
stability and prolong battery lifetime by up to
76.48percent",
- }
Genetic Programming entries for
Kathrin Kefer
Roland Hanghofer
Patrick Kefer
Markus Stoeger
Bernd Hofer
Michael Affenzeller
Stephan M Winkler
Citations