Electrical load management in smart homes using evolutionary algorithms

Florian Allerding; Marc Premm; Pradyumn Kumar Shukla; Hartmut Schmeck

doi:10.1007/978-3-642-29124-1_9

Electrical load management in smart homes using evolutionary algorithms

Florian Allerding^*, Marc Premm, Pradyumn Kumar Shukla, Hartmut Schmeck

^*Corresponding author for this work

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

Abstract

In this paper, we focus on a real world scenario of energy management of a smart home. External variable signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and supply. The problem is formulated as a nonlinear integer programming problem and a load management system, based on a customized evolutionary algorithm with local search, is proposed to control intelligent appliances, decentralized power plants and electrical storages in an optimized way with respect to the given external signals. The nonlinearities present in the integer programming problem makes it difficult for exact solvers. The results of this paper show the efficacy of evolutionary algorithms for solving such combinatorial problems.

Original language	English
Title of host publication	Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings
Pages	99-110
Number of pages	12
DOIs	https://doi.org/10.1007/978-3-642-29124-1_9
Publication status	Published - 2012
Event	12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012 - Malaga, Spain Duration: 11 Apr 2012 → 13 Apr 2012

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume	7245 LNCS
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Conference

Conference	12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012
Country/Territory	Spain
City	Malaga
Period	11/04/12 → 13/04/12

Keywords

Energy Management
Evolutionary Algorithms
Nonlinear Integer Program

Access to Document

10.1007/978-3-642-29124-1_9

Cite this

Allerding, F., Premm, M., Shukla, P. K., & Schmeck, H. (2012). Electrical load management in smart homes using evolutionary algorithms. In Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings (pp. 99-110). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7245 LNCS). https://doi.org/10.1007/978-3-642-29124-1_9

Allerding, Florian ; Premm, Marc ; Shukla, Pradyumn Kumar et al. / Electrical load management in smart homes using evolutionary algorithms. Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings. 2012. pp. 99-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)).

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abstract = "In this paper, we focus on a real world scenario of energy management of a smart home. External variable signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and supply. The problem is formulated as a nonlinear integer programming problem and a load management system, based on a customized evolutionary algorithm with local search, is proposed to control intelligent appliances, decentralized power plants and electrical storages in an optimized way with respect to the given external signals. The nonlinearities present in the integer programming problem makes it difficult for exact solvers. The results of this paper show the efficacy of evolutionary algorithms for solving such combinatorial problems.",

keywords = "Energy Management, Evolutionary Algorithms, Nonlinear Integer Program",

author = "Florian Allerding and Marc Premm and Shukla, {Pradyumn Kumar} and Hartmut Schmeck",

note = "Copyright: Copyright 2012 Elsevier B.V., All rights reserved.; 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012 ; Conference date: 11-04-2012 Through 13-04-2012",

year = "2012",

doi = "10.1007/978-3-642-29124-1_9",

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

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Allerding, F, Premm, M, Shukla, PK & Schmeck, H 2012, Electrical load management in smart homes using evolutionary algorithms. in Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), vol. 7245 LNCS, pp. 99-110, 12th European Conference on Evolutionary Computation in Combinatorial Optimization, EvoCOP 2012, Malaga, Spain, 11/04/12. https://doi.org/10.1007/978-3-642-29124-1_9

Electrical load management in smart homes using evolutionary algorithms. / Allerding, Florian; Premm, Marc; Shukla, Pradyumn Kumar et al.
Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings. 2012. p. 99-110 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 7245 LNCS).

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

TY - GEN

T1 - Electrical load management in smart homes using evolutionary algorithms

AU - Allerding, Florian

AU - Premm, Marc

AU - Shukla, Pradyumn Kumar

AU - Schmeck, Hartmut

PY - 2012

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N2 - In this paper, we focus on a real world scenario of energy management of a smart home. External variable signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and supply. The problem is formulated as a nonlinear integer programming problem and a load management system, based on a customized evolutionary algorithm with local search, is proposed to control intelligent appliances, decentralized power plants and electrical storages in an optimized way with respect to the given external signals. The nonlinearities present in the integer programming problem makes it difficult for exact solvers. The results of this paper show the efficacy of evolutionary algorithms for solving such combinatorial problems.

AB - In this paper, we focus on a real world scenario of energy management of a smart home. External variable signals, reflecting the low voltage grid's state, are used to address the challenge of balancing energy demand and supply. The problem is formulated as a nonlinear integer programming problem and a load management system, based on a customized evolutionary algorithm with local search, is proposed to control intelligent appliances, decentralized power plants and electrical storages in an optimized way with respect to the given external signals. The nonlinearities present in the integer programming problem makes it difficult for exact solvers. The results of this paper show the efficacy of evolutionary algorithms for solving such combinatorial problems.

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KW - Evolutionary Algorithms

KW - Nonlinear Integer Program

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DO - 10.1007/978-3-642-29124-1_9

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

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Allerding F, Premm M, Shukla PK, Schmeck H. Electrical load management in smart homes using evolutionary algorithms. In Evolutionary Computation in Combinatorial Optimization - 12th European Conference, EvoCOP 2012, Proceedings. 2012. p. 99-110. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)). doi: 10.1007/978-3-642-29124-1_9

Electrical load management in smart homes using evolutionary algorithms

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