Electrical load management in smart homes using evolutionary algorithms

Authors:
Florian Allerding;Marc Premm;Pradyumn Kumar Shukla;Hartmut Schmeck
Affiliations:
Karlsruhe Institute of Technology --- Institute AIFB, Karlsruhe, Germany;Universität Hohenheim, Stuttgart-Hohenheim, Germany;Karlsruhe Institute of Technology --- Institute AIFB, Karlsruhe, Germany;Karlsruhe Institute of Technology --- Institute AIFB, Karlsruhe, Germany
Venue:
EvoCOP'12 Proceedings of the 12th European conference on Evolutionary Computation in Combinatorial Optimization
Year:
2012

Citing 5
Cited 1

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Nonlinear Integer Programming (International Series in Operations Research & Management Science)
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Domestic load scheduling using genetic algorithms

EvoApplications'13 Proceedings of the 16th European conference on Applications of Evolutionary Computation

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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.