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.
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Allerding, F., Premm, M., Shukla, P.K., Schmeck, H. (2012). Electrical Load Management in Smart Homes Using Evolutionary Algorithms. In: Hao, JK., Middendorf, M. (eds) Evolutionary Computation in Combinatorial Optimization. EvoCOP 2012. Lecture Notes in Computer Science, vol 7245. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-29124-1_9
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DOI: https://doi.org/10.1007/978-3-642-29124-1_9
Publisher Name: Springer, Berlin, Heidelberg
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