Smart-home electrical energy scheduling system using multi-objective antlion optimizer and evidential reasoning

Document Type : Article

Authors

1 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of ‎Science and Technology, Narmak, Tehran, P.O. Box 16846-13114, Iran‎

2 Centre of Excellence for Fundamental Studies in Structural Engineering, Iran University of Science and Technology, Narmak, Tehran, Postal Code 16846-13114, Iran.

Abstract

Smart-home energy-management-systems (SHEMSs) are widely used for energy management in smart buildings. Energy management in smart homes is an arduous task and necessitates efficient scheduling of appliances in buildings. Scheduling of smart appliances is usually enmeshed by various and sometimes contradictory criteria which should be considered concurrently in the scheduling process. Multi-criteria decision-making (MCDM) techniques are able to select the most suitable alternative among copious ones. This paper tailors a comprehensive framework which merges MCDM techniques with evolutionary multi-objective optimization (EMOO) techniques for selecting the most proper schedule for appliances by creating a trade-off between optimization criteria. A Multi-Objective Ant Lion Optimizer (MOALO) is tailored and tested on a smart home case study to detect all the Pareto solutions. A benchmark instance of the appliance scheduling is solved employing the proposed methodology, Shannon’s entropy technique is employed to find the objectives’ corresponding weights, and afterward, the acquired Pareto optimal solutions are ranked utilizing the Evidential Reasoning (ER) method. By inspecting the efficiency of every solution considering multiple criteria such as unsafety, electricity cost, delay, Peak Average Ratio, and CO2 emission, the proposed approach confirms its effectiveness in enhancing the method for smart appliance scheduling.

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Main Subjects

References

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Scientia Iranica
Volume 27, Issue 1
Transactions on Civil Engineering (A)
January and February 2020
Pages 177-201

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History

  • Receive Date: 14 June 2019
  • Revise Date: 23 July 2019
  • Accept Date: 10 August 2019

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