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 can select the most suitable alternative among copious ones. This chapter tailors a comprehensive framework that 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 applied 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 utilized to find the objectives’ corresponding weights, and afterward, the acquired Pareto optimal solutions are ranked based on 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 CO emission, the proposed approach confirms its effectiveness in enhancing the method for smart appliance scheduling.
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Kaveh, A., Dadras Eslamlou, A. (2020). Multi-objective Electrical Energy Scheduling in Smart Homes Using Ant Lion Optimizer and Evidential Reasoning. In: Metaheuristic Optimization Algorithms in Civil Engineering: New Applications. Studies in Computational Intelligence, vol 900. Springer, Cham. https://doi.org/10.1007/978-3-030-45473-9_15
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DOI: https://doi.org/10.1007/978-3-030-45473-9_15
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